How do I install the Skill Search skill?

Run npx skillmds@latest add modbender/skill-search 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 Search safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. Capability flags: executes scripts. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Skill Search?

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 Search free to use?

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

Who published Skill Search?

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

Skill Search by modbender — Agent Skill

Name: Skill Search
Rating: 4.2 (5 reviews)
Author: modbender

Skill Search

Find and retrieve available skills using keyword search, semantic search, or LLM-powered task matching. Use when the agent needs to discover, search, or intelligently match skills to tasks. Supports hybrid search (BM25 + semantic), natural language task descriptions, and intelligent skill recommendations.

modbender Updated Sep 9, 2026 12 repo stars

File contents

Skill Search V1.1

Find skills using keyword, semantic, or AI-powered task matching.

Overview

This skill provides three search modes:

Keyword Search (Fast BM25) — Match skill names and descriptions
Semantic Search (Embeddings) — Find skills by meaning/concept similarity
LLM Task Matching (AI-powered) — Describe your task, get skill recommendations

When to Use

Before starting a new task — check if a relevant skill exists
When user asks for capabilities — "what can you do with PDFs?"
To avoid context bloat — find the right skill first, then load only that one
When unsure which skill applies — use semantic or LLM matching for fuzzy matches

Search Modes

1. Keyword Search (Fast)

./scripts/skill_search.py keyword "weather"
./scripts/skill_search.py keyword "pdf"
./scripts/skill_search.py keyword "image generation"

2. Semantic Search (Meaning-based)

./scripts/skill_search.py semantic "automate web browsing"
./scripts/skill_search.py semantic "create images with AI"
./scripts/skill_search.py semantic "search my past conversations"

3. LLM Task Matching (AI-powered)

./scripts/skill_search.py suggest "I need to transcribe a podcast episode"
./scripts/skill_search.py suggest "Help me generate product photos"
./scripts/skill_search.py suggest "Search through my old emails"

4. List All Skills

./scripts/skill_search.py list

Usage Pattern

Search: Find skills matching your need (keyword/semantic/LLM)
Preview: Read SKILL.md metadata (description, triggers)
Load: If it's the right skill, read full body and execute

Example Workflows

User: "I need to generate some images"

Agent: Uses semantic search

./scripts/skill_search.py semantic "generate images AI"

Output:

Top matches (semantic):
1. openai-image-gen (0.87) — Batch-generate images via OpenAI Images API
2. browser (0.65) — Control web browser via Playwright

Agent: Reads SKILL.md, confirms fit, executes

User: "What skill should I use for transcribing audio?"

Agent: Uses LLM suggest

./scripts/skill_search.py suggest "transcribe audio"

Output:

Recommended skills for "transcribe audio":

1. openai-whisper-api — Transcribe audio via OpenAI Audio Transcriptions API (Whisper)
   Confidence: High
   Reason: Task explicitly matches skill purpose

2. sag — ElevenLabs text-to-speech (inverse operation, may be related)
   Confidence: Low
   Reason: Related to audio processing but output not input

Search Index

The skill maintains a local search index at:

~/.openclaw/workspace/skills/skill-search/index/skills_index.json — Skill metadata
~/.openclaw/workspace/skills/skill-search/index/embeddings.json — Semantic embeddings (lazy-loaded)

Indexing happens automatically on first semantic search if no index exists.

Force reindex:

./scripts/skill_search.py index

Resources

scripts/

skill_search.py — Main search interface (keyword/semantic/LLM)
indexer.py — Build/update search index
embeddings.py — Embedding generation (local miniLM)

index/

skills_index.json — Searchable skill metadata
embeddings_cache.json — Pre-computed embeddings for semantic search

---name: skill-searchdescription: Find and retrieve available skills using keyword search, semantic search, or LLM-powered task matching. Use when the agent needs to discover, search, or intelligently match skills to tasks. Supports hybrid search (BM25 + semantic), natural language task descriptions, and intelligent skill recommendations.--- # Skill Search V1.1 Find skills using keyword, semantic, or AI-powered task matching. ## Overview This skill provides three search modes: 1. **Keyword Search** (Fast BM25) — Match skill names and descriptions 2. **Semantic Search** (Embeddings) — Find skills by meaning/concept similarity 3. **LLM Task Matching** (AI-powered) — Describe your task, get skill recommendations ## When to Use - **Before starting a new task** — check if a relevant skill exists - **When user asks for capabilities** — "what can you do with PDFs?" - **To avoid context bloat** — find the right skill first, then load only that one - **When unsure which skill applies** — use semantic or LLM matching for fuzzy matches ## Search Modes ### 1. Keyword Search (Fast) ```bash ./scripts/skill_search.py keyword "weather" ./scripts/skill_search.py keyword "pdf" ./scripts/skill_search.py keyword "image generation" ``` ### 2. Semantic Search (Meaning-based) ```bash ./scripts/skill_search.py semantic "automate web browsing" ./scripts/skill_search.py semantic "create images with AI" ./scripts/skill_search.py semantic "search my past conversations" ``` ### 3. LLM Task Matching (AI-powered) ```bash ./scripts/skill_search.py suggest "I need to transcribe a podcast episode" ./scripts/skill_search.py suggest "Help me generate product photos" ./scripts/skill_search.py suggest "Search through my old emails" ``` ### 4. List All Skills ```bash ./scripts/skill_search.py list ``` ## Usage Pattern 1. **Search**: Find skills matching your need (keyword/semantic/LLM) 2. **Preview**: Read SKILL.md metadata (description, triggers) 3. **Load**: If it's the right skill, read full body and execute ## Example Workflows **User**: "I need to generate some images" **Agent**: *Uses semantic search* ```bash ./scripts/skill_search.py semantic "generate images AI" ``` **Output**: ``` Top matches (semantic): 1. openai-image-gen (0.87) — Batch-generate images via OpenAI Images API 2. browser (0.65) — Control web browser via Playwright ``` **Agent**: *Reads SKILL.md, confirms fit, executes* --- **User**: "What skill should I use for transcribing audio?" **Agent**: *Uses LLM suggest* ```bash ./scripts/skill_search.py suggest "transcribe audio" ``` **Output**: ``` Recommended skills for "transcribe audio": 1. openai-whisper-api — Transcribe audio via OpenAI Audio Transcriptions API (Whisper) Confidence: High Reason: Task explicitly matches skill purpose 2. sag — ElevenLabs text-to-speech (inverse operation, may be related) Confidence: Low Reason: Related to audio processing but output not input ``` ## Search Index The skill maintains a local search index at: - `~/.openclaw/workspace/skills/skill-search/index/skills_index.json` — Skill metadata - `~/.openclaw/workspace/skills/skill-search/index/embeddings.json` — Semantic embeddings (lazy-loaded) **Indexing happens automatically** on first semantic search if no index exists. **Force reindex:** ```bash ./scripts/skill_search.py index ``` ## Resources ### scripts/ - `skill_search.py` — Main search interface (keyword/semantic/LLM) - `indexer.py` — Build/update search index - `embeddings.py` — Embedding generation (local miniLM) ### index/ - `skills_index.json` — Searchable skill metadata - `embeddings_cache.json` — Pre-computed embeddings for semantic search

Frequently asked questions