Best use case
Code Documenter is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
## Overview
Teams using Code Documenter should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/code-documenter/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How Code Documenter Compares
| Feature / Agent | Code Documenter | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
## Overview
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Code Documenter
## Overview
Analyzes source code to generate accurate, context-aware documentation at multiple levels: inline comments for complex logic, function/class docstrings, module-level overviews, architecture documents, and onboarding guides. Understands control flow, data transformations, side effects, and cross-module dependencies.
## Instructions
When asked to document code:
1. **Determine the documentation level needed:**
- **Inline**: Function/method docstrings, JSDoc, Python docstrings, Go doc comments
- **Module**: README or header comment explaining the module's purpose and exports
- **Architecture**: High-level document showing how modules interact, data flows, key design decisions
- **Onboarding**: Getting-started guide for new team members
2. **For function/method documentation, extract:**
- Purpose: What does this function do? (one sentence)
- Parameters: name, type, description, default values, constraints
- Return value: type, description, possible values
- Side effects: database writes, API calls, file I/O, state mutations, cache invalidation
- Exceptions/errors: what can go wrong, when, and what error types
- Example usage: realistic call with realistic data
3. **For complex logic, add inline comments explaining:**
- WHY, not WHAT — explain the reasoning, not the obvious operation
- Business rules embedded in conditionals
- Performance-critical sections and why they're written a certain way
- Workarounds with links to issues/bugs if available in git history
- Non-obvious algorithm choices
4. **For architecture documentation:**
- Map module dependencies (imports/requires graph)
- Identify entry points (API handlers, CLI commands, event listeners)
- Trace key data flows through the system (e.g., "user signup" flow)
- Document configuration and environment variables
- List external service dependencies (databases, APIs, queues)
- Note design patterns used (repository pattern, event sourcing, CQRS, etc.)
5. **Language-specific format:**
- TypeScript/JavaScript: JSDoc with `@param`, `@returns`, `@throws`, `@example`
- Python: Google-style docstrings with Args, Returns, Raises sections
- Go: Package-level and exported function doc comments per Go conventions
- Java: Javadoc with `@param`, `@return`, `@throws`
- Rust: `///` doc comments with `# Examples` sections
6. **Quality rules:**
- Never state the obvious ("Increments counter by 1")
- Document the contract, not the implementation (what, not how — unless the how is surprising)
- Include realistic example values, not `foo`, `bar`, `test123`
- Flag functions that are too complex and suggest refactoring before documenting
- Note when documentation reveals a likely bug (e.g., unhandled null case)
## Examples
### Example 1: Document a Python function
**Input:**
```python
def process_payment(user_id, amount, currency, idempotency_key=None):
user = db.users.find(user_id)
if not user:
raise ValueError("User not found")
if user.balance < amount and not user.credit_enabled:
return {"status": "declined", "reason": "insufficient_funds"}
charge = stripe.charges.create(
amount=int(amount * 100),
currency=currency,
customer=user.stripe_id,
idempotency_key=idempotency_key
)
db.transactions.insert({
"user_id": user_id, "amount": amount,
"stripe_charge_id": charge.id, "created_at": datetime.utcnow()
})
if user.balance < amount:
user.balance = user.balance - amount
db.users.update(user)
return {"status": "success", "charge_id": charge.id}
```
**Output:**
```python
def process_payment(user_id, amount, currency, idempotency_key=None):
"""Process a payment by charging the user's Stripe account.
Validates the user's balance (unless credit is enabled), creates a Stripe
charge, records the transaction, and deducts from the user's internal balance
if applicable.
Args:
user_id: Internal user identifier (e.g., "usr_8f2k3j").
amount: Charge amount in major currency units (e.g., 49.99).
currency: ISO 4217 currency code (e.g., "usd", "eur").
idempotency_key: Optional key to prevent duplicate charges on retry.
Recommended for all production calls.
Returns:
dict: {"status": "success", "charge_id": "ch_..."} on success,
{"status": "declined", "reason": "insufficient_funds"} if balance
is too low and credit is not enabled.
Raises:
ValueError: If user_id does not match any user in the database.
stripe.error.CardError: If the card is declined by Stripe.
stripe.error.InvalidRequestError: If Stripe parameters are invalid.
Side Effects:
- Creates a Stripe charge (external API call)
- Inserts a row into the transactions table
- May update the user's balance
Example:
>>> result = process_payment("usr_8f2k3j", 49.99, "usd", idempotency_key="pay_retry_001")
>>> result
{"status": "success", "charge_id": "ch_3Kx9mPqR2s"}
Note:
⚠ Potential bug: balance deduction only happens when balance < amount,
but the charge always goes through. If balance >= amount, the user is
charged via Stripe but their internal balance is not deducted.
"""
```
### Example 2: Generate architecture overview
**Input:**
```
Document the architecture of our notification service in src/services/notifications/
```
**Output:**
```markdown
# Notification Service — Architecture
## Purpose
Delivers user notifications across email, push, and in-app channels.
Handles template rendering, delivery scheduling, preference checking,
and delivery status tracking.
## Module Map
notifications/
├── router.ts — HTTP endpoints for notification preferences and history
├── service.ts — Core orchestration: resolves channel, renders template, dispatches
├── channels/
│ ├── email.ts — SendGrid integration (SMTP fallback)
│ ├── push.ts — Firebase Cloud Messaging
│ └── in-app.ts — WebSocket broadcast + database persistence
├── templates/
│ ├── renderer.ts — Handlebars template engine with i18n support
│ └── templates/ — .hbs files organized by notification type
├── preferences.ts — User channel preferences (opt-in/opt-out per type)
└── queue.ts — Bull queue consumer for async delivery
## Key Data Flow: Sending a Notification
1. API call or internal event → service.send(userId, type, data)
2. service.ts checks user preferences → skips opted-out channels
3. renderer.ts renders template with user's locale
4. Dispatch queued via Bull (Redis-backed) for reliability
5. Channel adapter delivers and records status in notifications table
## External Dependencies
- SendGrid API (email delivery)
- Firebase Cloud Messaging (push notifications)
- Redis (Bull queue backing store)
- PostgreSQL (notification history, preferences, templates metadata)
## Environment Variables
SENDGRID_API_KEY, FCM_SERVER_KEY, REDIS_URL, NOTIFICATION_FROM_EMAIL
```
## Guidelines
- Read the code before documenting — understand the actual behavior, not just function names.
- When you spot bugs or inconsistencies during documentation, flag them explicitly.
- Architecture docs should be useful to a new engineer on day 1 — no assumed context.
- Keep docstrings focused on the contract. Move implementation details to inline comments.
- For modules with no tests, note it: "⚠ No test coverage — documented behavior is inferred from code."
- Update documentation when the code changes — stale docs are worse than no docs.Related Skills
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