Local capability finder

Search installed agent skills, MCP tools, and Codex plugins from one cached index. Search returns at most three short matches by default. Use an exact ID to inspect one result or activate one skill for one agent.

This repository contains code and documentation only. Local skills, archive folders, generated indexes, and machine configuration are excluded from Git. By default, catalog data is stored in ~/ai-agent-library, regardless of where the code is cloned or installed. Agent skill folders are scanned under the current user's home directory.

Requirements

• Python 3.11 or newer

• Node.js 18 or newer for the optional capfind CLI

• Optional PyYAML for full YAML MCP metadata and Hermes profile discovery (pip install PyYAML); without it, the shared registry supplies server names only

• Agent CLIs for MCP registration; the installer skips clients it cannot find

Related MCP server: Taproom

Install for agents

For interactive setup from npm:

npx local-capability-finder-cli add

To use a cloned checkout instead:

python install_agents.py --dry-run
python install_agents.py

The installer refreshes the local index and registers the stdio MCP server with installed Codex, Claude Code, Gemini, Antigravity, Hermes, Cursor, OpenCode, and OMP clients. Select clients with --agents codex claude, or skip catalog refresh with --skip-refresh. Repeating the command leaves matching registrations alone; same-name entries pointing elsewhere or disabled are reported as conflicts. Codex, Claude Code, Gemini, and Hermes use their installed CLIs. Cursor, Antigravity, OpenCode, and OMP JSON configurations are backed up before an entry is added. Restart an agent after installation so it discovers the three MCP tools.

The tools are search_capabilities, get_capability, and activate_skill. The stdio server supports the 2026-07-28 stateless server/discover flow and the legacy initialize handshake through 2025-11-25.

Interactive CLI

From npm, run:

npx local-capability-finder-cli add

add shows an interactive agent picker. Use arrow keys to move, space to select, and Enter to confirm. It registers the same MCP server for the selected clients. The CLI also works without interaction:

npx local-capability-finder-cli add --agent codex --agent cursor
npx local-capability-finder-cli --version
npx local-capability-finder-cli search "inspect a CAD file" --json
npx local-capability-finder-cli show skill:EXACT_ID
npx local-capability-finder-cli activate skill:EXACT_ID --agent codex
npx local-capability-finder-cli refresh

Run npx . from a cloned checkout, or use node bin/capfind.js there. setup is an alias for add. When run from a package installation outside a Git clone, setup copies the Python server to a stable user data directory before registering it, so the MCP configuration does not point into a temporary package cache. CAPFIND_PYTHON can select a Python 3.11+ executable when automatic detection does not find one. capfind --version prints the CLI package version without starting Python.

Setup prompt for a new agent

Copy this prompt into a new agent's instructions. The agent should use its own client name and install from npm, or use an existing checkout if available.

Set up and use local-capability-finder-cli.

Location:
- npm package: local-capability-finder-cli
- MCP server name: local-capability-finder

Check whether local-capability-finder is already connected. If it is missing,
register it with the CLI. Python 3.11+ and Node.js 18+ are required.

For Codex, Claude Code, Gemini, Antigravity, Hermes, Cursor, OpenCode, or OMP,
run this command and select agents in the interactive menu:
  npx local-capability-finder-cli add

For automated setup, specify the client name directly:
  npx local-capability-finder-cli add --agent YOUR_AGENT_NAME

Restart the agent after registration so it discovers the MCP tools. For other
MCP clients, clone the repository and configure a stdio server with the absolute
path to capability_mcp.py and a Python 3.11+ interpreter.

When a task may need a specialized skill, MCP tool, or plugin:
1. Call search_capabilities with a short description of the need. Keep its
   default limit of three.
2. Call get_capability with the exact ID of the best match.
3. Follow its usage details. Read an on-demand skill's SKILL.md when needed.
4. Call activate_skill with that exact skill ID and your agent name only if
   your client requires an active skill folder.

Do not load the entire skill library into the conversation. Search does not
install or activate anything. If no result fits, continue with normal tools.

For other MCP clients, adapt mcp_servers.example.yaml to that client's configuration. The manual server command can be python with the absolute path to capability_mcp.py as its argument; the installer records the absolute path to the Python interpreter it uses.

CLI

python library_catalog.py
python capability_finder.py search "inspect a CAD file" --json
python capability_finder.py search "browser automation" --kind tool
python capability_finder.py show skill:EXACT_ID
python capability_finder.py activate skill:EXACT_ID --agent codex

library_catalog.py writes skills_index.json, skills_index.md, and skills_index.tsv with local paths. It also refreshes capabilities_index.json. Searches read the index and do not scan folders or run a model. A newer source catalog or changed indexed MCP configuration triggers a refresh error. Skill-store directory changes require an explicit python library_catalog.py refresh; routine activations and installs do not interrupt searches. The generated Markdown catalog is for human inspection; do not load it into an agent prompt. library_catalog.py --if-stale HOURS still refreshes when an indexed source file changed, even if the catalog is younger than the age threshold. capability_finder.py refresh --probe-tools optionally probes local command-based MCP servers for tool names, with a five-second limit per server. Probing skips servers that declare environment variables and package runners such as npx or uvx; those servers remain searchable by server name. Without PyYAML, the shared YAML registry contributes server names only and the active Hermes YAML profile is not read. Cached probed tool names expire after seven days or when the server config or local launch file changes. The catalog covers 21 configured activation stores, including OpenClaw AutoClaw, plus the Codex plugin cache, and the on-demand archive.

An archived skill has availability: ["on-demand"]. show returns its exact SKILL.md path. Reading that file directly avoids adding it back to an agent's startup skill list; activate is for clients that require an active skill folder. Search never activates or installs anything.

Token use

Find one skill without loading the whole library

In a local snapshot with the AutoClaw and Antigravity roots included, the catalog held 3,557 skill entries. Their names and descriptions totaled 1,026,935 characters (roughly 257,000 tokens using the simple four-characters-per-token estimate). The combined SKILL.md files occupied 35.0 MB. These are local corpus sizes, not tokens automatically sent to an agent.

For the query form validation zod, the finder returned zod-validation-expert among its short matches. The compact search result was 456 characters, its detail record was 1,749 characters, and the selected SKILL.md was 9,907 characters. Together that is about 12,100 characters, or 3,000 estimated tokens. The agent can inspect the exact skill it needs without receiving the other skill descriptions or files.

That is about 98.8% less discovery text than sending the complete name and description catalog for this example. The estimate uses character counts divided by four; it is not a tokenizer measurement or a claim of 98.8% lower billed usage. MCP tool definitions, calls, caching, conversation history, and agent-specific startup behavior also affect token use. Registering this MCP server does not remove skills that an agent already loads into its startup manifest. To reduce that startup cost, trim the agent's configured skill roots or reversibly archive specialist skills after confirming they remain discoverable through the finder.

skill_archive.py supports a reversible on-demand archive. It makes a dry-run manifest by default; --apply moves only its listed skills, and --restore --manifest NAME.json restores them. Archive and restore refresh the indexes. Rebuild the optional categorized junction view afterward with python organize_skills.py.

To compare active skill-list footprints before and after a batch, save two snapshots with python startup_inventory.py --output before.json and python startup_inventory.py --output after.json. Each snapshot records active skill counts and token counts for a normalized name: description list in every installed agent root. Token counts use cl100k_base when optional tiktoken is installed. They are a consistent proxy; actual startup prompt tokens depend on each agent and require that agent's own telemetry.

In one local rollout, archiving 68 audited specialist skills removed 68 top-level skill entries from each of 19 active roots. The normalized lists fell by 4,354 cl100k_base tokens per root (82,726 across those roots). Archived folders remained searchable and an exact-ID activation was checked. These figures measure skill-list text, not billed prompt tokens.

Local files

The index includes local paths and should stay out of Git. The repository .gitignore allows only source and documentation files.

License

Apache-2.0. See LICENSE and NOTICE.

See RELEASING.md for the release procedure.

Contributing and security

See CONTRIBUTING.md for development and pull request guidance. Report vulnerabilities privately as described in SECURITY.md.