skill-curator-mcp
Searches GitHub for skills using predefined topic tags to discover external skill candidates.
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skill-curator-mcp
Skill lifecycle intelligence for AI agents. Matches tasks to skills semantically, tracks effectiveness, detects gaps, and scouts external sources.
Problem
AI agents have 30+ skills but activate <5% per session. Skills exist but the agent doesn't know when to use them. No feedback loop measures if a skill actually helped.
Related MCP server: Skills Registry MCP Server
Solution
An MCP server that provides intelligent skill routing — not CRUD (skills-manager does that) nor a marketplace (daymade does that), but the missing intelligence layer:
Semantic matching: embed skills + task → cosine similarity + effectiveness boost
Feedback loop: EMA scoring tracks what works
Gap detection: identifies missing skills from session patterns
Scout: searches external sources (skills-manager marketplace, GitHub) correlated with local gaps
Tools (8)
Tool | Purpose |
| Find best skills for current task |
| Record success/partial/failure |
| Detect uncovered task patterns |
| Report: active, stale, candidates for promote/archive |
| Move draft → active |
| Deactivate with preservation |
| Rescan filesystem, regenerate embeddings |
| Search external skill sources |
Architecture
┌─────────────────────────────────────────┐
│ skill-curator-mcp │
│ (FastMCP, port 3204) │
├─────────────────────────────────────────┤
│ Index Layer (sqlite-vec embeddings) │
│ Scoring (0.6 semantic + 0.2 eff + 0.2 │
│ profile) │
│ Feedback (EMA α=0.3) │
│ Scout (HTTP → external registries) │
├─────────────────────────────────────────┤
│ Storage: ~/.local/share/skill-curator/ │
│ curator.db (SQLite WAL) │
└─────────────────────────────────────────┘
↕ MCP (StreamableHTTP)
┌─────────────────────────────────────────┐
│ Kiro CLI (agent) │
│ Steering: "call skill_match before │
│ every task" │
│ Hook startup: skill_reindex() │
│ Hook shutdown: skill_gaps() │
└─────────────────────────────────────────┘Stack
Python 3.11+
FastMCP (mcp SDK)
sqlite-vec (embeddings)
sentence-transformers (MiniLM-L6-v2 or paraphrase-multilingual-MiniLM-L12-v2)
httpx (scout HTTP calls)
uv (package management)
Schema
CREATE TABLE skills (
name TEXT PRIMARY KEY,
path TEXT NOT NULL,
description TEXT,
trigger_text TEXT,
effectiveness REAL DEFAULT 0.5,
total_uses INTEGER DEFAULT 0,
total_successes INTEGER DEFAULT 0,
gap_count INTEGER DEFAULT 0,
state TEXT DEFAULT 'active', -- active|stale|archived|draft
profile_tags TEXT, -- JSON array
last_used_at TEXT,
last_indexed_at TEXT,
created_at TEXT
);
CREATE TABLE feedback_log (
id INTEGER PRIMARY KEY,
skill_name TEXT REFERENCES skills(name),
session_id TEXT,
outcome TEXT, -- success|partial|failure
task_description TEXT,
created_at TEXT
);
CREATE TABLE scouted_skills (
id INTEGER PRIMARY KEY,
source_url TEXT NOT NULL,
name TEXT,
description TEXT,
relevance_score REAL,
matched_gap TEXT,
status TEXT DEFAULT 'new', -- new|adopted|dismissed
discovered_at TEXT
);
CREATE VIRTUAL TABLE skill_embeddings USING vec0(
name TEXT PRIMARY KEY,
embedding float[384]
);Scoring Formula
score_final = 0.6 * cosine_similarity + 0.2 * effectiveness + 0.2 * profile_matchcosine_similarity: embedding(task) vs embedding(skill.description + skill.trigger)effectiveness: EMA score (0.0-1.0, default 0.5, α=0.3)profile_match: 1.0 if skill in profile.expected_skills, else 0.0
Lifecycle Transitions
draft → active (skill_promote or effectiveness > 0.7 after 3+ uses)
active → stale (no use in 30 days)
stale → active (used again)
stale → archived (no use in 90 days, or effectiveness < 0.3)
archived → active (skill_promote)Scout Sources (MVP)
skills-manager marketplace (skills.sh) via HTTP API
GitHub search:
topic:claude-code-skillsORtopic:agent-skillsAnthropic official: github.com/anthropics/skills
Integration
Transport: StreamableHTTP on port 3204
Systemd:
~/.config/systemd/user/skill-curator.serviceSkills dir: reads
~/.kiro/skills/**/*.md+~/.kiro/skills/auto-generated/**/*.mdMigration: imports existing
.usage.jsondata on firstskill_reindex()
Development
cd ~/git/skill-curator-mcp
uv venv .venv
uv pip install -e ".[dev]"
pytestLicense
Apache-2.0
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