skills-vote-local
Use when retrieving the most relevant skills from a local or private skill library instead of relying on network-based skill discovery.
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.
$ npx -y skills add MemTensor/skills-vote --skill skills-vote --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/skills-voteContext preview
The summary Claude sees to decide when to auto-load this skill.
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.
name: skills-vote description: 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. metadata: version: "1.1.0"
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:
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.
Before using this skill, ensure that:
1. Confirm `SKILLS_VOTE_API_KEY` is set:
2. Verify that `uv` is installed: `uv -V` 3. If `uv` is missing, install it from the [official docs](https://docs.astral.sh/uv/getting-started/installation/).
4. Verify again: `uv -V`
`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.
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_name` | `client_version` | `command` | `output` | | :-: | :-: | :-: | :-: | | `openclaw-cli` | `2026.3.24` | `openclaw -v` | `OpenClaw 2026.3.24 (cff6dc9)` | | `codex` | `0.117.0` | `codex -V` | `codex-cli 0.117.0` | | `codex-app` | `26.325.21221` | `N/A` | `N/A` | | `claude-code` | `2.1.85` | `claude -v` | `2.1.85 (Claude Code)` | | `cursor` | `2.6.13` | `cursor -v` | `2.6.13` | | `gemini-cli` | `0.35.1` | `gemini -v` | `0.35.1` | | `opencode` | `1.3.0` | `opencode -v` | `1.3.0` |
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/"
}
EOFSkillsVote: Lifecycle Governance of Agent Skills from Collection, Recommendation to Evolution
Repo: MemTensor/skills-vote
Use when retrieving the most relevant skills from a local or private skill library instead of relying on network-based skill discovery.