Skill Gap Analyzer

Skill Gap Analyzer

Automatically identifies and creates missing skills based on project requirements. Enforces MCP Code Execution pattern for token efficiency when skills use external tools.

MANDATORY EXECUTION STEPS

When this skill is invoked, you MUST execute these steps IN ORDER:

Step A: Read Requirements File

Read the requirements file passed as argument

Step B: Detect Technologies

Scan for these keywords and mark detected:

Step C: List Existing Skills

ls -la .claude/skills/

Step D: Create Missing Skills

For EACH detected technology that doesn't have a skill:

1. Create directory: mkdir -p .claude/skills/{tech}-patterns
2. Create SKILL.md with the template below
3. Add technology-specific content

Step E: Generate Report

Create .specify/skill-gap-report.json with results

Core Workflow

1. Analyze Requirements

Extract technology requirements from the project:

def analyze_requirements(requirements_text: str) -> dict:
    """Extract technologies and patterns from requirements."""

    patterns = {
        "fastapi": ["fastapi", "python api", "rest api python"],
        "nextjs": ["next.js", "nextjs", "react frontend", "frontend"],
        "express": ["express", "node.js api", "nodejs"],
        "postgresql": ["postgresql", "postgres", "sql database"],
        "mongodb": ["mongodb", "mongo", "nosql", "document database"],
        "kafka": ["kafka", "event streaming", "message queue"],
        "graphql": ["graphql", "graph api"],
        "docker": ["docker", "container", "dockerfile"],
        "kubernetes": ["kubernetes", "k8s", "kubectl"],
        "github-actions": ["github actions", "ci/cd", "pipeline"],
    }

    # MCP-related patterns (require code execution)
    mcp_patterns = {
        "google-drive": ["google drive", "gdrive", "docs api"],
        "salesforce": ["salesforce", "crm", "sfdc"],
        "slack": ["slack", "slack api", "messaging"],
        "github-api": ["github api", "repository api", "issues api"],
        "database-query": ["query database", "sql queries", "data extraction"],
        "spreadsheet": ["spreadsheet", "excel", "csv processing", "sheets"],
        "email": ["email", "smtp", "mail api"],
        "storage": ["s3", "cloud storage", "blob storage"],
    }

    detected = []
    mcp_required = []
    text_lower = requirements_text.lower()

    for tech, keywords in patterns.items():
        if any(kw in text_lower for kw in keywords):
            detected.append(tech)

    for mcp, keywords in mcp_patterns.items():
        if any(kw in text_lower for kw in keywords):
            mcp_required.append(mcp)

    return {
        "technologies": detected,
        "mcp_integrations": mcp_required,
        "requires_code_execution": len(mcp_required) > 0
    }

2. Map Technologies to Skills

3. Check Existing Skills

def check_existing_skills(required_skills: list) -> dict:
    """Check which skills exist and which are missing."""

    existing = []
    missing = []

    for skill in required_skills:
        skill_path = f".claude/skills/{skill}/SKILL.md"
        if Path(skill_path).exists():
            existing.append(skill)
        else:
            missing.append(skill)

    return {"existing": existing, "missing": missing}

4. Detect MCP Pattern Requirements

def requires_mcp_pattern(skill_name: str, requirements: dict) -> bool:
    """Determine if a skill needs MCP Code Execution pattern."""

    mcp_skill_patterns = [
        "integration", "connector", "api-client",
        "extractor", "processor", "sync"
    ]

    # Check if skill name suggests MCP usage
    if any(p in skill_name for p in mcp_skill_patterns):
        return True

    # Check if requirements mention external APIs
    mcp_keywords = [
        "external api", "third-party", "integration",
        "fetch data", "sync data", "import from",
        "export to", "connect to"
    ]

    req_text = str(requirements).lower()
    return any(kw in req_text for kw in mcp_keywords)

5. Auto-Create Missing Skills

For each missing skill, generate using appropriate template:

Standard Skill Template:

---
name: {skill-name}
description: |
  {Description}. Triggers: {keywords}
version: 1.0.0
---

# {Skill Title}

## Workflow
1. {Step 1}
2. {Step 2}

## Code Templates
...

MCP Code Execution Skill Template:

---
name: {skill-name}
description: |
  {Description}. Uses MCP Code Execution for 98% token efficiency.
  Triggers: {keywords}
version: 1.0.0
mcp-pattern: code-execution
---

# {Skill Title}

## MCP Servers Used
- `{server}`: {tools}

## Execution Pattern

This skill uses **MCP Code Execution**:
1. Agent writes code to `./workspace/task.ts`
2. Code calls MCP tools in execution environment
3. Only final summary returns to model

## Directory Structure
\`\`\`
{skill-name}/
├── SKILL.md
├── servers/
│   └── {server-name}/
│       ├── index.ts
│       └── {tool}.ts
├── scripts/
│   └── execute.py
└── workspace/
\`\`\`

## Progressive Disclosure
\`\`\`bash
ls ./servers/                    # List MCP servers
ls ./servers/{server}/           # List tools
cat ./servers/{server}/{tool}.ts # Read definition
\`\`\`

## Code Template
\`\`\`typescript
import * as server from './servers/{server}';

async function main() {
  const data = await server.{tool}({ ... });
  const filtered = data.filter(item => item.relevant);
  console.log(\`Processed \${filtered.length} items\`);
}
main();
\`\`\`

## Validation
- [ ] Code executes outside model context
- [ ] Only summary returned (~100 tokens)
- [ ] Large data processed in execution env

6. Output

Generate report:

{
  "analyzed_requirements": "path/to/requirements.md",
  "technologies_detected": ["fastapi", "postgresql", "kafka"],
  "mcp_integrations_detected": ["google-drive", "salesforce"],
  "skills_required": [
    {"name": "fastapi-generator", "mcp_pattern": false},
    {"name": "gdrive-integration", "mcp_pattern": true}
  ],
  "skills_existing": ["fastapi-generator"],
  "skills_created": [
    {"name": "postgres-setup", "mcp_pattern": false},
    {"name": "gdrive-integration", "mcp_pattern": true}
  ],
  "mcp_pattern_enforced": true,
  "ready_to_build": true
}

MCP Pattern Enforcement Rules

When generating skills that use external tools/APIs:

1. MUST use Code Execution pattern if:

   • Skill fetches large documents/datasets
   • Skill chains multiple API calls
   • Skill processes external data

2. Skill structure MUST include:

   • servers/ directory with tool wrappers
   • workspace/ for intermediate files
   • Progressive disclosure (load tools on-demand)

3. Generated code MUST:

   • Run outside model context
   • Filter/aggregate before returning
   • Return only summaries (~100 tokens)

4. SKILL.md MUST include:

   • mcp-pattern: code-execution in frontmatter
   • Tool discovery commands
   • Code template with console.log for results

Token Efficiency Targets