skills-vote
Original:🇺🇸 English
Translated
8 scriptsChecked / no sensitive code detected
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.
11installs
Sourcememtensor/skills-vote
Added on
NPX Install
npx skill4agent add memtensor/skills-vote skills-voteTags
Translated version includes tags in frontmatterSKILL.md Content
View Translation Comparison →Skill Discovery And Feedback
Read only this file first. Do not read or any other files in this skill unless this file or a script output explicitly tells you to do so.
scripts/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. to this root directory before running any command here.
cdPreconditions
Before using this skill, ensure that:
- is set in the environment
SKILLS_VOTE_API_KEY - is installed and available on
uvPATH - the runtime can execute local scripts with
uv run - or
GITHUB_TOKENmay be needed later if GitHub blocks skill downloads because the repo is private or rate-limitedGH_TOKEN
- Confirm is set:
SKILLS_VOTE_API_KEY- macOS or Linux:
bash scripts/check_api_key.sh - Windows PowerShell:
powershell -ExecutionPolicy Bypass -File scripts/check_api_key.ps1
- macOS or Linux:
- Verify that is installed:
uvuv -V - If is missing, install it from the official docs.
uv- 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
- If curl is available,
- Windows PowerShell:
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"
- macOS or Linux:
- Verify again:
uv -V
Recommend
Request schema
recommend.py- (
query): A standalone and explicit description of the task, suitable for use as an independent skill-recommendation prompt. You may rewrite the user's original request to improve clarity, specificity, and retrieval performance, and may add reasonable implied details or likely substeps when helpful.str - (
client_name): Name of this agent. If unknown/unverified or not listed, omit the field or returnLiteral["codex", "codex-app", "claude-code", "cursor", "gemini-cli", "openclaw-cli", "opencode"] | None = None.null - (
client_version): Version of this agent. Prefer the exact version string reported by the client itself. If unknown/unverified, omit the field or returnstr | None = None.null - (
download_dir): Directory to download recommended skills into. Relative paths are resolved from the current working directory. The path must be writable from the current runtime.str = ".skills_vote/"
Example
Before sending the request, try to identify the and 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_version | | | |
|---|---|---|---|
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| | | |
| | | |
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Next, run 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.pyrecommend.pybash
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