The `/api/system-features` is required for the web app initialization.
Authentication would create circular dependency (can't authenticate without web app loading).
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
The `/console/api/system-features` is required for the dashboard initialization. Authentication would create circular dependency (can't login without dashboard loading).
ref: CVE-2025-63387
Related: #31368
description: Review backend code for quality, security, maintainability, and best practices based on established checklist rules. Use when the user requests a review, analysis, or improvement of backend files (e.g., `.py`) under the `api/` directory. Do NOT use for frontend files (e.g., `.tsx`, `.ts`, `.js`). Supports pending-change review, code snippets review, and file-focused review.
---
# Backend Code Review
## When to use this skill
Use this skill whenever the user asks to **review, analyze, or improve** backend code (e.g., `.py`) under the `api/` directory. Supports the following review modes:
- **Pending-change review**: when the user asks to review current changes (inspect staged/working-tree files slated for commit to get the changes).
- **Code snippets review**: when the user pastes code snippets (e.g., a function/class/module excerpt) into the chat and asks for a review.
- **File-focused review**: when the user points to specific files and asks for a review of those files (one file or a small, explicit set of files, e.g., `api/...`, `api/app.py`).
Do NOT use this skill when:
- The request is about frontend code or UI (e.g., `.tsx`, `.ts`, `.js`, `web/`).
- The user is not asking for a review/analysis/improvement of backend code.
- The scope is not under `api/` (unless the user explicitly asks to review backend-related changes outside `api/`).
## How to use this skill
Follow these steps when using this skill:
1.**Identify the review mode** (pending-change vs snippet vs file-focused) based on the user’s input. Keep the scope tight: review only what the user provided or explicitly referenced.
2. Follow the rules defined in **Checklist** to perform the review. If no Checklist rule matches, apply **General Review Rules** as a fallback to perform the best-effort review.
3. Compose the final output strictly follow the **Required Output Format**.
Notes when using this skill:
- Always include actionable fixes or suggestions (including possible code snippets).
- Use best-effort `File:Line` references when a file path and line numbers are available; otherwise, use the most specific identifier you can.
## Checklist
- db schema design: if the review scope includes code/files under `api/models/` or `api/migrations/`, follow [references/db-schema-rule.md](references/db-schema-rule.md) to perform the review
- architecture: if the review scope involves controller/service/core-domain/libs/model layering, dependency direction, or moving responsibilities across modules, follow [references/architecture-rule.md](references/architecture-rule.md) to perform the review
- repositories abstraction: if the review scope contains table/model operations (e.g., `select(...)`, `session.execute(...)`, joins, CRUD) and is not under `api/repositories`, `api/core/repositories`, or `api/extensions/*/repositories/`, follow [references/repositories-rule.md](references/repositories-rule.md) to perform the review
- sqlalchemy patterns: if the review scope involves SQLAlchemy session/query usage, db transaction/crud usage, or raw SQL usage, follow [references/sqlalchemy-rule.md](references/sqlalchemy-rule.md) to perform the review
## General Review Rules
### 1. Security Review
Check for:
- SQL injection vulnerabilities
- Server-Side Request Forgery (SSRF)
- Command injection
- Insecure deserialization
- Hardcoded secrets/credentials
- Improper authentication/authorization
- Insecure direct object references
### 2. Performance Review
Check for:
- N+1 queries
- Missing database indexes
- Memory leaks
- Blocking operations in async code
- Missing caching opportunities
### 3. Code Quality Review
Check for:
- Code forward compatibility
- Code duplication (DRY violations)
- Functions doing too much (SRP violations)
- Deep nesting / complex conditionals
- Magic numbers/strings
- Poor naming
- Missing error handling
- Incomplete type coverage
### 4. Testing Review
Check for:
- Missing test coverage for new code
- Tests that don't test behavior
- Flaky test patterns
- Missing edge cases
## Required Output Format
When this skill invoked, the response must exactly follow one of the two templates:
### Template A (any findings)
```markdown
# Code Review Summary
Found <X> critical issues need to be fixed:
## 🔴 Critical (Must Fix)
### 1. <brief description of the issue>
FilePath: <path> line <line>
<relevant code snippet or pointer>
#### Explanation
<detailed explanation and references of the issue>
#### Suggested Fix
1. <brief description of suggested fix>
2. <code example> (optional, omit if not applicable)
---
... (repeat for each critical issue) ...
Found <Y> suggestions for improvement:
## 🟡 Suggestions (Should Consider)
### 1. <brief description of the suggestion>
FilePath: <path> line <line>
<relevant code snippet or pointer>
#### Explanation
<detailed explanation and references of the suggestion>
#### Suggested Fix
1. <brief description of suggested fix>
2. <code example> (optional, omit if not applicable)
---
... (repeat for each suggestion) ...
Found <Z> optional nits:
## 🟢 Nits (Optional)
### 1. <brief description of the nit>
FilePath: <path> line <line>
<relevant code snippet or pointer>
#### Explanation
<explanation and references of the optional nit>
#### Suggested Fix
- <minor suggestions>
---
... (repeat for each nits) ...
## ✅ What's Good
- <Positive feedback on good patterns>
```
- If there are no critical issues or suggestions or option nits or good points, just omit that section.
- If the issue number is more than 10, summarize as "Found 10+ critical issues/suggestions/optional nits" and only output the first 10 items.
- Don't compress the blank lines between sections; keep them as-is for readability.
- If there is any issue requires code changes, append a brief follow-up question to ask whether the user wants to apply the fix(es) after the structured output. For example: "Would you like me to use the Suggested fix(es) to address these issues?"
- Description: Controllers should parse input, call services, and return serialized responses. Business decisions inside controllers make behavior hard to reuse and test.
- Suggested fix: Move domain/business logic into the service or core/domain layer. Keep controller handlers thin and orchestration-focused.
- Example:
- Bad:
```python
@bp.post("/apps/<app_id>/publish")
def publish_app(app_id: str):
payload = request.get_json() or {}
if payload.get("force") and current_user.role != "admin":
raise ValueError("only admin can force publish")
app = App.query.get(app_id)
app.status = "published"
db.session.commit()
return {"result": "ok"}
```
- Good:
```python
@bp.post("/apps/<app_id>/publish")
def publish_app(app_id: str):
payload = PublishRequest.model_validate(request.get_json() or {})
- Description: Controllers may depend on services, and services may depend on core/domain abstractions. Reversing this direction (for example, core importing controller/web modules) creates cycles and leaks transport concerns into domain code.
- Suggested fix: Extract shared contracts into core/domain or service-level modules and make upper layers depend on lower, not the reverse.
- Example:
- Bad:
```python
# core/policy/publish_policy.py
from controllers.console.app import request_context
def can_publish() -> bool:
return request_context.current_user.is_admin
```
- Good:
```python
# core/policy/publish_policy.py
def can_publish(role: str) -> bool:
return role == "admin"
# service layer adapts web/user context to domain input
allowed = can_publish(role=current_user.role)
```
### Keep libs business-agnostic
- Category: maintainability
- Severity: critical
- Description: Modules under `api/libs/` should remain reusable, business-agnostic building blocks. They must not encode product/domain-specific rules, workflow orchestration, or business decisions.
- Suggested fix:
- If business logic appears in `api/libs/`, extract it into the appropriate `services/` or `core/` module and keep `libs` focused on generic, cross-cutting helpers.
- Covers: model/base inheritance, schema boundaries in model properties, tenant-aware schema design, index redundancy checks, dialect portability in models, and cross-database compatibility in migrations.
- Does NOT cover: session lifecycle, transaction boundaries, and query execution patterns (handled by `sqlalchemy-rule.md`).
## Rules
### Do not query other tables inside `@property`
- Category: [maintainability, performance]
- Severity: critical
- Description: A model `@property` must not open sessions or query other tables. This hides dependencies across models, tightly couples schema objects to data access, and can cause N+1 query explosions when iterating collections.
- Suggested fix:
- Keep model properties pure and local to already-loaded fields.
- Move cross-table data fetching to service/repository methods.
- For list/batch reads, fetch required related data explicitly (join/preload/bulk query) before rendering derived values.
- Example:
- Bad:
```python
class Conversation(TypeBase):
__tablename__ = "conversations"
@property
def app_name(self) -> str:
with Session(db.engine, expire_on_commit=False) as session:
# Service/repository layer performs explicit batch fetch for related App rows.
```
### Prefer including `tenant_id` in model definitions
- Category: maintainability
- Severity: suggestion
- Description: In multi-tenant domains, include `tenant_id` in schema definitions whenever the entity belongs to tenant-owned data. This improves data isolation safety and keeps future partitioning/sharding strategies practical as data volume grows.
- Suggested fix:
- Add a `tenant_id` column and ensure related unique/index constraints include tenant dimension when applicable.
- Propagate `tenant_id` through service/repository contracts to keep access paths tenant-aware.
- Exception: if a table is explicitly designed as non-tenant-scoped global metadata, document that design decision clearly.
- Description: Review index definitions for leftmost-prefix redundancy. For example, index `(a, b, c)` can safely cover most lookups for `(a, b)`. Keeping both may increase write overhead and can mislead the optimizer into suboptimal execution plans.
- Suggested fix:
- Before adding an index, compare against existing composite indexes by leftmost-prefix rules.
- Drop or avoid creating redundant prefixes unless there is a proven query-pattern need.
- Apply the same review standard in both model `__table_args__` and migration index DDL.
### Avoid PostgreSQL-only dialect usage in models; wrap in `models.types`
- Category: maintainability
- Severity: critical
- Description: Model/schema definitions should avoid PostgreSQL-only constructs directly in business models. When database-specific behavior is required, encapsulate it in `api/models/types.py` using both PostgreSQL and MySQL dialect implementations, then consume that abstraction from model code.
- Suggested fix:
- Do not directly place dialect-only types/operators in model columns when a portable wrapper can be used.
- Add or extend wrappers in `models.types` (for example, `AdjustedJSON`, `LongText`, `BinaryData`) to normalize behavior across PostgreSQL and MySQL.
### Guard migration incompatibilities with dialect checks and shared types
- Category: maintainability
- Severity: critical
- Description: Migration scripts under `api/migrations/versions/` must account for PostgreSQL/MySQL incompatibilities explicitly. For dialect-sensitive DDL or defaults, branch on the active dialect (for example, `conn.dialect.name == "postgresql"`), and prefer reusable compatibility abstractions from `models.types` where applicable.
- Suggested fix:
- In migration upgrades/downgrades, bind connection and branch by dialect for incompatible SQL fragments.
- Reuse `models.types` wrappers in column definitions when that keeps behavior aligned with runtime models.
- Avoid one-dialect-only migration logic unless there is a documented, deliberate compatibility exception.
- Example:
- Bad:
```python
with op.batch_alter_table("dataset_keyword_tables") as batch_op:
- Covers: when to reuse existing repository abstractions, when to introduce new repositories, and how to preserve dependency direction between service/core and infrastructure implementations.
- Does NOT cover: SQLAlchemy session lifecycle and query-shape specifics (handled by `sqlalchemy-rule.md`), and table schema/migration design (handled by `db-schema-rule.md`).
## Rules
### Introduce repositories abstraction
- Category: maintainability
- Severity: suggestion
- Description: If a table/model already has a repository abstraction, all reads/writes/queries for that table should use the existing repository. If no repository exists, introduce one only when complexity justifies it, such as large/high-volume tables, repeated complex query logic, or likely storage-strategy variation.
- Suggested fix:
- First check `api/repositories`, `api/core/repositories`, and `api/extensions/*/repositories/` to verify whether the table/model already has a repository abstraction. If it exists, route all operations through it and add missing repository methods instead of bypassing it with ad-hoc SQLAlchemy access.
- If no repository exists, add one only when complexity warrants it (for example, repeated complex queries, large data domains, or multiple storage strategies), while preserving dependency direction (service/core depends on abstraction; infra provides implementation).
- Example:
- Bad:
```python
# Existing repository is ignored and service uses ad-hoc table queries.
- Covers: SQLAlchemy session and transaction lifecycle, query construction, tenant scoping, raw SQL boundaries, and write-path concurrency safeguards.
- Does NOT cover: table/model schema and migration design details (handled by `db-schema-rule.md`).
## Rules
### Use Session context manager with explicit transaction control behavior
- Category: best practices
- Severity: critical
- Description: Session and transaction lifecycle must be explicit and bounded on write paths. Missing commits can silently drop intended updates, while ad-hoc or long-lived transactions increase contention, lock duration, and deadlock risk.
- Suggested fix:
- Use **explicit `session.commit()`** after completing a related write unit.
- Or use **`session.begin()` context manager** for automatic commit/rollback on a scoped block.
- Keep transaction windows short: avoid network I/O, heavy computation, or unrelated work inside the transaction.
- Example:
- Bad:
```python
# Missing commit: write may never be persisted.
with Session(db.engine, expire_on_commit=False) as session:
run = session.get(WorkflowRun, run_id)
run.status = "cancelled"
# Long transaction: external I/O inside a DB transaction.
with Session(db.engine, expire_on_commit=False) as session, session.begin():
run = session.get(WorkflowRun, run_id)
run.status = "cancelled"
call_external_api()
```
- Good:
```python
# Option 1: explicit commit.
with Session(db.engine, expire_on_commit=False) as session:
run = session.get(WorkflowRun, run_id)
run.status = "cancelled"
session.commit()
# Option 2: scoped transaction with automatic commit/rollback.
with Session(db.engine, expire_on_commit=False) as session, session.begin():
run = session.get(WorkflowRun, run_id)
run.status = "cancelled"
# Keep non-DB work outside transaction scope.
call_external_api()
```
### Enforce tenant_id scoping on shared-resource queries
- Category: security
- Severity: critical
- Description: Reads and writes against shared tables must be scoped by `tenant_id` to prevent cross-tenant data leakage or corruption.
- Suggested fix: Add `tenant_id` predicate to all tenant-owned entity queries and propagate tenant context through service/repository interfaces.
### Prefer SQLAlchemy expressions over raw SQL by default
- Category: maintainability
- Severity: suggestion
- Description: Raw SQL should be exceptional. ORM/Core expressions are easier to evolve, safer to compose, and more consistent with the codebase.
- Suggested fix: Rewrite straightforward raw SQL into SQLAlchemy `select/update/delete` expressions; keep raw SQL only when required by clear technical constraints.
- Example:
- Bad:
```python
row = session.execute(
text("SELECT * FROM workflows WHERE id = :id AND tenant_id = :tenant_id"),
{"id": workflow_id, "tenant_id": tenant_id},
).first()
```
- Good:
```python
stmt = select(Workflow).where(
Workflow.id == workflow_id,
Workflow.tenant_id == tenant_id,
)
row = session.execute(stmt).scalar_one_or_none()
```
### Protect write paths with concurrency safeguards
- Category: quality
- Severity: critical
- Description: Multi-writer paths without explicit concurrency control can silently overwrite data. Choose the safeguard based on contention level, lock scope, and throughput cost instead of defaulting to one strategy.
- Suggested fix:
- **Optimistic locking**: Use when contention is usually low and retries are acceptable. Add a version (or updated_at) guard in `WHERE` and treat `rowcount == 0` as a conflict.
- **Redis distributed lock**: Use when the critical section spans multiple steps/processes (or includes non-DB side effects) and you need cross-worker mutual exclusion.
- **SELECT ... FOR UPDATE**: Use when contention is high on the same rows and strict in-transaction serialization is required. Keep transactions short to reduce lock wait/deadlock risk.
- In all cases, scope by `tenant_id` and verify affected row counts for conditional writes.
- Example:
- Bad:
```python
# No tenant scope, no conflict detection, and no lock on a contested write path.
description: Refactor high-complexity React components in Dify frontend. Use when `pnpm analyze-component --json` shows complexity > 50 or lineCount > 300, when the user asks for code splitting, hook extraction, or complexity reduction, or when `pnpm analyze-component` warns to refactor before testing; avoid for simple/well-structured components, third-party wrappers, or when the user explicitly wants testing without refactoring.
---
# Dify Component Refactoring Skill
Refactor high-complexity React components in the Dify frontend codebase with the patterns and workflow below.
> **Complexity Threshold**: Components with complexity > 50 (measured by `pnpm analyze-component`) should be refactored before testing.
## Quick Reference
### Commands (run from `web/`)
Use paths relative to `web/` (e.g., `app/components/...`).
Use `refactor-component` for refactoring prompts and `analyze-component` for testing prompts and metrics.
description: Generate Vitest + React Testing Library tests for Dify frontend components, hooks, and utilities. Triggers on testing, spec files, coverage, Vitest, RTL, unit tests, integration tests, or write/review test requests.
---
# Dify Frontend Testing Skill
This skill enables Claude to generate high-quality, comprehensive frontend tests for the Dify project following established conventions and best practices.
> **⚠️ Authoritative Source**: This skill is derived from `web/docs/test.md`. Use Vitest mock/timer APIs (`vi.*`).
## When to Apply This Skill
Apply this skill when the user:
- Asks to **write tests** for a component, hook, or utility
- Asks to **review existing tests** for completeness
- Mentions **Vitest**, **React Testing Library**, **RTL**, or **spec files**
- Requests **test coverage** improvement
- Uses `pnpm analyze-component` output as context
- Mentions **testing**, **unit tests**, or **integration tests** for frontend code
- Wants to understand **testing patterns** in the Dify codebase
**Do NOT apply** when:
- User is asking about backend/API tests (Python/pytest)
- User is asking about E2E tests (Playwright/Cypress)
- User is only asking conceptual questions without code context
- Modules are not mocked automatically. Global mocks live in `web/vitest.setup.ts` (for example `react-i18next`, `next/image`); mock other modules like `ky` or `mime` locally in test files.
See [Zustand Store Testing](#zustand-store-testing) section for full details.
## Mock Placement
| Location | Purpose |
|----------|---------|
| `web/vitest.setup.ts` | Global mocks shared by all tests (`react-i18next`, `next/image`, `zustand`) |
| `web/__mocks__/zustand.ts` | Zustand mock implementation (auto-resets stores after each test) |
| `web/__mocks__/` | Reusable mock factories shared across multiple test files |
| Test file | Test-specific mocks, inline with `vi.mock()` |
Modules are not mocked automatically. Use `vi.mock` in test files, or add global mocks in `web/vitest.setup.ts`.
**Note**: Zustand is special - it's globally mocked but you should NOT mock store modules manually. See [Zustand Store Testing](#zustand-store-testing).
## Essential Mocks
### 1. i18n (Auto-loaded via Global Mock)
A global mock is defined in `web/vitest.setup.ts` and is auto-loaded by Vitest setup.
The global mock provides:
-`useTranslation` - returns translation keys with namespace prefix
-`Trans` component - renders i18nKey and components
1.**Use real base components** - Import from `@/app/components/base/` directly
1.**Use real project components** - Prefer importing over mocking
1.**Use real Zustand stores** - Set test state via `store.setState()`
1.**Reset mocks in `beforeEach`**, not `afterEach`
1.**Match actual component behavior** in mocks (when mocking is necessary)
1.**Use factory functions** for complex mock data
1.**Import actual types** for type safety
1.**Reset shared mock state** in `beforeEach`
### ❌ DON'T
1.**Don't mock base components** (`Loading`, `Button`, `Tooltip`, etc.)
1.**Don't mock Zustand store modules** - Use real stores with `setState()`
1. Don't mock components you can import directly
1. Don't create overly simplified mocks that miss conditional logic
1. Don't forget to clean up nock after each test
1. Don't use `any` types in mocks without necessity
### Mock Decision Tree
```
Need to use a component in test?
│
├─ Is it from @/app/components/base/*?
│ └─ YES → Import real component, DO NOT mock
│
├─ Is it a project component?
│ └─ YES → Prefer importing real component
│ Only mock if setup is extremely complex
│
├─ Is it an API service (@/service/*)?
│ └─ YES → Mock it
│
├─ Is it a third-party lib with side effects?
│ └─ YES → Mock it (next/navigation, external SDKs)
│
├─ Is it a Zustand store?
│ └─ YES → DO NOT mock the module!
│ Use real store + setState() to set test state
│ (Global mock handles auto-reset)
│
└─ Is it i18n?
└─ YES → Uses shared mock (auto-loaded). Override only for custom translations
```
## Zustand Store Testing
### Global Zustand Mock (Auto-loaded)
Zustand is globally mocked in `web/vitest.setup.ts` following the [official Zustand testing guide](https://zustand.docs.pmnd.rs/guides/testing). The mock in `web/__mocks__/zustand.ts` provides:
- Real store behavior with `getState()`, `setState()`, `subscribe()` methods
- Automatic store reset after each test via `afterEach`
- Proper test isolation between tests
### ✅ Recommended: Use Real Stores (Official Best Practice)
**DO NOT mock store modules manually.** Import and use the real store, then use `setState()` to set test state:
@@ -4,7 +4,7 @@ This guide defines the workflow for generating tests, especially for complex com
## Scope Clarification
This guide addresses **multi-file workflow** (how to process multiple test files). For coverage requirements within a single test file, see `web/testing/testing.md` § Coverage Goals.
This guide addresses **multi-file workflow** (how to process multiple test files). For coverage requirements within a single test file, see `web/docs/test.md` § Coverage Goals.
description: Guide for implementing oRPC contract-first API patterns in Dify frontend. Trigger when creating or updating contracts in web/contract, wiring router composition, integrating TanStack Query with typed contracts, migrating legacy service calls to oRPC, or deciding whether to call queryOptions directly vs extracting a helper or use-* hook in web/service.
---
# oRPC Contract-First Development
## Intent
- Keep contract as single source of truth in `web/contract/*`.
- Default query usage: call-site `useQuery(consoleQuery|marketplaceQuery.xxx.queryOptions(...))` when endpoint behavior maps 1:1 to the contract.
- Keep abstractions minimal and preserve TypeScript inference.
## Minimal Structure
```text
web/contract/
├── base.ts
├── router.ts
├── marketplace.ts
└── console/
├── billing.ts
└── ...other domains
web/service/client.ts
```
## Core Workflow
1. Define contract in `web/contract/console/{domain}.ts` or `web/contract/marketplace.ts`
- Use `base.route({...}).output(type<...>())` as baseline.
- Add `.input(type<...>())` only when request has `params/query/body`.
- For `GET` without input, omit `.input(...)` (do not use `.input(type<unknown>())`).
2. Register contract in `web/contract/router.ts`
- Import directly from domain files and nest by API prefix.
3. Consume from UI call sites via oRPC query utils.
1. Default: call mutation helpers from `consoleQuery` / `marketplaceQuery`, for example `useMutation(consoleQuery.billing.bindPartnerStack.mutationOptions(...))`.
2. If mutation flow is heavily custom, use oRPC clients as `mutationFn` (for example `consoleClient.xxx` / `marketplaceClient.xxx`), instead of generic handwritten non-oRPC mutation logic.
## Key API Guide (`.key` vs `.queryKey` vs `.mutationKey`)
-`.key(...)`:
- Use for partial matching operations (recommended for invalidation/refetch/cancel patterns).
- Use for a specific query's full key (exact query identity / direct cache addressing).
-`.mutationKey(...)`:
- Use for a specific mutation's full key.
- Typical use cases: mutation defaults registration, mutation-status filtering (`useIsMutating`, `queryClient.isMutating`), or explicit devtools grouping.
## Anti-Patterns
- Do not wrap `useQuery` with `options?: Partial<UseQueryOptions>`.
- Do not split local `queryKey/queryFn` when oRPC `queryOptions` already exists and fits the use case.
- Do not create thin `use-*` passthrough hooks for a single endpoint.
- Reason: these patterns can degrade inference (`data` may become `unknown`, especially around `throwOnError`/`select`) and add unnecessary indirection.
## Contract Rules
- **Input structure**: Always use `{ params, query?, body? }` format
- **No-input GET**: Omit `.input(...)`; do not use `.input(type<unknown>())`
- **Path params**: Use `{paramName}` in path, match in `params` object
- **Router nesting**: Group by API prefix (e.g., `/billing/*` -> `billing: {}`)
- **No barrel files**: Import directly from specific files
- **Types**: Import from `@/types/`, use `type<T>()` helper
- **Mutations**: Prefer `mutationOptions`; use explicit `mutationKey` mainly for defaults/filtering/devtools
description: Refactor high-complexity React components in Dify frontend. Use when `pnpm analyze-component --json` shows complexity > 50 or lineCount > 300, when the user asks for code splitting, hook extraction, or complexity reduction, or when `pnpm analyze-component` warns to refactor before testing; avoid for simple/well-structured components, third-party wrappers, or when the user explicitly wants testing without refactoring.
---
# Dify Component Refactoring Skill
Refactor high-complexity React components in the Dify frontend codebase with the patterns and workflow below.
> **Complexity Threshold**: Components with complexity > 50 (measured by `pnpm analyze-component`) should be refactored before testing.
## Quick Reference
### Commands (run from `web/`)
Use paths relative to `web/` (e.g., `app/components/...`).
Use `refactor-component` for refactoring prompts and `analyze-component` for testing prompts and metrics.
description: Generate Vitest + React Testing Library tests for Dify frontend components, hooks, and utilities. Triggers on testing, spec files, coverage, Vitest, RTL, unit tests, integration tests, or write/review test requests.
---
# Dify Frontend Testing Skill
This skill enables Claude to generate high-quality, comprehensive frontend tests for the Dify project following established conventions and best practices.
> **⚠️ Authoritative Source**: This skill is derived from `web/testing/testing.md`. Use Vitest mock/timer APIs (`vi.*`).
## When to Apply This Skill
Apply this skill when the user:
- Asks to **write tests** for a component, hook, or utility
- Asks to **review existing tests** for completeness
- Mentions **Vitest**, **React Testing Library**, **RTL**, or **spec files**
- Requests **test coverage** improvement
- Uses `pnpm analyze-component` output as context
- Mentions **testing**, **unit tests**, or **integration tests** for frontend code
- Wants to understand **testing patterns** in the Dify codebase
**Do NOT apply** when:
- User is asking about backend/API tests (Python/pytest)
- User is asking about E2E tests (Playwright/Cypress)
- User is only asking conceptual questions without code context
- Modules are not mocked automatically. Global mocks live in `web/vitest.setup.ts` (for example `react-i18next`, `next/image`); mock other modules like `ky` or `mime` locally in test files.
description: Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.
---
# Skill Creator
This skill provides guidance for creating effective skills.
## About Skills
Skills are modular, self-contained packages that extend Claude's capabilities by providing
specialized knowledge, workflows, and tools. Think of them as "onboarding guides" for specific
domains or tasks—they transform Claude from a general-purpose agent into a specialized agent
equipped with procedural knowledge that no model can fully possess.
### What Skills Provide
1. Specialized workflows - Multi-step procedures for specific domains
2. Tool integrations - Instructions for working with specific file formats or APIs
3. Domain expertise - Company-specific knowledge, schemas, business logic
4. Bundled resources - Scripts, references, and assets for complex and repetitive tasks
## Core Principles
### Concise is Key
The context window is a public good. Skills share the context window with everything else Claude needs: system prompt, conversation history, other Skills' metadata, and the actual user request.
**Default assumption: Claude is already very smart.** Only add context Claude doesn't already have. Challenge each piece of information: "Does Claude really need this explanation?" and "Does this paragraph justify its token cost?"
Prefer concise examples over verbose explanations.
### Set Appropriate Degrees of Freedom
Match the level of specificity to the task's fragility and variability:
**High freedom (text-based instructions)**: Use when multiple approaches are valid, decisions depend on context, or heuristics guide the approach.
**Medium freedom (pseudocode or scripts with parameters)**: Use when a preferred pattern exists, some variation is acceptable, or configuration affects behavior.
**Low freedom (specific scripts, few parameters)**: Use when operations are fragile and error-prone, consistency is critical, or a specific sequence must be followed.
Think of Claude as exploring a path: a narrow bridge with cliffs needs specific guardrails (low freedom), while an open field allows many routes (high freedom).
### Anatomy of a Skill
Every skill consists of a required SKILL.md file and optional bundled resources:
```
skill-name/
├── SKILL.md (required)
│ ├── YAML frontmatter metadata (required)
│ │ ├── name: (required)
│ │ └── description: (required)
│ └── Markdown instructions (required)
└── Bundled Resources (optional)
├── scripts/ - Executable code (Python/Bash/etc.)
├── references/ - Documentation intended to be loaded into context as needed
└── assets/ - Files used in output (templates, icons, fonts, etc.)
```
#### SKILL.md (required)
Every SKILL.md consists of:
- **Frontmatter** (YAML): Contains `name` and `description` fields. These are the only fields that Claude reads to determine when the skill gets used, thus it is very important to be clear and comprehensive in describing what the skill is, and when it should be used.
- **Body** (Markdown): Instructions and guidance for using the skill. Only loaded AFTER the skill triggers (if at all).
#### Bundled Resources (optional)
##### Scripts (`scripts/`)
Executable code (Python/Bash/etc.) for tasks that require deterministic reliability or are repeatedly rewritten.
- **When to include**: When the same code is being rewritten repeatedly or deterministic reliability is needed
- **Example**: `scripts/rotate_pdf.py` for PDF rotation tasks
- **Benefits**: Token efficient, deterministic, may be executed without loading into context
- **Note**: Scripts may still need to be read by Claude for patching or environment-specific adjustments
##### References (`references/`)
Documentation and reference material intended to be loaded as needed into context to inform Claude's process and thinking.
- **When to include**: For documentation that Claude should reference while working
- **Examples**: `references/finance.md` for financial schemas, `references/mnda.md` for company NDA template, `references/policies.md` for company policies, `references/api_docs.md` for API specifications
- **Use cases**: Database schemas, API documentation, domain knowledge, company policies, detailed workflow guides
- **Benefits**: Keeps SKILL.md lean, loaded only when Claude determines it's needed
- **Best practice**: If files are large (>10k words), include grep search patterns in SKILL.md
- **Avoid duplication**: Information should live in either SKILL.md or references files, not both. Prefer references files for detailed information unless it's truly core to the skill—this keeps SKILL.md lean while making information discoverable without hogging the context window. Keep only essential procedural instructions and workflow guidance in SKILL.md; move detailed reference material, schemas, and examples to references files.
##### Assets (`assets/`)
Files not intended to be loaded into context, but rather used within the output Claude produces.
- **When to include**: When the skill needs files that will be used in the final output
- **Examples**: `assets/logo.png` for brand assets, `assets/slides.pptx` for PowerPoint templates, `assets/frontend-template/` for HTML/React boilerplate, `assets/font.ttf` for typography
- **Use cases**: Templates, images, icons, boilerplate code, fonts, sample documents that get copied or modified
- **Benefits**: Separates output resources from documentation, enables Claude to use files without loading them into context
#### What to Not Include in a Skill
A skill should only contain essential files that directly support its functionality. Do NOT create extraneous documentation or auxiliary files, including:
- README.md
- INSTALLATION_GUIDE.md
- QUICK_REFERENCE.md
- CHANGELOG.md
- etc.
The skill should only contain the information needed for an AI agent to do the job at hand. It should not contain auxilary context about the process that went into creating it, setup and testing procedures, user-facing documentation, etc. Creating additional documentation files just adds clutter and confusion.
### Progressive Disclosure Design Principle
Skills use a three-level loading system to manage context efficiently:
1.**Metadata (name + description)** - Always in context (~100 words)
2.**SKILL.md body** - When skill triggers (<5k words)
3.**Bundled resources** - As needed by Claude (Unlimited because scripts can be executed without reading into context window)
#### Progressive Disclosure Patterns
Keep SKILL.md body to the essentials and under 500 lines to minimize context bloat. Split content into separate files when approaching this limit. When splitting out content into other files, it is very important to reference them from SKILL.md and describe clearly when to read them, to ensure the reader of the skill knows they exist and when to use them.
**Key principle:** When a skill supports multiple variations, frameworks, or options, keep only the core workflow and selection guidance in SKILL.md. Move variant-specific details (patterns, examples, configuration) into separate reference files.
**Pattern 1: High-level guide with references**
```markdown
# PDF Processing
## Quick start
Extract text with pdfplumber:
[code example]
## Advanced features
- **Form filling**: See [FORMS.md](FORMS.md) for complete guide
- **API reference**: See [REFERENCE.md](REFERENCE.md) for all methods
- **Examples**: See [EXAMPLES.md](EXAMPLES.md) for common patterns
```
Claude loads FORMS.md, REFERENCE.md, or EXAMPLES.md only when needed.
**Pattern 2: Domain-specific organization**
For Skills with multiple domains, organize content by domain to avoid loading irrelevant context:
```
bigquery-skill/
├── SKILL.md (overview and navigation)
└── reference/
├── finance.md (revenue, billing metrics)
├── sales.md (opportunities, pipeline)
├── product.md (API usage, features)
└── marketing.md (campaigns, attribution)
```
When a user asks about sales metrics, Claude only reads sales.md.
Similarly, for skills supporting multiple frameworks or variants, organize by variant:
```
cloud-deploy/
├── SKILL.md (workflow + provider selection)
└── references/
├── aws.md (AWS deployment patterns)
├── gcp.md (GCP deployment patterns)
└── azure.md (Azure deployment patterns)
```
When the user chooses AWS, Claude only reads aws.md.
**Pattern 3: Conditional details**
Show basic content, link to advanced content:
```markdown
# DOCX Processing
## Creating documents
Use docx-js for new documents. See [DOCX-JS.md](DOCX-JS.md).
## Editing documents
For simple edits, modify the XML directly.
**For tracked changes**: See [REDLINING.md](REDLINING.md)
**For OOXML details**: See [OOXML.md](OOXML.md)
```
Claude reads REDLINING.md or OOXML.md only when the user needs those features.
**Important guidelines:**
- **Avoid deeply nested references** - Keep references one level deep from SKILL.md. All reference files should link directly from SKILL.md.
- **Structure longer reference files** - For files longer than 100 lines, include a table of contents at the top so Claude can see the full scope when previewing.
## Skill Creation Process
Skill creation involves these steps:
1. Understand the skill with concrete examples
2. Plan reusable skill contents (scripts, references, assets)
3. Initialize the skill (run init_skill.py)
4. Edit the skill (implement resources and write SKILL.md)
5. Package the skill (run package_skill.py)
6. Iterate based on real usage
Follow these steps in order, skipping only if there is a clear reason why they are not applicable.
### Step 1: Understanding the Skill with Concrete Examples
Skip this step only when the skill's usage patterns are already clearly understood. It remains valuable even when working with an existing skill.
To create an effective skill, clearly understand concrete examples of how the skill will be used. This understanding can come from either direct user examples or generated examples that are validated with user feedback.
For example, when building an image-editor skill, relevant questions include:
- "What functionality should the image-editor skill support? Editing, rotating, anything else?"
- "Can you give some examples of how this skill would be used?"
- "I can imagine users asking for things like 'Remove the red-eye from this image' or 'Rotate this image'. Are there other ways you imagine this skill being used?"
- "What would a user say that should trigger this skill?"
To avoid overwhelming users, avoid asking too many questions in a single message. Start with the most important questions and follow up as needed for better effectiveness.
Conclude this step when there is a clear sense of the functionality the skill should support.
### Step 2: Planning the Reusable Skill Contents
To turn concrete examples into an effective skill, analyze each example by:
1. Considering how to execute on the example from scratch
2. Identifying what scripts, references, and assets would be helpful when executing these workflows repeatedly
Example: When building a `pdf-editor` skill to handle queries like "Help me rotate this PDF," the analysis shows:
1. Rotating a PDF requires re-writing the same code each time
2. A `scripts/rotate_pdf.py` script would be helpful to store in the skill
Example: When designing a `frontend-webapp-builder` skill for queries like "Build me a todo app" or "Build me a dashboard to track my steps," the analysis shows:
1. Writing a frontend webapp requires the same boilerplate HTML/React each time
2. An `assets/hello-world/` template containing the boilerplate HTML/React project files would be helpful to store in the skill
Example: When building a `big-query` skill to handle queries like "How many users have logged in today?" the analysis shows:
1. Querying BigQuery requires re-discovering the table schemas and relationships each time
2. A `references/schema.md` file documenting the table schemas would be helpful to store in the skill
To establish the skill's contents, analyze each concrete example to create a list of the reusable resources to include: scripts, references, and assets.
### Step 3: Initializing the Skill
At this point, it is time to actually create the skill.
Skip this step only if the skill being developed already exists, and iteration or packaging is needed. In this case, continue to the next step.
When creating a new skill from scratch, always run the `init_skill.py` script. The script conveniently generates a new template skill directory that automatically includes everything a skill requires, making the skill creation process much more efficient and reliable.
- Creates the skill directory at the specified path
- Generates a SKILL.md template with proper frontmatter and TODO placeholders
- Creates example resource directories: `scripts/`, `references/`, and `assets/`
- Adds example files in each directory that can be customized or deleted
After initialization, customize or remove the generated SKILL.md and example files as needed.
### Step 4: Edit the Skill
When editing the (newly-generated or existing) skill, remember that the skill is being created for another instance of Claude to use. Include information that would be beneficial and non-obvious to Claude. Consider what procedural knowledge, domain-specific details, or reusable assets would help another Claude instance execute these tasks more effectively.
#### Learn Proven Design Patterns
Consult these helpful guides based on your skill's needs:
- **Multi-step processes**: See references/workflows.md for sequential workflows and conditional logic
- **Specific output formats or quality standards**: See references/output-patterns.md for template and example patterns
These files contain established best practices for effective skill design.
#### Start with Reusable Skill Contents
To begin implementation, start with the reusable resources identified above: `scripts/`, `references/`, and `assets/` files. Note that this step may require user input. For example, when implementing a `brand-guidelines` skill, the user may need to provide brand assets or templates to store in `assets/`, or documentation to store in `references/`.
Added scripts must be tested by actually running them to ensure there are no bugs and that the output matches what is expected. If there are many similar scripts, only a representative sample needs to be tested to ensure confidence that they all work while balancing time to completion.
Any example files and directories not needed for the skill should be deleted. The initialization script creates example files in `scripts/`, `references/`, and `assets/` to demonstrate structure, but most skills won't need all of them.
#### Update SKILL.md
**Writing Guidelines:** Always use imperative/infinitive form.
##### Frontmatter
Write the YAML frontmatter with `name` and `description`:
-`name`: The skill name
-`description`: This is the primary triggering mechanism for your skill, and helps Claude understand when to use the skill.
- Include both what the Skill does and specific triggers/contexts for when to use it.
- Include all "when to use" information here - Not in the body. The body is only loaded after triggering, so "When to Use This Skill" sections in the body are not helpful to Claude.
- Example description for a `docx` skill: "Comprehensive document creation, editing, and analysis with support for tracked changes, comments, formatting preservation, and text extraction. Use when Claude needs to work with professional documents (.docx files) for: (1) Creating new documents, (2) Modifying or editing content, (3) Working with tracked changes, (4) Adding comments, or any other document tasks"
Do not include any other fields in YAML frontmatter.
##### Body
Write instructions for using the skill and its bundled resources.
### Step 5: Packaging a Skill
Once development of the skill is complete, it must be packaged into a distributable .skill file that gets shared with the user. The packaging process automatically validates the skill first to ensure it meets all requirements:
- Skill naming conventions and directory structure
- Description completeness and quality
- File organization and resource references
2.**Package** the skill if validation passes, creating a .skill file named after the skill (e.g., `my-skill.skill`) that includes all files and maintains the proper directory structure for distribution. The .skill file is a zip file with a .skill extension.
If validation fails, the script will report the errors and exit without creating a package. Fix any validation errors and run the packaging command again.
### Step 6: Iterate
After testing the skill, users may request improvements. Often this happens right after using the skill, with fresh context of how the skill performed.
**Iteration workflow:**
1. Use the skill on real tasks
2. Notice struggles or inefficiencies
3. Identify how SKILL.md or bundled resources should be updated
For complex tasks, break operations into clear, sequential steps. It is often helpful to give Claude an overview of the process towards the beginning of SKILL.md:
```markdown
Filling a PDF form involves these steps:
1. Analyze the form (run analyze_form.py)
2. Create field mapping (edit fields.json)
3. Validate mapping (run validate_fields.py)
4. Fill the form (run fill_form.py)
5. Verify output (run verify_output.py)
```
## Conditional Workflows
For tasks with branching logic, guide Claude through decision points:
```markdown
1. Determine the modification type:
**Creating new content?** → Follow "Creation workflow" below
description: [TODO: Complete and informative explanation of what the skill does and when to use it. Include WHEN to use this skill - specific scenarios, file types, or tasks that trigger it.]
---
# {skill_title}
## Overview
[TODO: 1-2 sentences explaining what this skill enables]
## Structuring This Skill
[TODO: Choose the structure that best fits this skill's purpose. Common patterns:
**1. Workflow-Based** (best for sequential processes)
- Works well when there are clear step-by-step procedures
- BigQuery: API reference documentation and query examples
- Finance: Schema documentation, company policies
**Appropriate for:** In-depth documentation, API references, database schemas, comprehensive guides, or any detailed information that Claude should reference while working.
### assets/
Files not intended to be loaded into context, but rather used within the output Claude produces.
**Examples from other skills:**
- Brand styling: PowerPoint template files (.pptx), logo files
**Appropriate for:** Templates, boilerplate code, document templates, images, icons, fonts, or any files meant to be copied or used in the final output.
---
**Any unneeded directories can be deleted.** Not every skill requires all three types of resources.
"""
EXAMPLE_SCRIPT='''#!/usr/bin/env python3
"""
Example helper script for {skill_name}
This is a placeholder script that can be executed directly.
Replace with actual implementation or delete if not needed.
Example real scripts from other skills:
- pdf/scripts/fill_fillable_fields.py - Fills PDF form fields
- pdf/scripts/convert_pdf_to_images.py - Converts PDF pages to images
"""
def main():
print("This is an example script for {skill_name}")
# TODO: Add actual script logic here
# This could be data processing, file conversion, API calls, etc.
if __name__ == "__main__":
main()
'''
EXAMPLE_REFERENCE="""# Reference Documentation for {skill_title}
This is a placeholder for detailed reference documentation.
Replace with actual reference content or delete if not needed.
Example real reference docs from other skills:
- product-management/references/communication.md - Comprehensive guide for status updates
- product-management/references/context_building.md - Deep-dive on gathering context
- bigquery/references/ - API references and query examples
## When Reference Docs Are Useful
Reference docs are ideal for:
- Comprehensive API documentation
- Detailed workflow guides
- Complex multi-step processes
- Information too lengthy for main SKILL.md
- Content that's only needed for specific use cases
## Structure Suggestions
### API Reference Example
- Overview
- Authentication
- Endpoints with examples
- Error codes
- Rate limits
### Workflow Guide Example
- Prerequisites
- Step-by-step instructions
- Common patterns
- Troubleshooting
- Best practices
"""
EXAMPLE_ASSET="""# Example Asset File
This placeholder represents where asset files would be stored.
Replace with actual asset files (templates, images, fonts, etc.) or delete if not needed.
Asset files are NOT intended to be loaded into context, but rather used within
- Run backend CLI commands through `uv run --project api <command>`.
- Before submission, all backend modifications must pass local checks: `make lint`, `make type-check`, and `uv run --project api --dev dev/pytest/pytest_unit_tests.sh`.
- Use Makefile targets for linting and formatting; `make lint` and `make type-check` cover the required checks.
- Integration tests are CI-only and are not expected to run in the local environment.
## Frontend Workflow
```bash
cd web
pnpm lint:fix
pnpm type-check:tsgo
pnpm test
```
- Read `web/AGENTS.md` for details
## Testing & Quality Practices
@@ -38,7 +29,7 @@ pnpm test
## Language Style
- **Python**: Keep type hints on functions and attributes, and implement relevant special methods (e.g., `__repr__`, `__str__`).
- **Python**: Keep type hints on functions and attributes, and implement relevant special methods (e.g., `__repr__`, `__str__`). Prefer `TypedDict` over `dict` or `Mapping` for type safety and better code documentation.
- **TypeScript**: Use the strict config, rely on ESLint (`pnpm lint:fix` preferred) plus `pnpm type-check:tsgo`, and avoid `any` types.
For setting up the frontend service, please refer to our comprehensive [guide](https://github.com/langgenius/dify/blob/main/web/README.md) in the `web/README.md` file. This document provides detailed instructions to help you set up the frontend environment properly.
**Testing**: All React components must have comprehensive test coverage. See [web/testing/testing.md](https://github.com/langgenius/dify/blob/main/web/testing/testing.md) for the canonical frontend testing guidelines and follow every requirement described there.
**Testing**: All React components must have comprehensive test coverage. See [web/docs/test.md](https://github.com/langgenius/dify/blob/main/web/docs/test.md) for the canonical frontend testing guidelines and follow every requirement described there.
<a href="./docs/bn-BD/README.md"><img alt="README in বাংলা" src="https://img.shields.io/badge/বাংলা-d9d9d9"></a>
</p>
Dify is an open-source platform for developing LLM applications. Its intuitive interface combines agentic AI workflows, RAG pipelines, agent capabilities, model management, observability features, and more—allowing you to quickly move from prototype to production.
Dify is an open-source LLM app development platform. Its intuitive interface combines AI workflow, RAG pipeline, agent capabilities, model management, observability features (including [Opik](https://www.comet.com/docs/opik/integrations/dify), [Langfuse](https://docs.langfuse.com), and [Arize Phoenix](https://docs.arize.com/phoenix)) and more, letting you quickly go from prototype to production. Here's a list of the core features:
## Quick start
@@ -137,7 +133,7 @@ Star Dify on GitHub and be instantly notified of new releases.
### Custom configurations
If you need to customize the configuration, please refer to the comments in our [.env.example](docker/.env.example) file and update the corresponding values in your `.env` file. Additionally, you might need to make adjustments to the `docker-compose.yaml` file itself, such as changing image versions, port mappings, or volume mounts, based on your specific deployment environment and requirements. After making any changes, please re-run `docker-compose up -d`. You can find the full list of available environment variables [here](https://docs.dify.ai/getting-started/install-self-hosted/environments).
If you need to customize the configuration, please refer to the comments in our [.env.example](docker/.env.example) file and update the corresponding values in your `.env` file. Additionally, you might need to make adjustments to the `docker-compose.yaml` file itself, such as changing image versions, port mappings, or volume mounts, based on your specific deployment environment and requirements. After making any changes, please re-run `dockercompose up -d`. You can find the full list of available environment variables [here](https://docs.dify.ai/getting-started/install-self-hosted/environments).
Start with the section that best matches your need. Each entry lists the problems it solves plus key files/concepts so you know what to expect before opening it.
Before changing any backend code under `api/`, you MUST read the surrounding docstrings and comments. These notes contain required context (invariants, edge cases, trade-offs) and are treated as part of the spec.
In this section, “notes” means module/class/function docstrings plus any relevant paragraph/block comments.
## Plugin & Extension Development
- **Before working**
- Read the notes in the area you’ll touch; treat them as part of the spec.
- If a docstring or comment conflicts with the current code, treat the **code as the single source of truth** and update the docstring or comment to match reality.
- If important intent/invariants/edge cases are missing, add them in the closest docstring or comment (module for overall scope, function for behaviour).
- **During working**
- Keep the notes in sync as you discover constraints, make decisions, or change approach.
- If you move/rename responsibilities across modules/classes, update the affected docstrings and comments so readers can still find the “why” and the invariants.
- Record non-obvious edge cases, trade-offs, and the test/verification plan in the nearest docstring or comment that will stay correct.
- Keep the notes **coherent**: integrate new findings into the relevant docstrings and comments; avoid append-only “recent fix” / changelog-style additions.
- **When finishing**
- Update the notes to reflect what changed, why, and any new edge cases/tests.
- Remove or rewrite any comments that could be mistaken as current guidance but no longer apply.
- Keep docstrings and comments concise and accurate; they are meant to prevent repeated rediscovery.
- **[Plugin Systems](agent_skills/plugin.md)**\
When to read this:
## Coding Style
- You’re building or debugging a marketplace plugin.
- You need to know how manifests, providers, daemons, and migrations fit together.\
What it covers: plugin manifests (`core/plugin/entities/plugin.py`), installation/upgrade flows (`services/plugin/plugin_service.py`, CLI commands), runtime adapters (`core/plugin/impl/*` for tool/model/datasource/trigger/endpoint/agent), daemon coordination (`core/plugin/entities/plugin_daemon.py`), and how provider registries surface capabilities to the rest of the platform.
This is the default standard for backend code in this repo. Follow it for new code and use it as the checklist when reviewing changes.
- Code should usually include type annotations that match the repo’s current Python version (avoid untyped public APIs and “mystery” values).
- Prefer modern typing forms (e.g. `list[str]`, `dict[str, int]`) and avoid `Any` unless there’s a strong reason.
- For dictionary-like data with known keys and value types, prefer `TypedDict` over `dict[...]` or `Mapping[...]`.
- For optional keys in typed payloads, use `NotRequired[...]` (or `total=False` when most fields are optional).
- Keep `dict[...]` / `Mapping[...]` for truly dynamic key spaces where the key set is unknown.
## Additional Notes for Agents
```python
fromdatetimeimportdatetime
fromtypingimportNotRequired,TypedDict
- All skill docs assume you follow the coding style guide—run Ruff/BasedPyright/tests listed there before submitting changes.
- When you cannot find an answer in these briefs, search the codebase using the paths referenced (e.g., `core/plugin/impl/tool.py`, `services/dataset_service.py`).
- If you run into cross-cutting concerns (tenancy, configuration, storage), check the infrastructure guide first; it links to most supporting modules.
- Keep multi-tenancy and configuration central: everything flows through `configs.dify_config` and `tenant_id`.
- When touching plugins or triggers, consult both the system overview and the specialised doc to ensure you adjust lifecycle, storage, and observability consistently.
classUserProfile(TypedDict):
user_id:str
email:str
created_at:datetime
nickname:NotRequired[str]
```
- For classes, declare member variables at the top of the class body (before `__init__`) so the class shape is obvious at a glance:
1. Review `api/.env`, `web/.env.local`, and `docker/middleware.env` values (see the `SECRET_KEY` note below).
```cli
cp .env.example .env
1. Start middleware (PostgreSQL/Redis/Weaviate).
```bash
./dev/start-docker-compose
```
1. Start backend (runs migrations first).
```bash
./dev/start-api
```
1. Start Dify [web](../web) service.
```bash
./dev/start-web
```
1. Set up your application by visiting `http://localhost:3000`.
1. Start the worker service (async and scheduler tasks, runs from `api`).
```bash
./dev/start-worker
```
1. Optional: start Celery Beat (scheduled tasks).
```bash
./dev/start-beat
```
### Environment notes
> [!IMPORTANT]
>
> When the frontend and backend run on different subdomains, set COOKIE_DOMAIN to the site’s top-level domain (e.g., `example.com`). The frontend and backend must be under the same top-level domain in order to share authentication cookies.
1. Generate a `SECRET_KEY` in the `.env` file.
- Generate a `SECRET_KEY` in the `.env` file.
bash for Linux
bash for Linux
```bash for Linux
sed -i "/^SECRET_KEY=/c\SECRET_KEY=$(openssl rand -base64 42)" .env
```
```bash
sed -i "/^SECRET_KEY=/c\\SECRET_KEY=$(openssl rand -base64 42)" .env
```
bash for Mac
bash for Mac
```bash for Mac
secret_key=$(openssl rand -base64 42)
sed -i '' "/^SECRET_KEY=/c\\
SECRET_KEY=${secret_key}" .env
```
1. Create environment.
Dify API service uses [UV](https://docs.astral.sh/uv/) to manage dependencies.
First, you need to add the uv package manager, if you don't have it already.
```bash
pip install uv
# Or on macOS
brew install uv
```
1. Install dependencies
```bash
uv sync --dev
```
1. Run migrate
Before the first launch, migrate the database to the latest version.
```bash
uv run flask db upgrade
```
1. Start backend
```bash
uv run flask run --host 0.0.0.0 --port=5001 --debug
```
1. Start Dify [web](../web) service.
1. Setup your application by visiting `http://localhost:3000`.
1. If you need to handle and debug the async tasks (e.g. dataset importing and documents indexing), please start the worker service.
```bash
uv run celery -A app.celery worker -P threads -c 2 --loglevel INFO -Q dataset,priority_dataset,priority_pipeline,pipeline,mail,ops_trace,app_deletion,plugin,workflow_storage,conversation,workflow,schedule_poller,schedule_executor,triggered_workflow_dispatcher,trigger_refresh_executor,retention
```
Additionally, if you want to debug the celery scheduled tasks, you can run the following command in another terminal to start the beat service:
```bash
uv run celery -A app.celery beat
```
```bash
secret_key=$(openssl rand -base64 42)
sed -i '' "/^SECRET_KEY=/c\\
SECRET_KEY=${secret_key}" .env
```
## Testing
1. Install dependencies for both the backend and the test environment
```bash
uv sync --dev
cd api
uv sync --group dev
```
1. Run the tests locally with mocked system environment variables in `tool.pytest_env` section in `pyproject.toml`, more can check [Claude.md](../CLAUDE.md)
```bash
cd api
uv run pytest # Run all tests
uv run pytest tests/unit_tests/ # Unit tests only
uv run pytest tests/integration_tests/ # Integration tests
- Import `configs.dify_config` for every runtime toggle. Do not read environment variables directly.
- Add new settings to the proper mixin inside `configs/` (deployment, feature, middleware, etc.) so they load through `DifyConfig`.
- Remote overrides come from the optional providers in `configs/remote_settings_sources`; keep defaults in code safe when the value is missing.
- Example: logging pulls targets from `extensions/ext_logging.py`, and model provider URLs are assembled in `services/entities/model_provider_entities.py`.
## Dependencies
- Runtime dependencies live in `[project].dependencies` inside `pyproject.toml`. Optional clients go into the `storage`, `tools`, or `vdb` groups under `[dependency-groups]`.
- Always pin versions and keep the list alphabetised. Shared tooling (lint, typing, pytest) belongs in the `dev` group.
- When code needs a new package, explain why in the PR and run `uv lock` so the lockfile stays current.
## Storage & Files
- Use `extensions.ext_storage.storage` for all blob IO; it already respects the configured backend.
- Convert files for workflows with helpers in `core/file/file_manager.py`; they handle signed URLs and multimodal payloads.
- When writing controller logic, delegate upload quotas and metadata to `services/file_service.py` instead of touching storage directly.
- All outbound HTTP fetches (webhooks, remote files) must go through the SSRF-safe client in `core/helper/ssrf_proxy.py`; it wraps `httpx` with the allow/deny rules configured for the platform.
## Redis & Shared State
- Access Redis through `extensions.ext_redis.redis_client`. For locking, reuse `redis_client.lock`.
- Prefer higher-level helpers when available: rate limits use `libs.helper.RateLimiter`, provider metadata uses caches in `core/helper/provider_cache.py`.
## Models
- SQLAlchemy models sit in `models/` and inherit from the shared declarative `Base` defined in `models/base.py` (metadata configured via `models/engine.py`).
-`models/__init__.py` exposes grouped aggregates: account/tenant models, app and conversation tables, datasets, providers, workflow runs, triggers, etc. Import from there to avoid deep path churn.
- Follow the DDD boundary: persistence objects live in `models/`, repositories under `repositories/` translate them into domain entities, and services consume those repositories.
- When adding a table, create the model class, register it in `models/__init__.py`, wire a repository if needed, and generate an Alembic migration as described below.
## Vector Stores
- Vector client implementations live in `core/rag/datasource/vdb/<provider>`, with a common factory in `core/rag/datasource/vdb/vector_factory.py` and enums in `core/rag/datasource/vdb/vector_type.py`.
- Retrieval pipelines call these providers through `core/rag/datasource/retrieval_service.py` and dataset ingestion flows in `services/dataset_service.py`.
- The CLI helper `flask vdb-migrate` orchestrates bulk migrations using routines in `commands.py`; reuse that pattern when adding new backend transitions.
- To add another store, mirror the provider layout, register it with the factory, and include any schema changes in Alembic migrations.
## Observability & OTEL
- OpenTelemetry settings live under the observability mixin in `configs/observability`. Toggle exporters and sampling via `dify_config`, not ad-hoc env reads.
- HTTP, Celery, Redis, SQLAlchemy, and httpx instrumentation is initialised in `extensions/ext_app_metrics.py` and `extensions/ext_request_logging.py`; reuse these hooks when adding new workers or entrypoints.
- When creating background tasks or external calls, propagate tracing context with helpers in the existing instrumented clients (e.g. use the shared `httpx` session from `core/helper/http_client_pooling.py`).
- If you add a new external integration, ensure spans and metrics are emitted by wiring the appropriate OTEL instrumentation package in `pyproject.toml` and configuring it in `extensions/`.
## Ops Integrations
- Langfuse support and other tracing bridges live under `core/ops/opik_trace`. Config toggles sit in `configs/observability`, while exporters are initialised in the OTEL extensions mentioned above.
- External monitoring services should follow this pattern: keep client code in `core/ops`, expose switches via `dify_config`, and hook initialisation in `extensions/ext_app_metrics.py` or sibling modules.
- Before instrumenting new code paths, check whether existing context helpers (e.g. `extensions/ext_request_logging.py`) already capture the necessary metadata.
## Controllers, Services, Core
- Controllers only parse HTTP input and call a service method. Keep business rules in `services/`.
- Services enforce tenant rules, quotas, and orchestration, then call into `core/` engines (workflow execution, tools, LLMs).
- When adding a new endpoint, search for an existing service to extend before introducing a new layer. Example: workflow APIs pipe through `services/workflow_service.py` into `core/workflow`.
## Plugins, Tools, Providers
- In Dify a plugin is a tenant-installable bundle that declares one or more providers (tool, model, datasource, trigger, endpoint, agent strategy) plus its resource needs and version metadata. The manifest (`core/plugin/entities/plugin.py`) mirrors what you see in the marketplace documentation.
- Installation, upgrades, and migrations are orchestrated by `services/plugin/plugin_service.py` together with helpers such as `services/plugin/plugin_migration.py`.
- Runtime loading happens through the implementations under `core/plugin/impl/*` (tool/model/datasource/trigger/endpoint/agent). These modules normalise plugin providers so that downstream systems (`core/tools/tool_manager.py`, `services/model_provider_service.py`, `services/trigger/*`) can treat builtin and plugin capabilities the same way.
- For remote execution, plugin daemons (`core/plugin/entities/plugin_daemon.py`, `core/plugin/impl/plugin.py`) manage lifecycle hooks, credential forwarding, and background workers that keep plugin processes in sync with the main application.
- Acquire tool implementations through `core/tools/tool_manager.py`; it resolves builtin, plugin, and workflow-as-tool providers uniformly, injecting the right context (tenant, credentials, runtime config).
- To add a new plugin capability, extend the relevant `core/plugin/entities` schema and register the implementation in the matching `core/plugin/impl` module rather than importing the provider directly.
## Async Workloads
see `agent_skills/trigger.md` for more detailed documentation.
- Enqueue background work through `services/async_workflow_service.py`. It routes jobs to the tiered Celery queues defined in `tasks/`.
- Workers boot from `celery_entrypoint.py` and execute functions in `tasks/workflow_execution_tasks.py`, `tasks/trigger_processing_tasks.py`, etc.
- Scheduled workflows poll from `schedule/workflow_schedule_tasks.py`. Follow the same pattern if you need new periodic jobs.
## Database & Migrations
- SQLAlchemy models live under `models/` and map directly to migration files in `migrations/versions`.
- Generate migrations with `uv run --project api flask db revision --autogenerate -m "<summary>"`, then review the diff; never hand-edit the database outside Alembic.
- Apply migrations locally using `uv run --project api flask db upgrade`; production deploys expect the same history.
- If you add tenant-scoped data, confirm the upgrade includes tenant filters or defaults consistent with the service logic touching those tables.
## CLI Commands
- Maintenance commands from `commands.py` are registered on the Flask CLI. Run them via `uv run --project api flask <command>`.
- Use the built-in `db` commands from Flask-Migrate for schema operations (`flask db upgrade`, `flask db stamp`, etc.). Only fall back to custom helpers if you need their extra behaviour.
- Custom entries such as `flask reset-password`, `flask reset-email`, and `flask vdb-migrate` handle self-hosted account recovery and vector database migrations.
- Before adding a new command, check whether an existing service can be reused and ensure the command guards edition-specific behaviour (many enforce `SELF_HOSTED`). Document any additions in the PR.
- Ruff helpers are run directly with `uv`: `uv run --project api --dev ruff format ./api` for formatting and `uv run --project api --dev ruff check ./api` (add `--fix` if you want automatic fixes).
## When You Add Features
- Check for an existing helper or service before writing a new util.
- Uphold tenancy: every service method should receive the tenant ID from controller wrappers such as `controllers/console/wraps.py`.
- Update or create tests alongside behaviour changes (`tests/unit_tests` for fast coverage, `tests/integration_tests` when touching orchestrations).
- Run `uv run --project api --dev ruff check ./api`, `uv run --directory api --dev basedpyright`, and `uv run --project api --dev dev/pytest/pytest_unit_tests.sh` before submitting changes.
Trigger is a collection of nodes that we called `Start` nodes, also, the concept of `Start` is the same as `RootNode` in the workflow engine `core/workflow/graph_engine`, On the other hand, `Start` node is the entry point of workflows, every workflow run always starts from a `Start` node.
## Trigger nodes
-`UserInput`
-`Trigger Webhook`
-`Trigger Schedule`
-`Trigger Plugin`
### UserInput
Before `Trigger` concept is introduced, it's what we called `Start` node, but now, to avoid confusion, it was renamed to `UserInput` node, has a strong relation with `ServiceAPI` in `controllers/service_api/app`
1.`UserInput` node introduces a list of arguments that need to be provided by the user, finally it will be converted into variables in the workflow variable pool.
1.`ServiceAPI` accept those arguments, and pass through them into `UserInput` node.
1. For its detailed implementation, please refer to `core/workflow/nodes/start`
### Trigger Webhook
Inside Webhook Node, Dify provided a UI panel that allows user define a HTTP manifest `core/workflow/nodes/trigger_webhook/entities.py`.`WebhookData`, also, Dify generates a random webhook id for each `Trigger Webhook` node, the implementation was implemented in `core/trigger/utils/endpoint.py`, as you can see, `webhook-debug` is a debug mode for webhook, you may find it in `controllers/trigger/webhook.py`.
Finally, requests to `webhook` endpoint will be converted into variables in workflow variable pool during workflow execution.
### Trigger Schedule
`Trigger Schedule` node is a node that allows user define a schedule to trigger the workflow, detailed manifest is here `core/workflow/nodes/trigger_schedule/entities.py`, we have a poller and executor to handle millions of schedules, see `docker/entrypoint.sh` / `schedule/workflow_schedule_task.py` for help.
To Achieve this, a `WorkflowSchedulePlan` model was introduced in `models/trigger.py`, and a `events/event_handlers/sync_workflow_schedule_when_app_published.py` was used to sync workflow schedule plans when app is published.
### Trigger Plugin
`Trigger Plugin` node allows user define there own distributed trigger plugin, whenever a request was received, Dify forwards it to the plugin and wait for parsed variables from it.
1. Requests were saved in storage by `services/trigger/trigger_request_service.py`, referenced by `services/trigger/trigger_service.py`.`TriggerService`.`process_endpoint`
1. Plugins accept those requests and parse variables from it, see `core/plugin/impl/trigger.py` for details.
A `subscription` concept was out here by Dify, it means an endpoint address from Dify was bound to thirdparty webhook service like `Github``Slack``Linear``GoogleDrive``Gmail` etc. Once a subscription was created, Dify continually receives requests from the platforms and handle them one by one.
## Worker Pool / Async Task
All the events that triggered a new workflow run is always in async mode, a unified entrypoint can be found here `services/async_workflow_service.py`.`AsyncWorkflowService`.`trigger_workflow_async`.
The infrastructure we used is `celery`, we've already configured it in `docker/entrypoint.sh`, and the consumers are in `tasks/async_workflow_tasks.py`, 3 queues were used to handle different tiers of users, `PROFESSIONAL_QUEUE``TEAM_QUEUE``SANDBOX_QUEUE`.
## Debug Strategy
Dify divided users into 2 groups: builders / end users.
Builders are the users who create workflows, in this stage, debugging a workflow becomes a critical part of the workflow development process, as the start node in workflows, trigger nodes can `listen` to the events from `WebhookDebug``Schedule``Plugin`, debugging process was created in `controllers/console/app/workflow.py`.`DraftWorkflowTriggerNodeApi`.
A polling process can be considered as combine of few single `poll` operations, each `poll` operation fetches events cached in `Redis`, returns `None` if no event was found, more detailed implemented: `core/trigger/debug/event_bus.py` was used to handle the polling process, and `core/trigger/debug/event_selectors.py` was used to select the event poller based on the trigger type.
description="HNSW efSearch parameter (query-time search width, -1 uses server default)",
default=-1,
)
OCEANBASE_VECTOR_POOL_SIZE:PositiveInt=Field(
description="SQLAlchemy connection pool size",
default=5,
)
OCEANBASE_VECTOR_MAX_OVERFLOW:int=Field(
description="SQLAlchemy connection pool max overflow connections",
default=10,
)
OCEANBASE_HNSW_REFRESH_THRESHOLD:int=Field(
description="Minimum number of inserted documents to trigger an automatic HNSW index refresh (0 to disable)",
default=1000,
)
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