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Claude-skill-registry-data 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.

install
source · Clone the upstream repo
git clone https://github.com/majiayu000/claude-skill-registry-data
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry-data "$T" && mkdir -p ~/.claude/skills && cp -r "$T/data/mcp-genkit-flows-skill" ~/.claude/skills/majiayu000-claude-skill-registry-data-mcp-genkit-flows-skill && rm -rf "$T"
manifest: data/mcp-genkit-flows-skill/SKILL.md
tags
#mcp#genkit#ai-flows#orchestration#caching
source content

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

Implementation Status

⚠️ NOTE: This skill describes a GenKitFlowRegistry MCP server that was never implemented in the codebase. The 

genkit-server.py
file does not exist.

Current Genkit Integration

Genkit flows are currently accessed via:

  • Direct Backend Calls: FastAPI endpoints in 
    backend/app/api/
  • Genkit Flows Directory: 
    backend/app/genkit_flows/
  • Frontend Services: Direct HTTP calls to backend endpoints

Migration Note

If you need to execute Genkit flows:

  1. Use backend API endpoints directly
  2. Call flows via the backend's Genkit initialization
  3. Access via frontend AI service wrappers

The described MCP caching layer does not currently exist.

Integration Points

Works with:

  • Backend Genkit flows (
    backend/app/genkit_flows/
    )
  • Frontend AI Services (
    generateKscResponses
    , 
    generateCoverLetter
    )
  • FastAPI endpoints for flow execution
  • Firebase/Firestore for data persistence

Note: The 

mcp-documentation-skill
and 
mcp-configuration-skill
referenced below are deprecated.

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