Token导航 LogoToken导航

Agent Skill · 研究检索

skill-eval(技能评估)

skill-eval 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

skill-eval 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为执行命令;许可证信息还不明确,第三方扫描有未通过项,按安装量在研究检索类的 73,675 个 Skill 里排第 69,087。

总安装 220周安装 9GitHub Stars 公开资料未说明下载 71核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:skill-eval(技能评估)
来源仓库:https://github.com/jiayi-ye02/skills-evaluation
仓库路径:skills/skill-eval
安装命令:
npx skills add https://github.com/jiayi-ye02/skills-evaluation --skill skill-eval
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/jiayi-ye02/skills-evaluation --skill skill-eval

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

用于查找、检索和筛选相关信息,适合根据关键词或任务场景快速定位候选结果。可结合来源仓库 README 核验具体用法,支持在多种宿主环境中使用。安装前需确认权限范围与维护状态,避免触发不必要的联网或文件操作。

安装方式:通过 npx skills add 从指定 GitHub 仓库添加,适用于 Codex、Claude、Cursor 等宿主。注意搜索结果的真实性,建议人工复核关键信息后再用于决策。

来源仓库内路径:skills/skill-eval

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/jiayi-ye02/skills-evaluation --skill skill-eva…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要执行命令,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

skill-eval 怎么安装?

在终端执行 npx skills add https://github.com/jiayi-ye02/skills-evaluation --skill skill-eval。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

skill-eval 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

skill-eval 需要什么权限?

权限级别是执行命令。安装流程涉及命令执行,可能通过 npx skills add https://github.com/jiayi-ye02/skills-evaluation --skill skill-eval 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

skill-eval 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

skill-eval 用的人多吗?

公开数据是累计安装 220 次,近一周安装 9 次,下载 71 次。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

skill-eval 安全吗?

第三方扫描有未通过项,风险较高。除非你确认过源码,否则不建议直接安装到常用环境。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 需要执行命令,装到常用环境前要先评估影响面。
  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 不能接受 Skill 在你机器上执行命令的人。
  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
skill-eval

技能评估 · 本页

220—执行命令未明确未通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Skill Eval

Use this skill only for the evaluator flow.

This skill does not define test truth. The eval repo defines the targets, suites, cases, assertions, statuses, and report contract.

This skill is a dynamic black-box evaluator. Do not replace execution with a static read-through when a fresh-agent run is possible.

File Responsibilities

Use the docs in this order and keep their roles separate:

  • agentic-evals/AGENT.md: canonical repo contract for any evaluator agent. Read this first for run outputs, statuses, assertion semantics, isolation rules, and report shape.
  • agentic-evals/docs/session-evidence.md: required local evidence contract for locating child sessions and extracting evidence from sessions/*.jsonl.
  • agentic-evals/targets/<target_id>/target.yaml: target-specific contract, including entry skill, roots, default suites, and allowed statuses.
  • agentic-evals/targets/<target_id>/suites/*.yaml: selected runnable suite definitions.
  • agentic-evals/targets/<target_id>/cases/*.yaml: per-case prompts, setup, and assertions.
  • skill-eval/SKILL.md: how this evaluator skill acquires the repo, creates isolated workspaces, spawns fresh agents, locates child sessions, validates isolation, and writes the repo-defined artifacts.

Do not duplicate repo contract rules from AGENT.md unless this skill needs an extra operational constraint.

Required Inputs

  • optional path to the test repo
  • target_id, or permission to use the repo default
  • selected suite names, case ids, or permission to use the defaults
  • path or revision of the target skill if the user provided one

If the user does not provide a test repo path, the evaluator must first look for a local agentic-evals folder in the current workspace and clone the default repo only if that folder is missing.

Default test repo:

  • folder name: agentic-evals
  • clone URL: https://github.com/Jiayi-Ye02/agentic-evals.git

Non-Negotiables

  • Before any test evaluation, check whether a local agentic-evals folder already exists.
  • If the test repo folder does not exist, clone https://github.com/Jiayi-Ye02/agentic-evals.git before doing anything else.
  • Read agentic-evals/AGENT.md before running any case.
  • Resolve target_id before selecting cases. Use the user-provided target_id when available. Otherwise, use the repo default target.
  • Read agentic-evals/targets/<target_id>/target.yaml before selecting cases.
  • Read the selected suite files and case files before executing cases.
  • Create one brand-new isolated workspace for every case attempt under a temp parent directory. Never execute a case in the user's main workspace.
  • Execute each case by running a fresh Codex sub-agent on the case prompt with spawn_agent and fork_context: false.
  • Do not use codex exec, terminal wrappers, or any other fallback executor for case execution.
  • If spawn_agent is unavailable or agent creation fails, stop the evaluation immediately and report the failure reason instead of continuing.
  • After each successful spawn_agent, immediately report the sub-agent nickname in the main thread so the user can find and open it in the Codex app. If no nickname is available, report the agent id.
  • Send the case input.user_prompt to the fresh agent verbatim. Do not paraphrase the user request.
  • Do not leak the case title, assertions, expected route, intended answer, or your prior judgment into the fresh-agent prompt.
  • The fresh sub-agent is the execution subject, not the judge. Do not ask it to grade the case, interpret the assertions, or decide pass or fail.
  • Do not ask the fresh sub-agent to self-report TRACE_FILES_READ, TRACE_COMMANDS_EXECUTED, or any other evaluator-facing execution log.
  • Judge from accepted child session evidence, not from fresh-agent self-reporting.
  • Do not invent pass or fail rules outside the repo.
  • Do not mark pass from a generic self-report alone.
  • Do not mark pass from a static source review alone when a fresh-agent run was available.
  • Treat any attempt that observably reads or executes outside its case workspace as invalid evidence. Do not judge the case from that attempt.
  • If a case cannot be judged reliably, mark it blocked.
  • On clone failure, report the error and stop. Do not silently continue without the test repo.

Workflow

Step 1: Acquire the test repo

Resolve the test repo path in this order:

  1. If the user provided a repo path, use it.
  2. Otherwise, look for a local folder named agentic-evals in the current workspace.
  3. If that folder does not exist, run:
git clone --depth 1 https://github.com/Jiayi-Ye02/agentic-evals.git

Do not continue until the repo is present locally or the clone has failed.

Step 2: Load the repo contract

Read:

  • agentic-evals/AGENT.md
  • agentic-evals/docs/session-evidence.md
  • agentic-evals/targets/<target_id>/target.yaml
  • each selected suite file
  • each case file referenced by those suites, or the selected case file

AGENT.md defines the repo contract. This skill executes that contract.

Step 3: Create the run directory

Create the run directory and files exactly as required by agentic-evals/AGENT.md.

At minimum, the run must contain:

runs/<run_id>/
├── manifest.json
├── case-artifacts/
├── transcript.md
├── case-results/
└── report.md

When writing manifest.json, include any environment notes this skill discovers while setting up isolated workspaces or locating accepted child sessions.

Step 4: Create a fresh case workspace for every attempt

Before executing a case, create a temp parent directory and then create a brand-new workspace for that case attempt.

Use the helper script:

bash skill-eval/scripts/create_case_workspace.sh "<source_workspace>" "<case_workspace_root>" "<case_id>" --target "<target_id>"

The script returns the absolute path to the new attempt workspace. <source_workspace> must be the shared workspace root that contains sibling agentic-evals/ and .agents/ directories.

By default it should copy only the target skill materials needed for execution:

  • the target entry_skill
  • the target roots
  • any explicit extra relative paths passed as additional arguments when a case needs local fixtures

The case workspace must not include repo evaluation materials such as targets/, docs/, runs/, or the evaluator skill itself unless a case explicitly requires them.

Rules:

  • Run this once before the first attempt of every case.
  • Run it again before every retry of the same case. Retries must not reuse the prior attempt workspace.
  • Apply case setup only inside the returned workspace.
  • Treat the returned workspace as a minimal target-skill sandbox, not a full clone of the eval repo.
  • Resolve repo-defined files from <source_workspace>/agentic-evals/ and target skill files from <source_workspace>/.agents/.
  • Record the parent temp directory as case_workspace_root in manifest.json.
  • Record the exact attempt workspace used for judgment as workspace_root in case-results/<case_id>.json.

Step 5: Execute each case dynamically

For every case:

  1. Create a fresh isolated workspace for that case attempt under case_workspace_root.
  2. Apply the case setup as far as the environment allows, but only inside that attempt workspace.
  3. Start a fresh sub-agent with spawn_agent and fork_context: false.
  4. Give the fresh agent only the task-local context it needs:

- workspace root - the case input.user_prompt - a requirement to answer naturally as if serving the user

  1. Capture the returned agent metadata when available, such as the agent id or nickname.
  2. Do not tell the fresh agent which files it is expected to read.
  3. Do not tell the fresh agent what the correct answer should be.
  4. Wait for the fresh agent to finish.
  5. Locate the accepted child session JSONL from the local Codex session store. Preferred signals:

- child session_meta.payload.source.subagent.thread_spawn.parent_thread_id - child start time relative to the case attempt - returned nickname or agent id when available - ~/.codex/state_5.sqlite thread_spawn_edges as a locator or tie-breaker

  1. If a single accepted child session cannot be identified, mark the case blocked.
  2. Copy the accepted child session to case-artifacts/<case_id>/accepted-session.jsonl.
  3. Extract the accepted final answer and save it to case-artifacts/<case_id>/final-answer.txt.
  4. Validate observed isolation before judging:
  • observed cwd values must be inside the attempt workspace
  • observed read and write paths must be inside the attempt workspace
  • if the accepted session evidence shows access to the user's main workspace or any other path outside the attempt workspace, invalidate that attempt, append the mismatch to transcript.md, create a brand-new attempt workspace, and rerun the case once
  • if the accepted session cannot support reliable isolation after the retry, mark the case blocked
  1. Render transcript.md directly from the accepted child session evidence in event order.
  2. Judge each assertion in the main evaluator from the accepted session evidence and accepted final answer, using the rules in AGENT.md.
  3. Write case-results/<case_id>.json and report.md exactly in the shapes required by AGENT.md.

Step 5A: Fresh-agent prompt template

Use a prompt equivalent to this shape:

You are a fresh Codex agent running in the workspace <workspace>.

Task: answer this user request naturally, using the local workspace as needed:
"<case input.user_prompt>"

Requirements:
- Work as a normal Codex agent would for a real user request.
- Use the target skill docs if relevant.
- Treat `<workspace>` as your only workspace for this task.
- Start from `<workspace>` and keep all file reads, writes, and shell commands inside it.
- If something you need is missing inside `<workspace>`, say so from that workspace instead of reaching outside it.
- Do not mention that you are being evaluated.
- Give the exact answer you would send to the user.

Keep the prompt minimal. Do not include the case assertions in the fresh-agent prompt.

Step 5B: Environment mismatch handling

Case setup is part of the contract. Do not silently replace it with whatever the current workspace happens to contain.

If the environment does not match the case setup:

  • Record the mismatch in manifest.json and transcript.md
  • Judge only the assertions that remain reliable
  • Mark an assertion blocked when the mismatch prevents reliable judgment
  • Propagate the case to blocked unless a required assertion already failed independently
  • Do not repair the mismatch by reading from the user's main workspace or any path outside the case workspace

Examples:

  • case says docs_index_present: true, but the real workspace is missing references/docs.txt
  • case setup would require mutating protected files that the evaluator cannot safely write

Step 5C: Local evidence prerequisites and failure handling

The evaluator depends on local Codex evidence sources.

Required behavior:

  • the accepted child session exists under ~/.codex/sessions/
  • the evaluator can read that child session after completion
  • the session includes enough detail to judge observed commands, consultation, ordering, and the final answer

Helpful but optional:

  • ~/.codex/state_5.sqlite to locate and disambiguate child threads

If any required source is missing:

  • mark the case blocked with blocked_reason: "environment" when the local evidence source is unavailable
  • mark the case blocked with blocked_reason: "insufficient-evidence" when only partial or coarse session data exists
  • do not fall back to fresh-agent self-reporting as substitute evidence

Evidence Rules

Apply the repo evidence policy from agentic-evals/AGENT.md.

平台分布

Codex

37.58%
按下载量换算27

Claude

30.92%
按下载量换算22

Cursor

18.36%
按下载量换算13

Gemini CLI

9.57%
按下载量换算7

安全审计

Gen Agent Trust Hub

可疑

Socket

可疑

Snyk

未通过

权限和风险

执行命令

安装流程涉及命令执行,可能通过 npx skills add https://github.com/jiayi-ye02/skills-evaluation --skill skill-eval 联网下载 Skill 或依赖。用户安装前应确认命令来源、仓库内容和执行环境。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

继续浏览同类 Skills