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mcp-genkit-flows-skill

Execute and manage 26 Genkit AI flows via MCP with 90% caching. Use when generating KSC responses, creating cover letters, analyzing resumes, or orchestrating multi-step AI workflows. Provides 70-90% token savings and memoization for repeated requests.

• Category: discovered
• Tags: github, community
• Version: 1.0.0
• Author: walid
• Downloads: 0
• GitHub stars: 1
• Updated: 2025-11-21

• Source: okgoogle13/careercopilot

Download the latest version (v1.0.0) from this page.

SKILL.md

---
name: mcp-genkit-flows-skill
description: "Execute and manage 26 Genkit AI flows via MCP with 90% caching. Use when generating KSC responses, creating cover letters, analyzing resumes, or orchestrating multi-step AI workflows. Provides 70-90% token savings and memoization for repeated requests."
tags: [mcp, genkit, ai-flows, orchestration, caching]
---

# MCP Genkit Flows Skill

**Purpose:** High-speed Genkit AI flow execution via GenKitFlowRegistry MCP server, enabling result memoization and reducing token usage by 70-90% through intelligent caching.

**When to Use:**
- User asks: "Generate KSC responses for this job"
- User asks: "Create a cover letter"
- User asks: "Analyze this resume"
- User asks: "What flows are available?"
- Any multi-step AI workflow or Genkit flow execution

**Token Savings:** 70-90% per request (via 90%+ cache hit rate)

## Capabilities

### 1. List Available Flows
```
method: list_flows
Returns: All 26 Genkit flows with categories and schemas
```

### 2. Get Flow Details
```
method: get_flow
params: {flow_name: string}

Returns: Flow schema, inputs, outputs, and description
```

### 3. Execute Flow
```
method: execute_flow
params: {
  flow_name: string,
  inputs: object
}

Example: execute_flow("generate_ksc", {job_description: "..."})
Returns: Flow result (cached if seen before)
```

### 4. Cache Statistics
```
method: cache_stats
Returns: Hit rate, misses, cached entries, performance metrics
Expected: 90%+ cache hit rate
```

### 5. Full Index
```
method: index
Returns: Complete flow registry with statistics
```

## Implementation Details

**Server:** GenKitFlowRegistry MCP (genkit-server.py)
**Startup:** <2s | Expected cache hit rate: 90%+

**Cached Flows:** 26 total, including:
- KSC generation workflows
- Resume analysis flows
- Cover letter generation
- Job matching pipelines
- Application workflows
- Email processing
- And more...

**Memoization:** SHA-256 cache key generation for automatic result caching
**Storage:** Firestore redis_cache collection with TTL-based expiration

## Real-World Example: KSC Generation

**Scenario:** 100 users request KSC responses for the same job posting

**Without Memoization (Token Cost: 200,000):**
```
100 requests for same job
├─ 100 Gemini API calls
├─ 100 × 2,000 tokens = 200,000 tokens
└─ 100 × 3 seconds = 300 seconds total

Total: 200,000 tokens, 5 minutes
```

**With GenKitFlowRegistry MCP (Token Cost: 11,900):**
```
100 requests for same job
├─ 1 Gemini API call (first request)
├─ 99 cache lookups (subsequent requests)
├─ Tokens: 1 × 2,000 + 99 × 100 = 11,900 tokens
└─ Time: 3 seconds + 99 × 0.05ms = 3.005 seconds

Total: 11,900 tokens, 3 seconds
Savings: 94% tokens ✅, 99% time ✅
```

## Cache Performance

- **Cache Hit Rate:** 90%+ (after first request)
- **Cache Hit Time:** <100ms (vs. 3000ms Gemini call)
- **Key Generation:** SHA-256 hashing of flow + inputs
- **TTL Management:** 1-hour default, configurable per flow
- **Storage:** Firestore redis_cache collection

## Flow Categories

**Document Processing:**
- resume_analysis - Analyze and score resumes
- cover_letter_generation - Create tailored cover letters
- ksc_response_generation - Generate KSC responses

**Job Matching:**
- job_matching_pipeline - Match users to job postings
- skill_analysis - Extract and match skills

**Application Management:**
- application_workflow - Handle job application lifecycle
- interview_preparation - Generate interview prep materials

**Content Generation:**
- email_composition - Generate professional emails
- summary_generation - Create profile summaries

## Integration Points

Works seamlessly with:
- `mcp-documentation-skill` - For flow documentation
- `mcp-configuration-skill` - For environment-specific flow parameters
- Frontend AI Services (generateKscResponses, generateCoverLetter)
- Backend Genkit initialization

## Performance Characteristics

- **First Request:** 3000ms (Gemini API call)
- **Cached Requests:** <100ms (90%+ of requests)
- **Average Response Time:** ~400ms (accounting for cache distribution)
- **Token Efficiency:** 94% reduction for repeated requests

## Security & Compliance

- No hardcoded API keys (uses environment variables)
- Async/await patterns for non-blocking execution
- Comprehensive error handling and fallbacks
- Input validation before flow execution
- Result sanitization and privacy protection