How do I install the Skill Finder skill?

Run npx skillmds@latest add zedarvates/skill-finder in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Skill Finder safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Skill Finder?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Skill Finder free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Skill Finder?

zedarvates (@zedarvates) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Skill Finder by zedarvates — Agent Skill

Name: Skill Finder
Author: zedarvates

Skill Finder

Find which skills/tools/MCP are relevant to a task by searching SKILL.md files locally — zero cloud tokens (lexical/fuzzy match, optional local-LLM rerank). Use when you need to pick tools for a project or task and want to avoid spending a paid cloud model on the search step. Also use when the user mentions skill search, tool selection, "which skill should I use", or cutting token cost on routing/recall.

zedarvates 9de3e64 5 files · 16.4 KB Updated Sep 17, 2026

File contents

skill_finder — local, zero-token skill & tool search

Picking the right skill/tool for a task is retrieval, not reasoning — so it should not cost paid cloud tokens. This module does the search locally and leaves the cloud model free for the actual work.

Two tiers

Tier 0 — free (0 tokens total): lexical + fuzzy match over each skill's name, description, tags, triggers and full SKILL.md body. Deterministic.
Tier 1 — local (0 cloud tokens): for ambiguous queries, a local model (via [[llm_backends]]) re-ranks the shortlist. Still no cloud spend.

Use it

python -m skills.skill_finder.cli "optimize slow postgres queries"
python -m skills.skill_finder.cli "set up an A/B test" --local      # local-LLM rerank
python -m skills.skill_finder.cli "audit dead code" --roots ~/.claude/skills --json

from skills.skill_finder import find
r = find("decide local vs cloud routing", top_k=5)
r["cloud_tokens"]   # 0
r["matches"]        # [{name, score, why, description, path, tokens_est}, …]

--roots points at any directory tree containing SKILL.md files (your global skill library, a project's skills, etc.); defaults to this repo's skills/. Works whether or not a SKILL.md has YAML frontmatter — the body is always indexed.

Why it saves money

A coding agent normally burns expensive context having the cloud model read every skill description to decide what to load. find_skills returns a ranked shortlist for 0 cloud tokens, so the cloud model only sees the few skills that matter — or none, when the local rerank is decisive.

Exposed via [[llm_mcp]] as the find_skills tool. Related: skill_project_optimizer (rule-based per-project filtering), auto_router.

---name: skill-finderdescription: Find which skills/tools/MCP are relevant to a task by searching SKILL.md files locally — zero cloud tokens (lexical/fuzzy match, optional local-LLM rerank). Use when you need to pick tools for a project or task and want to avoid spending a paid cloud model on the search step. Also use when the user mentions skill search, tool selection, "which skill should I use", or cutting token cost on routing/recall.---# skill_finder — local, zero-token skill & tool searchPicking the right skill/tool for a task is **retrieval, not reasoning** — so itshould not cost paid cloud tokens. This module does the search locally and leavesthe cloud model free for the actual work.## Two tiers- **Tier 0 — free (0 tokens total):** lexical + fuzzy match over each skill's name, description, tags, triggers and full SKILL.md body. Deterministic.- **Tier 1 — local (0 cloud tokens):** for ambiguous queries, a local model (via [[llm_backends]]) re-ranks the shortlist. Still no cloud spend.## Use it```bashpython -m skills.skill_finder.cli "optimize slow postgres queries"python -m skills.skill_finder.cli "set up an A/B test" --local # local-LLM rerankpython -m skills.skill_finder.cli "audit dead code" --roots ~/.claude/skills --json``````pythonfrom skills.skill_finder import findr = find("decide local vs cloud routing", top_k=5)r["cloud_tokens"] # 0r["matches"] # [{name, score, why, description, path, tokens_est}, …]````--roots` points at any directory tree containing `SKILL.md` files (your globalskill library, a project's skills, etc.); defaults to this repo's `skills/`.Works whether or not a SKILL.md has YAML frontmatter — the body is always indexed.## Why it saves moneyA coding agent normally burns expensive context having the cloud model read everyskill description to decide what to load. `find_skills` returns a ranked shortlistfor **0 cloud tokens**, so the cloud model only sees the few skills that matter —or none, when the local rerank is decisive.Exposed via [[llm_mcp]] as the `find_skills` tool. Related:`skill_project_optimizer` (rule-based per-project filtering), `auto_router`.

Frequently asked questions