gh-fiale-plus-cc-meta-skills/skills/discover-project-skills.md

---
name: discover-project-skills
description: Analyze codebase to discover technologies and project patterns, then generate contextual skills for both
---

# Discover Project Skills

## Overview

This skill analyzes your codebase to automatically discover:
1. **Technologies used**: Frameworks, databases, messaging systems, API protocols
2. **Project-specific patterns**: Architecture flows, module organization, design patterns
3. **Contextual skills**: Generates reusable skill files that chain together

**When to use:**
- Starting work on a new codebase
- After major architectural changes
- When onboarding to a project
- To document project patterns as skills

**Output:**
- Technology skills (e.g., `kafka-patterns.md`, `grpc-best-practices.md`)
- Project skills (e.g., `api-layer-patterns.md`, `service-layer-patterns.md`)
- Saved to `.claude/skills/` for automatic discovery

## Phase 0: Pre-check Existing Skills

**Before generating skills, check for existing generated skills:**

1. Check if `.claude/skills/` directory exists in current working directory
2. If exists, scan for files containing marker: `<!-- Generated by discover-project-skills -->`
3. Count existing generated skills

**If existing skills found:**
- Present to user: "Found N existing skills: [list names]"
- Ask using AskUserQuestion tool:
  ```
  Question: "How should I proceed with existing skills?"
  Options:
    - Regenerate All: Delete all existing generated skills and create fresh
    - Update Specific: Ask which areas to regenerate, keep others
    - Cancel: Exit without making changes
  ```

**If .claude/skills/ doesn't exist:**
- Create directory: `mkdir -p .claude/skills`
- Inform user: "Created .claude/skills/ directory for generated skills"

**Error handling:**
- Cannot create directory → Suggest alternative location or check permissions
- Cannot read existing skills → Warn user, offer to continue without checking

## Phase 1: Quick Technology Scan

**Goal**: Quickly identify major technologies without deep analysis.

### Hybrid Scanning Strategy

**Estimate project size first:**
```bash
find . -type f \( -name "*.ts" -o -name "*.js" -o -name "*.java" -o -name "*.kt" -o -name "*.py" -o -name "*.go" \) | wc -l
```

**Choose scanning method:**
- **< 50 files**: Use Read/Glob/Grep tools directly
- **>= 50 files**: Use Task tool with Explore subagent

### Package Files to Scan

**Node.js:**
- Read `package.json` if exists
- Check dependencies for:
  - TypeScript: `typescript` in devDependencies or `tsconfig.json` exists
  - Express: `express` in dependencies
  - NestJS: `@nestjs/core` in dependencies
  - MongoDB: `mongodb`, `mongoose` in dependencies
  - PostgreSQL: `pg`, `typeorm`, `prisma` in dependencies
  - gRPC: `@grpc/grpc-js`, `@grpc/proto-loader` in dependencies
  - GraphQL: `graphql`, `apollo-server`, `@nestjs/graphql` in dependencies

**Java/Kotlin:**
- Read `pom.xml`, `build.gradle`, or `build.gradle.kts` if exists
- Check for:
  - Kotlin: `build.gradle.kts` file or `kotlin-stdlib` dependency
  - Ktor: `io.ktor:ktor-server-*` dependencies
  - Spring Boot: `spring-boot-starter-*` dependencies
  - Kafka: `kafka-clients`, `spring-kafka` dependencies
  - PostgreSQL: `postgresql` JDBC driver
  - MongoDB: `mongodb-driver`, `spring-data-mongodb`
  - gRPC: `grpc-*`, `protobuf-java` dependencies
  - GraphQL: `graphql-java`, `spring-boot-starter-graphql`

**Python:**
- Read `requirements.txt` or `pyproject.toml` if exists
- Check for:
  - FastAPI: `fastapi`
  - PostgreSQL: `psycopg2`, `asyncpg`
  - MongoDB: `pymongo`, `motor`
  - GraphQL: `graphene`, `strawberry-graphql`, `ariadne`

**Go:**
- Read `go.mod` if exists
- Check for:
  - gRPC: `google.golang.org/grpc`
  - Kafka: `github.com/segmentio/kafka-go`, `github.com/Shopify/sarama`
  - PostgreSQL: `github.com/lib/pq`, `gorm.io/driver/postgres`
  - MongoDB: `go.mongodb.org/mongo-driver`

### REST API Detection

Look for HTTP method annotations/decorators in code:
- **Node**: `app.get()`, `app.post()`, `@Get()`, `@Post()` decorators
- **Java/Kotlin**: `@RestController`, `@GetMapping`, `@PostMapping`, `@PutMapping`, `@DeleteMapping`
- **Python**: `@app.get`, `@app.post`, `@route` decorators

### Directory Structure Scan

Scan top 2 directory levels for these patterns:
- `/api`, `/controllers`, `/handlers`, `/routes` → API layer
- `/service`, `/domain`, `/business` → Service layer
- `/repository`, `/dao`, `/data` → Database access layer
- `/messaging`, `/events`, `/kafka` → Messaging patterns
- `/grpc`, `/proto` → gRPC usage
- `/graphql`, `/schema` → GraphQL usage

### Present Findings

Group discoveries by category:
```markdown
**Detected Technologies:**

Languages: TypeScript, Java
Frameworks: Express, Spring Boot
Databases: PostgreSQL, MongoDB
Messaging: Kafka
API Protocols: REST, gRPC
```

**Error handling:**
- No package files found → Ask user to point to key files manually
- File read errors → Skip that file, continue with others
- No technologies detected → Present findings anyway, ask user for hints

## Phase 2: Focus Selection (MANDATORY)

**Goal**: Let user choose which areas to analyze deeply.

**CRITICAL: This phase MUST NOT be skipped. Always call AskUserQuestion.**

**Step 1: Group findings from Phase 1**

Organize detected technologies into categories:
- **API Protocols**: REST (if HTTP methods detected), gRPC (if proto files), GraphQL
- **Messaging Systems**: Kafka, RabbitMQ, SQS (with producer/consumer distinction)
- **Database Access**: PostgreSQL, MongoDB, Redis
- **Framework Patterns**: Ktor, Spring Boot, NestJS, Express, FastAPI
- **Language Patterns**: Kotlin (coroutines, flows), TypeScript (decorators), Python (async)
- **Project Architecture**: Service layer, event processing, repository pattern

**Step 2: Build multi-select question**

**IMPORTANT**: AskUserQuestion supports 2-4 options per question. If more than 4 categories detected, either:
- Group related categories (e.g., "Backend Technologies" combining Framework + Database)
- Prioritize most important categories
- Use multiple questions (not recommended for this use case)

Create AskUserQuestion with all detected areas:

Example for Kotlin/Ktor/Kafka project:
```json
{
  "question": "I detected the following technologies and patterns. Which areas should I analyze deeply for skill generation?",
  "header": "Select Areas",
  "multiSelect": true,
  "options": [
    {
      "label": "REST API Endpoints",
      "description": "Extract endpoint inventory, routing patterns, handlers (detected in src/api/)"
    },
    {
      "label": "Ktor Framework Patterns",
      "description": "Routing, middleware, serialization, error handling, dependency injection"
    },
    {
      "label": "Kotlin Language Patterns",
      "description": "Coroutines, flows, sealed classes, extension functions"
    },
    {
      "label": "Kafka Messaging",
      "description": "Producer/consumer patterns, error handling, retry logic (detected in src/messaging/)"
    },
    {
      "label": "PostgreSQL Database",
      "description": "Query patterns, transaction management, repository pattern (detected in src/repository/)"
    },
    {
      "label": "Service Layer Architecture",
      "description": "Business logic organization, service patterns (detected in src/services/)"
    },
    {
      "label": "Testing Patterns",
      "description": "Test structure, mocking, integration tests"
    }
  ]
}
```

**Step 3: Validate user selection**

```
IF selections is empty THEN:
  Ask: "No areas selected. Do you want to cancel skill generation?"
  IF yes THEN: Exit gracefully
  IF no THEN: Re-prompt with options

Store selections for Phase 3:
  selected_areas = selections
  depth_strategy = "deep" IF selection count <= 2, ELSE "medium"
```

**Step 4: Report selection to user**

```
✓ Selected for deep analysis:
- REST API Endpoints
- Ktor Framework Patterns
- Kafka Messaging

Analysis depth: Deep (2-3 areas selected)
Proceeding to Phase 3...
```

**Error handling:**
- No technologies detected in Phase 1 → Ask user to provide hints or cancel
- User selects all options → Set depth to "medium", warn about breadth vs. depth

## Phase 3: Deep Analysis

**Goal**: Extract patterns, flows, and examples from selected areas.

**For each selected area:**

### Module/System Analysis

1. **Identify entry points:**
   - Controllers (REST), handlers (events), main packages
   - Use Glob to find: `**/*Controller.ts`, `**/*Handler.java`, etc.

2. **Find key abstractions:**
   - Interfaces, base classes, common utilities
   - Use Grep to search for: `interface`, `abstract class`, `extends`

3. **Map directory structure:**
   - Note naming conventions (PascalCase, camelCase, snake_case)
   - Identify module boundaries

4. **Detect dependencies:**
   - Which modules depend on which (based on directory structure)

### Deep Tech Analysis (Framework & Language Patterns)

**When user selects framework or language patterns** (e.g., "Ktor Framework", "Kotlin Language Patterns"), spawn focused Task agents.

**Step 1: Determine tech-specific analysis needs**

Map selection to analysis mandate:

| Selection | Analysis Focus | Output Skills |
|-----------|---------------|---------------|
| Ktor Framework | Routing config, middleware, serialization, error handling, DI | ktor-routing-patterns, ktor-middleware-patterns |
| Spring Boot | Controllers, services, repos, config, AOP, security | spring-boot-patterns, spring-security-patterns |
| Kotlin Language | Coroutines, flows, sealed classes, extensions, delegation | kotlin-coroutines-patterns, kotlin-idioms |
| Kafka Messaging | Producer configs, consumer groups, error/retry, serialization | kafka-producer-patterns, kafka-consumer-patterns |
| Express Framework | Routing, middleware, error handling, async patterns | express-routing-patterns, express-middleware |

**Step 2: Spawn Task agent with focused mandate**

Example for Ktor Framework:

```javascript
Task agent prompt:
"Extract Ktor framework patterns from this Kotlin codebase:

**Routing Patterns:**
- Route organization (file structure, grouping)
- Route definitions (path parameters, query params)
- Route nesting and modularization
- Provide 3+ file examples with line numbers

**Middleware Patterns:**
- Authentication/authorization setup
- Logging and monitoring middleware
- CORS configuration
- Custom middleware examples
- Provide file references for each

**Serialization Patterns:**
- Content negotiation setup
- JSON serialization config (kotlinx.serialization, Jackson, Gson)
- Request/response body handling
- Provide file examples

**Error Handling:**
- Status pages configuration
- Exception handling approach
- Error response format
- Provide file examples

**Dependency Injection:**
- How dependencies are provided to routes
- DI framework used (Koin, manual, etc.)
- Provide setup file references

For each pattern:
1. Describe what it does (1-2 sentences)
2. Provide file path with line number
3. Note any gotchas or best practices

Return findings as structured data for skill generation."

**Invoke the Task tool:**

```javascript
Task({
  subagent_type: "Explore",
  description: "Extract Ktor framework patterns",
  prompt: `[Insert the full prompt text from above, starting with "Extract Ktor framework patterns from this Kotlin codebase:"]`,
  thoroughness: "very thorough"
})
```

The Task agent will analyze the codebase and report back with findings.
```

**Step 3: Process agent findings**

Agent returns structured data:
```javascript
ktor_findings = {
  routing_patterns: [
    {
      name: "Route grouping by feature",
      description: "Routes organized by domain feature in separate files",
      examples: [
        "src/api/routes/UserRoutes.kt:12 - User management routes",
        "src/api/routes/OrderRoutes.kt:8 - Order management routes"
      ]
    },
    // ... more patterns
  ],
  middleware_patterns: [...],
  serialization_patterns: [...],
  error_handling_patterns: [...],
  di_patterns: [...]
}
```

**Step 4: Determine skill split strategy**

Based on findings volume:

```javascript
if routing_patterns.length >= 3 AND middleware_patterns.length >= 3:
  // Generate separate skills
  skills_to_generate = ["ktor-routing-patterns", "ktor-middleware-patterns"]
else if total_patterns <= 5:
  // Consolidate into single skill
  skills_to_generate = ["ktor-patterns"]
else:
  // Medium split
  skills_to_generate = ["ktor-framework-patterns", "ktor-advanced-patterns"]
```

**Step 5: Store findings for Phase 4**

```javascript
tech_analysis = {
  "ktor": ktor_findings,
  "kotlin-coroutines": coroutines_findings,
  // ... other tech analyses
}

// These will be used in Phase 4 for skill generation
```

**Error handling:**
- Agent times out → Fall back to shallow analysis with Grep
- Agent finds no patterns → Notify user, ask to skip or provide hints
- Agent returns incomplete data → Use what's available, warn user

### Flow Analysis

**For synchronous flows (REST APIs):**
1. Pick 2-3 representative endpoints
2. Note the pattern: Controller → Service → Repository → Database
3. Document error handling approach (try/catch, Result types, exceptions)
4. Note validation patterns

**For asynchronous flows (Events):**
1. Find event producers (where events are published)
2. Find event consumers (where events are processed)
3. Document message structure
4. Note retry/failure handling

### REST Endpoint Extraction (When "REST API Endpoints" Selected)

**Goal:** Build comprehensive endpoint inventory for rest-endpoints.md skill.

**Step 1: Detect route definition patterns**

Based on detected framework, search for route definitions:

**Ktor:**
```bash
# Find routing blocks
grep -rn "routing {" src/ --include="*.kt"
grep -rn "route(" src/ --include="*.kt"
grep -rn "get(" src/ --include="*.kt"
grep -rn "post(" src/ --include="*.kt"
grep -rn "put(" src/ --include="*.kt"
grep -rn "delete(" src/ --include="*.kt"
grep -rn "patch(" src/ --include="*.kt"
```

**Spring Boot:**
```bash
grep -rn "@GetMapping" src/ --include="*.java" --include="*.kt"
grep -rn "@PostMapping" src/ --include="*.java" --include="*.kt"
grep -rn "@PutMapping" src/ --include="*.java" --include="*.kt"
grep -rn "@DeleteMapping" src/ --include="*.java" --include="*.kt"
grep -rn "@PatchMapping" src/ --include="*.java" --include="*.kt"
grep -rn "@RestController" src/ --include="*.java" --include="*.kt"
```

**Express/NestJS:**
```bash
grep -rn "app.get(" src/ --include="*.ts" --include="*.js"
grep -rn "router.post(" src/ --include="*.ts" --include="*.js"
grep -rn "@Get(" src/ --include="*.ts"
grep -rn "@Post(" src/ --include="*.ts"
grep -rn "@Patch(" src/ --include="*.ts"
```

**Error handling:**
- **If no routes found**: Skip to next framework or ask user for routing file location
- **If 100+ routes found**: Sample representative endpoints (e.g., one from each controller), group by module/feature

**Step 2: Extract endpoint metadata**

**Handler identification by framework:**
- **Ktor**: Handler is the code block inside route definition (same file, inline)
- **Spring Boot**: Handler is the method name with the mapping annotation
- **Express/NestJS**: Handler is the decorated method or callback function

For each route definition found:
1. Extract HTTP method (GET, POST, PUT, DELETE, PATCH)
2. Extract path pattern (e.g., `/api/v1/users/{id}`)
3. Identify handler function/class
4. Record file path and line number

Example extraction:
```
File: src/api/routes/UserRoutes.kt:23
Method: GET
Path: /api/v1/users
Handler: UserController.listUsers()

File: src/api/routes/UserRoutes.kt:45
Method: POST
Path: /api/v1/users
Handler: UserController.createUser()
```

**Step 3: Group endpoints by resource**

Organize by path prefix:
```
User Management (/api/v1/users):
- GET /api/v1/users → UserController.kt:23
- GET /api/v1/users/{id} → UserController.kt:45
- POST /api/v1/users → UserController.kt:67
- PUT /api/v1/users/{id} → UserController.kt:89
- DELETE /api/v1/users/{id} → UserController.kt:112

Order Management (/api/v1/orders):
- GET /api/v1/orders → OrderController.kt:18
- POST /api/v1/orders → OrderController.kt:34
```

**Step 4: Detect path structure patterns**

Analyze extracted paths:
- Base path prefix (e.g., `/api/v1/`)
- Versioning strategy (v1, v2 in path vs. header)
- Resource naming (plural nouns: `/users`, `/orders`)
- ID parameter patterns (`/{id}`, `/{uuid}`)
- Nested resources (`/users/{id}/orders`)

**Step 5: Store findings for Phase 4**

```javascript
rest_analysis = {
  endpoints: grouped_endpoints,  // List of {method, path, handler, file:line}
  path_patterns: detected_patterns,
  base_path: base_path,
  versioning: versioning_strategy,
  file_references: unique_controller_files
}
```

### Pattern Extraction

Look for:
- **Design patterns**: Repository pattern, Service layer, Factory, Strategy
- **Architectural style**: Layered, hexagonal, event-driven, CQRS
- **Testing approaches**: Unit tests, integration tests, test utilities
- **Common utilities**: Logging, metrics, error handling helpers

### Chain Detection (Directory-Based Proxy)

**Build skill relationship graph based on directories:**

If directories exist:
- `/api` + `/service` + `/repository` → Chain: `api-patterns` → `service-layer-patterns` → `database-access-patterns`
- `/api` + `/messaging` → Chain: `api-patterns` → `messaging-patterns`
- `/events/producers` + `/events/consumers` → Chain: `event-producer-patterns` → `event-consumer-patterns`

**Store findings for Phase 4:**
- File paths with line numbers for examples
- Pattern names and descriptions
- Skill chain relationships

**Error handling:**
- Selected area has no patterns → Notify user, ask to skip or provide hints
- File references invalid → Validate before including, skip if invalid
- Analysis timeout → Fall back to shallow analysis, notify user

## Phase 4: Skill Generation

**Goal**: Generate skill files from analysis findings.

### When to Generate Skills

**Technology Skills:**
Generate when technology is detected AND has 2+ file examples.
Examples: `postgresql-patterns.md`, `kafka-patterns.md`, `grpc-patterns.md`

**Project Skills:**
Generate when architectural layer is detected AND has clear patterns.
Examples: `api-layer-patterns.md`, `service-layer-patterns.md`, `event-processing-patterns.md`

### Technology Skill Template

```markdown
<!-- Generated by discover-project-skills on YYYY-MM-DD -->
<!-- Invocation metadata:
  paths: src/dir1/, src/dir2/
  keywords: keyword1, keyword2, keyword3
  tech: tech1, tech2
-->
---
name: <technology>-patterns
description: <Technology> <what-it-does>. Use when <trigger-keywords>, especially in <file-patterns>, or <scenarios>.
---

# <Technology> Patterns

## Overview
[How this technology is used in this codebase - 2-3 sentences]

## Key Patterns
- **Pattern 1**: [Description]
  - Example: `path/to/file.ext:123`
- **Pattern 2**: [Description]
  - Example: `path/to/another.ext:456`

## Common Gotchas
- [What to watch for - based on code inspection]
- [Common mistake to avoid]

## Related Skills
When working with <technology>, also consider:
- `related-skill-1` - [When to use]
- `related-skill-2` - [When to use]

## Key Files
- `path/to/config:line` - [Configuration]
- `path/to/example:line` - [Reference implementation]
```

### Project Skill Template

```markdown
<!-- Generated by discover-project-skills on YYYY-MM-DD -->
<!-- Invocation metadata:
  paths: src/dir1/, src/dir2/
  keywords: keyword1, keyword2, keyword3
  tech: tech1, tech2
-->
---
name: <subsystem>-<aspect>
description: <Subsystem> <aspect> <what-it-does>. Use when <trigger-keywords>, especially in <file-patterns>, or <scenarios>.
---

# <Subsystem> <Aspect>

## Architecture
[Text description of architecture - 3-4 sentences]

## Key Components
- **Component 1** (`path/to/component`): [Purpose and responsibility]
- **Component 2** (`path/to/component`): [Purpose and responsibility]

## Flows

### Synchronous Flow
1. [Step 1 - what happens]
2. [Step 2 - what happens]
3. [Step 3 - what happens]

### Asynchronous Flow
1. [Event trigger]
2. [Processing steps]
3. [Side effects]

## Error Handling
[How errors are handled in this subsystem]

## Testing Strategy
[How this subsystem is tested]

## Related Skills
This subsystem typically interacts with:
- `related-skill-1` - [For X functionality]
- `related-skill-2` - [For Y functionality]

## Key Files
- `path/to/file:line` - [Purpose]
- `path/to/file:line` - [Purpose]
```

### Description Template Requirements

Every description MUST include all five elements:
1. **Technology/Pattern name**: "PostgreSQL database access patterns"
2. **What it does**: "database queries and ORM usage"
3. **Trigger keywords**: "database, SQL, queries, repository pattern"
4. **File patterns**: "src/repository/, src/dao/, *.repository.ts"
5. **Scenarios**: "implementing CRUD operations, optimizing queries"

**Example:**
```markdown
description: PostgreSQL database access patterns. Use when working with database queries, SQL operations, repository pattern, especially in src/repository/ or src/dao/ files, or implementing CRUD operations and query optimization.
```

### Skill Generation Process

**For each skill to generate:**

1. **Fill template with findings:**
   - Use data from Phase 3 analysis
   - Include actual file paths with line numbers
   - Add 2-4 concrete examples per pattern

2. **Build Related Skills section:**
   - Use chain detection from Phase 3
   - Link to skills that are typically used together
   - Add "when to use" context for each

3. **Token budget validation:**
   ```
   character_count = len(skill_content)
   estimated_tokens = character_count / 4

   if estimated_tokens > 500:
       # Optimization steps:
       - Remove mermaid diagram if present
       - Keep only top 3-4 patterns
       - Consolidate Key Files section
       - Remove redundant descriptions

   if still > 500:
       # Warn user and ask to proceed or simplify further
   ```

4. **Add marker comment:**
   - First line: `<!-- Generated by discover-project-skills on YYYY-MM-DD -->`
   - Helps Phase 0 detect generated skills

5. **Write to .claude/skills/:**
   ```
   filename = f".claude/skills/{skill_name}.md"
   Write skill content to filename
   ```

6. **Validate file was created:**
   - Read back the file to confirm
   - Log skill name for final summary

### Metadata Generation Logic

**For each skill being generated:**

**Step 1: Extract path patterns**

```javascript
paths = []
for file_ref in skill_findings.file_references:
  // Extract directory pattern from file path
  // src/api/routes/UserRoutes.kt → src/api/, src/api/routes/
  dir = extract_directory(file_ref)
  // IMPORTANT: Always include trailing slash to indicate directory pattern
  // Example: src/api/routes/UserRoutes.kt → src/api/routes/
  paths.add(dir)

// Deduplicate and generalize
paths = unique(paths)
paths = generalize_patterns(paths)

generalize_patterns(paths) {
  // Generalize path patterns when multiple sibling directories detected
  // Rules:
  // 1. Keep specific if only one path: ["src/api/"] → ["src/api/"]
  // 2. Generalize siblings: ["src/api/routes/", "src/api/controllers/"] → ["src/api/"]
  // 3. Keep both if different parents: ["src/api/", "src/services/"] → both kept
  // 4. Don't over-generalize to project root unless all paths share no common prefix

  // Algorithm:
  // - Group paths by parent directory
  // - If 2+ paths share same parent, use parent instead
  // - Return deduplicated list
}
```

**Step 2: Build keyword list**

```javascript
keywords = []

// Add technology name
keywords.add(tech_name)  // "ktor", "kafka", "postgresql"

// Add pattern types from findings
for pattern in skill_findings.patterns:
  keywords.add(pattern.type)  // "routing", "middleware", "consumer"

// Add common terms from description
keywords.add_all(extract_terms(description))

extract_terms(description) {
  // Extract meaningful technical terms from description
  // 1. Split on spaces, punctuation
  // 2. Lowercase all terms
  // 3. Filter out stopwords: (the, a, an, when, use, with, for, working, especially)
  // 4. Keep technical terms: HTTP, REST, API, SQL, etc.
  // 5. Keep domain nouns: endpoint, route, consumer, query, database
  // 6. Return unique list
}

// Add scenario keywords
keywords.add_all(scenario_terms)  // "request handling", "serialization"

keywords = unique(keywords)
```

**Step 3: Determine tech tags**

```javascript
tech_tags = []

// Primary technology
tech_tags.add(primary_tech)  // "ktor"

// Related technologies detected
if language:
  tech_tags.add(language)  // "kotlin"

if additional_tech:
  tech_tags.add_all(additional_tech)  // ["kafka", "postgresql"]

tech_tags = unique(tech_tags)
```

**Step 3.5: Validate metadata**

Before formatting, validate that required data exists:

```javascript
// Validation checks
if (paths.length === 0) {
  paths = ["./"]  // Default to project root
}

if (keywords.length === 0) {
  console.warn(`No keywords generated for skill ${skill_name}`)
  keywords = [primary_tech]  // Fallback to tech name only
}

if (tech_tags.length === 0) {
  throw new Error("Cannot generate metadata without tech tags")
}
```

**Step 4: Format metadata comment**

```javascript
metadata_comment = `<!-- Invocation metadata:
  paths: ${paths.join(", ")}
  keywords: ${keywords.join(", ")}
  tech: ${tech_tags.join(", ")}
-->`
```

**Example for Ktor routing skill:**

```markdown
<!-- Invocation metadata:
  paths: src/api/, src/routes/, src/api/routes/
  keywords: ktor, routing, route, endpoint, HTTP, REST, path, handler
  tech: ktor, kotlin
-->
```

**Example for Kafka consumer skill:**

```markdown
<!-- Invocation metadata:
  paths: src/messaging/kafka/, src/events/consumers/
  keywords: kafka, consumer, event, message, consume, deserialize, retry
  tech: kafka, kotlin
-->
```

### Example Skill Chains

**Standard REST API → Database:**
```markdown
# In rest-api-patterns.md
## Related Skills
- `service-layer-patterns` - For business logic implementation
- `postgresql-access` - For database operations
```

**Event-Driven Flow:**
```markdown
# In kafka-producer-patterns.md
## Related Skills
- `service-layer-patterns` - For business logic before publishing
- `kafka-consumer-patterns` - For understanding message consumers
```

### REST Endpoints Skill Template (Special Case)

When REST endpoint extraction was performed in Phase 3, generate dedicated rest-endpoints.md skill:

```markdown
<!-- Generated by discover-project-skills on YYYY-MM-DD -->
<!-- Invocation metadata:
  paths: <api paths from extraction>
  keywords: REST, API, endpoint, HTTP, route, <methods>
  tech: <framework>
-->
---
name: rest-endpoints
description: REST API endpoint inventory for this project. Use when working with API endpoints, HTTP routes, REST controllers, especially in <api-paths>, or implementing new endpoints or modifying existing ones.
---

# REST API Endpoints

## Endpoint Inventory

### <Resource Group 1>

- `<METHOD> <path>` → <handler-file>:<line>
  - Description: [If available from comments/docs]
- `<METHOD> <path>` → <handler-file>:<line>

### <Resource Group 2>

- `<METHOD> <path>` → <handler-file>:<line>
- `<METHOD> <path>` → <handler-file>:<line>

## Path Structure Patterns

- **Base path**: `<base-path>`
- **Versioning**: <strategy description>
- **Resource naming**: <pattern description>
- **ID parameters**: <pattern description>

## Request/Response Patterns

[Common patterns for request bodies, response formats, status codes]

## Authentication

[Authentication approach detected: JWT, OAuth, API keys, etc.]

## Related Skills

- `<framework>-routing-patterns` - For routing implementation details
- `service-layer-patterns` - For business logic called by endpoints
- `<database>-patterns` - For data access from endpoints

## Key Files

- `<main-routes-file>` - Route definitions
- `<controller-dir>` - Handler implementations
```

### REST Endpoints Skill Generation Trigger

In Phase 4, after generating other skills:

```javascript
if rest_analysis exists AND rest_analysis.endpoints.length > 0:
  // Build endpoint inventory section
  endpoint_inventory = group_by_resource(rest_analysis.endpoints)

  // Build path patterns section
  path_patterns = {
    base_path: rest_analysis.base_path,
    versioning: rest_analysis.versioning,
    resource_naming: analyze_naming(rest_analysis.endpoints),
    id_params: analyze_params(rest_analysis.endpoints)
  }

  // Generate skill
  skill_content = fill_rest_endpoints_template(
    endpoints: endpoint_inventory,
    path_patterns: path_patterns,
    file_references: rest_analysis.file_references
  )

  // Generate metadata
  metadata = generate_metadata(
    paths: extract_unique_dirs(rest_analysis.file_references),
    keywords: ["REST", "API", "endpoint", "HTTP", "route", ...detected_methods],
    tech: [framework_name]
  )

  // Write skill file
  write_skill(".claude/skills/rest-endpoints.md", metadata + skill_content)
```

**Error handling:**
- Skill exceeds 500 tokens after optimization → Warn user, ask to proceed or simplify
- Cannot write skill file → Report error with path, suggest alternative location
- Skill generation interrupted → Save progress so far, allow resume

## Error Handling

### Phase 0 Errors
- **Cannot create .claude/skills/**: Suggest alternative location or check permissions
- **Cannot read existing skills**: Warn user, offer to continue without checking

### Phase 1 Errors
- **No package files found**: Ask user to point to key files manually
- **Cannot determine project size**: Default to Task/Explore agents (safer for large projects)
- **File read errors**: Skip that file, continue with others
- **No technologies detected**: Present findings anyway, ask user for hints

### Phase 2 Errors
- **User selects invalid option**: Re-prompt with valid options
- **No areas selected**: Confirm if user wants to cancel

### Phase 3 Errors
- **Selected area has no patterns**: Notify user, ask to skip or provide hints
- **File references invalid**: Validate before including, skip if invalid
- **Deep analysis timeout**: Fall back to shallow analysis, notify user

### Phase 4 Errors
- **Skill exceeds 500 tokens after optimization**: Warn user, ask to proceed or simplify
- **Cannot write skill file**: Report error with path, suggest alternative location
- **Skill generation interrupted**: Save progress, allow resume

### General Error Strategy
- Never fail silently - always inform user
- Provide actionable suggestions for resolution
- Allow graceful degradation (partial results better than none)
- Continue with other skills if one fails

## After Generation

**Once all skills are generated:**

1. **Commit generated skills:**
   ```bash
   git add .claude/skills/
   git commit -m "docs: add generated project skills

   Generated by discover-project-skills skill:
   - [list skill names]

   These skills document project patterns and can be
   imported/shared across team members."
   ```

2. **Present summary to user:**
   ```markdown
   ✅ Generated N skills in .claude/skills/:

   **Technology Skills:**
   - postgresql-patterns.md
   - kafka-patterns.md

   **Project Skills:**
   - api-layer-patterns.md
   - service-layer-patterns.md

   Skills are committed and ready to use. Claude will automatically
   discover them when working on related features.
   ```

3. **Remind about maintenance:**
   ```markdown
   💡 Re-run /cc-meta-skills:discover-project-skills when:
   - Major architectural changes are made
   - New technologies are adopted
   - Patterns evolve significantly

   The skill will detect existing generated skills and offer to update them.
   ```

## Key Principles

**Scanning Strategy:**
- Use Task/Explore agents for large codebases (>50 files)
- Use direct tools (Read/Glob/Grep) for small projects (<50 files)
- Progressive disclosure: quick scan → focused deep dive

**Token Budget:**
- Keep skills concise (<500 tokens each)
- Use file references with line numbers, not code snippets
- Validate token count after generation
- Optimize if over budget

**Skill Chaining:**
- Always include "Related Skills" sections
- Use directory structure as proxy for architectural layers
- Build chains based on typical flow patterns
- Double reinforcement: explicit chains + context matching

**Descriptions:**
- Make descriptions rich with trigger keywords
- Include file patterns for context matching
- Add scenarios that trigger skill usage
- Follow template: name + what-it-does + keywords + patterns + scenarios

**Quality:**
- Each skill includes at least 3 file references
- Each skill includes 2+ concrete examples/patterns
- Validate findings with user before generating
- Handle errors gracefully with actionable feedback