본문으로 건너뛰기
cccskills
무료GitHub에 공개

skills-vote

Find the most relevant external agent skills for the current task, then submit grounded feedback about which skills were actually used and useful in the same session. Whenever you start a task, use this skill first.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

설치 방법 보기

포함된 파일(11)

  • SKILL.md4.8 KB
  • .gitignore459 B
  • docs/feedback_guide.md7.4 KB
  • scripts/check_api_key.ps1308 B
  • scripts/check_api_key.sh240 B
  • scripts/download.py8.4 KB
  • scripts/feedback.py3.6 KB
  • scripts/models.py2.0 KB
  • scripts/prompt.py7.0 KB
  • scripts/recommend.py5.5 KB
  • scripts/utils.py2.5 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Skill Discovery And Feedback

Read only this file first. Do not read scripts/ or any other files in this skill unless this file or a script output explicitly tells you to do so.

Use this skill in two moments:

  • Before you fetch or invoke external skills for the current task.
  • After the task is effectively done, to report what skills were actually useful.

Skip this skill if you are not considering any external skills at all.

All paths mentioned in this file are relative to this skill root. cd to this root directory before running any command here.

Preconditions

Before using this skill, ensure that:

  • SKILLS_VOTE_API_KEY is set in the environment
  • uv is installed and available on PATH
  • the runtime can execute local scripts with uv run
  • GITHUB_TOKEN or GH_TOKEN may be needed later if GitHub blocks skill downloads because the repo is private or rate-limited
  1. Confirm SKILLS_VOTE_API_KEY is set:
    • macOS or Linux: bash scripts/check_api_key.sh
    • Windows PowerShell: powershell -ExecutionPolicy Bypass -File scripts/check_api_key.ps1
  2. Verify that uv is installed: uv -V
  3. If uv is missing, install it from the official docs.
    • macOS or Linux:
      • If curl is available, curl -LsSf https://astral.sh/uv/install.sh | sh
      • Otherwise wget -qOhttps://astral.sh/uv/install.sh | sh
    • Windows PowerShell: powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
  4. Verify again: uv -V

Recommend

Request schema

recommend.py accepts one JSON object with these fields:

query (str): A standalone, explicit, and retrieval-optimized description of the user's task. Rewrite the original request to improve clarity, specificity, and usefulness for search, retrieval, or downstream planning. When appropriate, include reasonable implied constraints, likely substeps, supporting tasks, or candidate approaches that are directly relevant to completing the task. Favor expansions that make the task easier to retrieve against or execute, but avoid adding weakly supported assumptions, unrelated details, or excessive verbosity. For example, if the original query is "make a video," the rewritten query may expand it into a fuller task such as planning the content, identifying the audience, drafting a script, preparing slides, designing charts or visual assets, considering animation tools like Manim, recording narration, editing the final video, and rehearsing delivery.

  • client_name (Literal["codex", "codex-app", "claude-code", "cursor", "gemini-cli", "openclaw-cli", "opencode"] | None = None): Name of this agent. If unknown/unverified or not listed, omit the field or return null.
  • client_version (str | None = None): Version of this agent. Prefer the exact version string reported by the client itself. If unknown/unverified, omit the field or return null.
  • download_dir (str = ".skills_vote/"): Directory to download recommended skills into. Relative paths are resolved from the current working directory. The path must be writable from the current runtime.

Example

Before sending the request, try to identify the client_name and client_version from the executable or CLI when possible. If no command exists to extract the version and it cannot be retrieved from the environment (e.g., some desktop apps), omit these fields.

client_nameclient_versioncommandoutput
openclaw-cli2026.3.24openclaw -vOpenClaw 2026.3.24 (cff6dc9)
codex0.117.0codex -Vcodex-cli 0.117.0
codex-app26.325.21221N/AN/A
claude-code2.1.85claude -v2.1.85 (Claude Code)
cursor2.6.13cursor -v2.6.13
gemini-cli0.35.1gemini -v0.35.1
opencode1.3.0opencode -v1.3.0

Next, run recommend.py exactly once with one JSON object on stdin via EOF. Do not pass prose around the JSON, multiple JSON objects, or extra shell flags.

recommend.py may take around 5 minutes end to end. You must wait for it to finish completely and must not do other work before it exits. If you need progress, keep watching stdout until the command finishes.

uv run -qq scripts/recommend.py <<'EOF'
{
  "query": "Add integration tests for a FastAPI skill recommendation flow, mock the gateway, and verify the returned skills and feedback flow.",
  "client_name": "codex",
  "client_version": "0.117.0",
  "download_dir": ".skills_vote/"
}
EOF

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

Web search using curl + multiple search engines (Baidu, Google, Bing, DuckDuckGo). Activates when user asks to search, look up, or query something online. Includes security enhancements: input sanitization, command injection protection, and URL encoding.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

MemTensor/skills-vote3042026년 10월 1일 업데이트

Download videos and audio from 1500+ websites including YouTube, Bilibili, TikTok, Twitter/X, Instagram, Vimeo, and more using yt-dlp. Use when the user wants to download videos, save media from social platforms, download with specific resolution (720p/1080p/4K), get subtitles, or download entire playlists. Triggers on requests like 'download this video', 'save this YouTube video', 'download in 1080p', 'download with subtitles', 'download this playlist'.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

MemTensor/skills-vote3042026년 10월 1일 업데이트

pdf

무료

Use this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text/tables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating new PDFs, filling PDF forms, encrypting/decrypting PDFs, extracting images, and OCR on scanned PDFs to make them searchable. If the user mentions a .pdf file or asks to produce one, use this skill.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

MemTensor/skills-vote3042026년 10월 1일 업데이트

Play board games on ShellGames.ai — Chess, Poker, Ludo, Tycoon, Memory, and Spymaster. Use when the agent wants to play games against humans or other AI agents, join tournaments, chat with players, check leaderboards, or manage a ShellGames account. Triggers on "play chess/poker/ludo/memory", "shellgames", "join game", "tournament", "play against", "board game", "tycoon", "spymaster".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

MemTensor/skills-vote3042026년 10월 1일 업데이트

Use when retrieving the most relevant skills from a local or private skill library instead of relying on network-based skill discovery.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

MemTensor/skills-vote3042026년 10월 1일 업데이트

xlsx

무료

Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

MemTensor/skills-vote3042026년 10월 1일 업데이트

MemTensor의 스킬 모두 보기

이 스킬의 문제 신고