auto-skill-connector
A two-tool MCP server that finds a matching packaged skill in Auto-Skill's catalog and can install it locally for Claude Code.
recommend_skill— search ~200k scraped Claude skills, MCP servers, and plugins by a short, keyword-rich task description, and get back the best match's fullSKILL.mdcontent to read and follow immediately; intended to be called before building something from scratch.install_skill— takes theurlreturned byrecommend_skill(optionalname) and downloads thatSKILL.mdinto~/.claude/skills/<name>/SKILL.md, making it a permanent/skillavailable in any project.Note: this is a search-and-install surface, which is broader than the README's routing-only story — the README says routing requires no skill installation and catalog skills stay hosted, so treat local installation here as optional persistence rather than the default path.
Uses Supabase as the database backend to store a large collection of Claude skills, supporting hybrid full-text and semantic search queries.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@auto-skill-connector find a skill to summarize youtube videos"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Auto-Skill
Just-in-time skill routing for coding agents. Auto-Skill watches what a task needs, hands the agent a verified, relevant technique for it, and gets out of the way. No skill installation, no directory browsing, no maintaining a local skills folder.
Works with Claude Code, Codex CLI, Cursor, and GitHub Copilot.
Does it actually help?
We measured it rather than assumed it. Routing a skill into context can just as easily override a model's own reasoning as inform it — and our first pass at this got that wrong, measurably. After fixing that (the wrapper now says "if you don't already know a correct way to do this, use this technique — it should let you do this better than you could on your own," not "apply this now") and building a judge-graded benchmark to check the result:
condition | baseline pass rate | with Auto-Skill | lift |
well-matched task + genuinely relevant skill | 50% | 90% | +40 points |
general, unfiltered task mix | 18–59% | 24–65% | +6 to +12 points |
Both numbers are real, from the same investigation, on a local weak model
(llama3.2:3b) — not cherry-picked, not from a paid frontier model that
already knows most things unaided. The first row is what Auto-Skill's
mechanism does when the pieces line up (a real capability gap, a routed
skill that's actually right for it); the second is closer to what a
representative task mix looks like. Read both, not just the good one:
backend/bench/WEAK_MODEL_BENCHMARK_REPORT_20260803.md
has the full methodology, every regression found along the way, and how
each was fixed.
Related MCP server: @skill-hub/mcp-server
Quickstart
Requires Python 3.10+ and git.
git clone https://github.com/auto-skill/auto-skill-connector
cd auto-skill-connector
python -m venv .venv
.venv\Scripts\python -m pip install -e ".[dev]"
auto-skill login
auto-skill route "build a React landing page with Tailwind"backend: self-hosted-route
route: skill
tier: full
skill plan (coding):
policy: ponytail
primary: frontend-design
next action: apply the returned skill plan in-turn; no skill installation is requiredInstalling the connector does not install skills — catalog skills stay in Auto-Skill's hosted database and are retrieved per task. Free accounts get 100 routes/month; the CLI session is a 30-day sliding token (refreshes on use).
How it works, briefly
A task gets classified (family + action), not just keyword-matched.
Auto-Skill composes an ordered plan: an always-on team/task-family policy, plus one primary specialist for this specific task.
Only high-confidence, content-hash-verified, risk-0 content is delivered as active guidance (
fulltier). Ambiguous matches come back as hints, not instructions. Content tied to one specific repo or to an agent's own sandbox filesystem is excluded — it wouldn't generalize to your machine.Your own project/team instructions always win over a routed skill if they conflict.
Full pipeline details, response tiers, and current launch scope:
docs/ROUTING.md.
Using it
Three ways to get routing, in increasing order of automation:
Mode | What it does | Setup |
CLI, on demand | You call | Nothing extra |
MCP, proactive | A connected agent calls | Add the MCP server — see |
Auto Mode, opt-in hook | Every eligible prompt is preflighted locally and routed automatically |
|
Client | Delivery path | Skill install required? |
Claude Code | MCP or Auto Mode hook | No |
Codex CLI | MCP or Auto Mode hook | No |
Cursor | MCP only (no inject hook yet — vendor limit) | No |
GitHub Copilot | MCP only (no inject hook yet — vendor limit) | No |
Auto Mode never means silent, persistent installation, and it's off until
you opt in — read SECURITY.md first, then
docs/AUTO_MODE.md for exactly what data moves
where and how to turn it off.
Commands
auto-skill search "<task>"
auto-skill route "<task>"
auto-skill route "<task>" --json --show-content
auto-skill preview "<task-or-url>"
auto-skill doctor
auto-skill login
auto-skill enable-hook [--target codex]
auto-skill disable-hook [--target codex]Full command reference, including route-prompt, feedback, metrics,
and the optional developer install/validate export for skill authors:
docs/AUTHORING.md.
Repo layout
Client (repo root):
auto_skill_cli.py,mcp_server.py,hooks/— the package end users install. Talks to the hosted backend over HTTP.Backend (
backend/): the FastAPI/SQLite service behindskills.autoskill.dev— ingestion, embeddings, routing, content storage, deploy scripts. Own run story and CI; not part of the pip package. Seebackend/README.mdandbackend/RUNBOOK.md.
The backend subtree tracks the standalone backend repository at
https://github.com/auto-skill/auto-skill.git. After syncing it, update
.autoskill-backend-subtree.json and run
python scripts/check_backend_subtree.py --check-remote.
Security
Skills are instructions that can shape agent behavior. Content-hash
matching detects drift; it does not establish trust. Read
SECURITY.md before enabling Auto Mode or installing an
unfamiliar skill.
More
docs/ROUTING.md— pipeline internals, response tiers, current launch scope, what's deferred.docs/MCP.md— per-client MCP setup, self-hosting the connector, tunnels.docs/AUTO_MODE.md— the opt-in prompt hook, what data it sends, diagnostics and analytics (both off by default).docs/AUTHORING.md— turning team standards into a skill, the optional localinstall/validateexport.backend/bench/WEAK_MODEL_BENCHMARK_REPORT_20260803.md— does Auto-Skill actually help, measured.
Available Tools
2 toolsinstall_skillA
Download a skill's SKILL.md (by url, as returned from recommend_skill) and install it into ~/.claude/skills//SKILL.md so Claude Code can invoke it as a normal /skill from now on, in any project.
Inputs: url (required), name. Returns: result
recommend_skillA
Search the auto-skill database (~200k scraped Claude skills, MCP servers, and plugins) for the one that best matches a task, and return its full SKILL.md content so it can be read and followed immediately.
Call this whenever the user's request might already be covered by an existing packaged skill/MCP server, before building something from scratch. Pass a short, keyword-rich description of the task.
Inputs: task (required).
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 2 tools
The two tools have clearly distinct purposes: one searches and returns skill content, the other installs a skill from a URL. No overlap in functionality.
Both tools follow a consistent verb_noun pattern: 'install_skill' and 'recommend_skill', making them predictable and easy to distinguish.
With only two tools, the server feels minimal for a skill management system. While it covers the core workflow of finding and installing skills, it lacks tools for listing, updating, or removing skills, which limits its utility.
The server covers the basic find-and-install flow but is missing lifecycle operations like listing installed skills, updating, or removing them. Users may need additional tools to manage their skill set effectively.
Maintenance
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