运维安装
- 作者仓库星标 255
- 作者更新于 实时读取
- 作者仓库 skills-vote
- 领域
- AI 智能
- 兼容 Agent
-
- Claude Code
- Cursor
- Cline
- Codex
- Windsurf
- Gemini CLI
- +20
- 信任分
- 83 / 100 · 社区维护
- 作者 / 版本 / 许可
- @MemTensor · 未声明 license
- Token 消耗评级
- 低消耗
- 接入复杂程度
- 需简单配置
- 是否需要外部 API Key
- 需要 · GitHub
- 兼容的系统
- macOS · Linux · Windows
- 底层运行要求
- 无特殊要求
- 文件与系统权限
-
- 只读
- 允许写入 / 修改
- Shell 执行
- 网络行为
- 允许外网请求
- 安装命令数
- 26 条
档案由构建时根据 SKILL.md 与安装命令自动衍生,可能与作者实际意图存在差异。
需要注意: 未限定 allowed-tools,默认拥有全部工具权限。;检出高风险片段:pipe_curl_to_shell
---
name: skills-vote
description: Find the most relevant external agent skills for the current task, then submit grounded feedback…
category: AI 智能
runtime: 无特殊运行时
---
# skills-vote 输出预览
## PART A: 任务判断
- 适用问题:提示词、Agent 工作流、模型评估或自动化推理。
- 输入要求:目标材料、限制条件、期望输出和验收方式。
- 证据边界:围绕“Preconditions / Recommend / Request schema”读取原文规则,不把推断写成作者承诺。
## PART B: 执行结果
- **01** 任务判断:确认你的需求是否属于提示词、Agent 工作流、模型评估或自动化推理,并标出输入、限制和预期结果。
- **02** 执行计划:优先按“Preconditions / Recommend / Request schema”拆成步骤,说明每一步会读取什么、修改什么、产出什么。
- **03** 交付结果:给出可复制的命令、文件改动、检查清单或内容草稿,并说明如何继续迭代。
- **04** 风险边界:结合 读取文件、写入/修改文件、执行终端命令、会按任务需要访问外部网络、需要准备 GitHub API Key 给出执行前确认项。
## Running Rules
- 读取文件、写入/修改文件、执行终端命令;会按任务需要访问外部网络;需要准备 GitHub API Key。
- 先小样例验证,再放大到真实任务。
- 交付时同时给结果、检查口径和下一步迭代建议。 原文没有稳定的斜杠命令要求。安装验证后通常全局生效,直接在对话里点名这个 Skill 并描述任务即可。
告诉 Agent 目标文件或材料、期望结果、不可改范围、是否允许联网或执行命令。本 Skill 的权限画像是:读取文件、写入/修改文件、执行终端命令。
先用一个小任务确认它会围绕“Preconditions / Recommend / Request schema”工作;涉及文件或命令时,先看 diff、日志、预览或测试结果。
检查最终产物是否包含明确结果、必要证据和下一步动作;如果输出泛泛而谈,就补充输入、边界和验收标准后重跑。
---
name: skills-vote
description: Find the most relevant external agent skills for the current task, then submit grounded feedback…
category: AI 智能
source: MemTensor/skills-vote
---
# skills-vote
## 什么时候使用
- 把 AI / Agent方向的常用动作沉淀成 Agent 可调用的技能 适合处理AI Agent、提示词、模型评估与自动化推理,核心价值是把输入、判断、执行、验证和交付边界固定下来,避免 Agent 泛泛回答。 把任务拆成可执行、可检查…
- 面向提示词、Agent 工作流、模型评估或自动化推理,优先处理能明确输入、步骤和验收标准的工作。
## 需要提供什么
- 目标材料、目录范围、期望结果和不可改动内容。
- 是否允许联网、执行命令、读写文件或调用外部服务。
## 执行规则
- 围绕「Preconditions / Recommend / Request schema」组织步骤,不把推断写成作者事实。
- 读取文件、写入/修改文件、执行终端命令;会按任务需要访问外部网络;需要准备 GitHub API Key。
- 先跑小样例,确认结果可检查后再扩大任务范围。
## 输出要求
- 给出最终产物、关键证据、验证方式和下一步动作。
- 信息不足时标记 unknown,不编造命令、平台或依赖。 作者原文负责流程事实;仓库文件负责来源和命令;流狐只补充适用场景、限制和质量判断。
skill "skills-vote" {
输入层 -> 用户目标 + 目标文件 + 禁止范围 + 验收标准
上下文层 -> Preconditions / Recommend / Request schema
规则层 -> SKILL.md 触发条件 / 执行顺序 / 输出格式
运行层 -> 无特殊运行时 | 读取文件、写入/修改文件、执行终端命令 | 会按任务需要访问外部网络
安全层 -> 需要准备 GitHub API Key + 小任务验证 + diff / 日志复核
输出层 -> 可复制结果 + 检查清单 + 下一步迭代
} 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_KEYis set in the environmentuvis installed and available onPATH- the runtime can execute local scripts with
uv run GITHUB_TOKENorGH_TOKENmay be needed later if GitHub blocks skill downloads because the repo is private or rate-limited
- Confirm
SKILLS_VOTE_API_KEYis set:- 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
uvis installed:uv -V - If
uvis 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
- 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 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 returnnull.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 returnnull.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_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/"
}
EOF
先判断是否适合
作者设计意图
作者的方法与取舍
边界和复核