wheelry/deep-skill-finder

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🚀 Install in your Agent (30 seconds)

Copy this prompt, send it to your Agent (Claude Code / Codex / OpenClaw / Cursor / 40+ supported):

Please install the deep-skill-finder skill: download from
https://www.deepskill.market/api/v1/skill-finder, extract to local skills
directory, and enable it.

That’s it — install typically completes in 15-30 seconds. Don’t like it? Uninstall anytime with one command. Next time your Agent needs a skill, DSF will find candidates and ask you before installing.

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Why deep-skill-finder

Using an Agent requires installing Skills. But which Skill actually works on your specific task?

Two problems every Agent user hits:

• Can’t find what you need. Creators write broad, abstract descriptions to rank in more searches. Your specific need gets buried under noise.
• Can’t trust what you find. Downloads and star ratings can’t prove a Skill actually ran correctly. You install one, it crashes on your task, you uninstall, you try another. 20 minutes later, you’re still looking.

deep-skill-finder solves both. Install once — your Agent handles Skill discovery, evaluation, and installation autonomously, ranking by real community runs and task-fit — not by download counts.

See 3 real cases below ↓

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See it in action · 3 real cases

Case 1 · GitHub Actions CI/CD

Others found: github-actions-gen — sparse docs, runtime bugs DSF found: cicd-pipeline-generator — detailed docs with copy-paste examples, runs clean

Case 2 · Stock market data (龙虎榜)

Others found: pywencaistock — all data endpoints down DSF found: lhb-api — purpose-built for this data source, 3 API calls all passed

Case 3 · Blog translation (GPT-4o → Chinese)

Others found: translation-pro — translation correct but too stiff, not “accessible” style DSF found: blog-polish-zhcn — translation + polish + term retention, done in 175 seconds

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How it works

8 layers of ranking intelligence and Final intelligence synthesis

deep-skill-finder goes beyond keyword matching. It evaluates every candidate through eight layers of judgment, prioritizing real capability and task fit over surface-level similarity.

1. Meta-intent awareness — When you are looking for a skill-discovery tool itself, deep-skill-finder recognizes that intent and puts the right meta-skill first.
2. Capability-first matching — Structured capability data takes priority over promotional descriptions, so rankings reflect what a skill can actually do.
3. Intent-direction reasoning — It understands that “A → B” is not the same as “B → A,” preventing reversed workflows from ranking highly.
4. Execution readiness — Relevant but impractical skills are demoted when they depend on hidden credentials, complex setup, or non-executable documentation.
5. Multi-intent coverage — For complex requests, skills that cover more of the end-to-end workflow rank above narrow, single-step tools.
6. Community corroboration — Community posts count only when they provide evidence aligned with the user’s actual intent.
7. Contradiction filtering — Fundamentally mismatched candidates are removed, even when they share similar keywords.
8. Popularity in its proper place — Download count helps break close ties, but never outweighs capability, direction, or task fit.

Final intelligence synthesis — A reasoning-driven reranker synthesizes every signal into a holistic final judgment, delivering up to five high-confidence recommendations with clear, decision-ready rationales.

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Ecosystem status

deep-skill-finder works out of the box across 40+ Agent runtimes — no matter which Agent you use, it fits:

• Claude Code · Codex · Cursor · Windsurf · Cline
• WorkBuddy · OpenClaw · CatDesk · Hermes
• Copilot · Gemini · Antigravity · Amp
• • 28 more

30-day active data (2026-08 · updated monthly):

• 40+ distinct agentType clients calling DSF
• Top skills installed by real users through DSF (each verified by 10+ distinct client installations):
  • desktop-pet (116 clients) · ppt-maker (115) · product-compare (102) · business-plan (81) · amazon-a-plus-content (78)

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Quick Start

Prerequisites

• A running Agent (Claude Code / Codex / Cursor / any of the 40+ supported clients)

Install in your Agent

Send this prompt directly to your Agent:

Please install the deep-skill-finder skill: download the skill package from
https://www.deepskill.market/api/v1/skill-finder, extract it to the local skills
directory, and enable it.

The Agent handles download, extraction, and enablement automatically.

Use

Talk to your Agent naturally. When a task needs an external Skill, DSF triggers automatically:

"Find me a skill that builds interactive dashboards from a CSV"
"Is there a skill for pulling stock market data?"
"Recommend a skill for translating technical docs into plain English"
"Set up a CI/CD pipeline that runs on every PR"

DSF returns a ranked TOP-5 with reasons. Confirm a number → installation completes automatically.

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Architecture

User describes task in natural language
            │
            ▼
    Intent understanding
  (rewrite → semantic query)
            │
            ▼
      Multi-channel recall
  ┌─────────┬──────────────┐
  │ Skill   │  Community   │
  │ profile │  test posts  │
  └────┬────┴──────┬───────┘
       └─────┬─────┘
             ▼
   8-rule semantic ranking
   → TOP 5 with reasons
             │
             ▼
   Confirm number → auto-install → run → feedback loop

Once installed, the loop runs autonomously: identify → recall → confirm → execute → feedback. Each match gets more accurate over time.

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Project structure

├── SKILL.md                  # Skill definition (Agent reads this)
└── scripts/
    ├── deep_skill_search.py  # Semantic search via Meyo retrieval service
    └── deep_skill_install.py # Download and install Skills locally

Scripts reference

Typically you don’t call these directly — the Agent handles invocation. But you can run them standalone:

Search:

python3 scripts/deep_skill_search.py "your task description" [--agent-type openclaw]

Search failures return a non-zero exit status and a structured error.code such as search_timeout, search_network_error, or search_service_error. A successful search with no matches still exits normally with an empty community list.

Install / Uninstall / List:

# Install
python3 scripts/deep_skill_install.py  --dir ~/.catpaw/skills

# Uninstall
python3 scripts/deep_skill_install.py  --dir ~/.catpaw/skills --uninstall

# List installed
python3 scripts/deep_skill_install.py --dir ~/.catpaw/skills --list

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Common questions

Q: Isn’t downloads/stars a good enough signal? A: Downloads and stars tell you what’s popular — not what runs on your specific task. DSF ranks by capability match + real community runs. Ranking rule #8 explicitly caps download count as a tie-breaker only, never as the primary signal.

Q: “A skill that installs other skills” — is this recursion? Is it safe? A: No — DSF only recommends skills. Every install requires your explicit confirmation before anything happens on your machine. Each recommended skill passes security audit and quality checks before reaching you.

Q: What if I already use SkillHub / ClawHub / Vercel find-skills? A: DSF works alongside them, not against. It multi-channel recalls across all major skill sources — SkillHub, ClawHub, GitHub, community test posts. Install DSF, try one task, decide from there.

Q: Do I need an account? A: No. DSF works standalone — no signup, no email required, no telemetry beyond an anonymous local UUID for cross-session persistence.

Q: Which Agents does it support? A: 40+ agent runtimes including Claude Code, Codex, OpenClaw, Cursor, Windsurf, Cline, WorkBuddy, Hermes, CatDesk, Copilot, and more.

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Contributing

Issues and pull requests are welcome.

If you’re a user

• If a specific Skill ranks too high or too low, the underlying signal lives in Meyo Community — leaving real run records there is the most direct way to improve future rankings.
• Report issues or request coverage of specific tasks/domains via Issues.

If you’re a Skill creator

• We index Skills across the web to build the most comprehensive Skill discovery layer. If your Skill isn’t showing up in DSF results, open an issue with your Skill URL and we’ll investigate.
• Interested in collaborating on a “Why I put my Skill on DSF” post? Reach out via Issues.

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Star History

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Found this useful?

• ⭐ Star this repo — help other Agent users discover DSF
• 💬 Open a Discussion — share your use case or ask questions
• 🐛 Report an Issue — if a recommendation seems off, tell us
• 📖 Try DSF now — 30 seconds to install

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License

MIT — free to use, modify, and distribute with attribution. See LICENSE.

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Feedback & Privacy

When you choose to rate a Skill, we upload usageScenario, skillPerformance, rating, comment, and under context: agentType, occurredAt, trajectoryIdHash, and estimatedTokenUsage.

All feedback content is first processed by a deterministic redaction script; the raw trajectory is never uploaded. estimatedTokenUsage is an integer estimate by the Agent of how many tokens the Skill execution consumed. It is not sensitive personal information, but it is submitted to the server along with the rest of the feedback.

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Related links

• Landing page: https://www.deepskill.market/skill
• Community: https://www.deepskill.market/community/skills
• SkillHub listing: https://skillhub.cn/skills/deep-skill-finder
• ClawHub listing: https://clawhub.ai/lintong123/skills/deep-skill-finder