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Agent Skill · 待分类

evaluating-candidates(评估候选人)

evaluating-candidates 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。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。

evaluating-candidates 是待分类类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为只读;许可证信息还不明确,第三方扫描已通过,按安装量在待分类类的 11,209 个 Skill 里排第 4,227。

总安装 783周安装 32GitHub Stars 3下载 251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:evaluating-candidates(评估候选人)
来源仓库:https://github.com/oldwinter/skills
仓库路径:skills/evaluating-candidates
安装命令:
npx skills add https://github.com/oldwinter/skills --skill evaluating-candidates
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/oldwinter/skills --skill evaluating-candidates

这个 Skill 是做什么的

这个 Skill 的分类还没最终确定,用法以来源仓库的说明为准。

evaluating-candidates 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息。适合在 Codex、Claude、Cursor、Gemini CLI 中围绕仓库状态、代码变更或协作事项进行整理。通过 npx skills add 命令从指定仓库安装并使用。

使用前需确认权限范围、维护状态,避免触发联网或文件读写操作。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/evaluating-candidates

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/oldwinter/skills --skill evaluating-candidates;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

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

evaluating-candidates 怎么安装?

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

evaluating-candidates 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。

evaluating-candidates 需要什么权限?

权限级别是只读。该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

evaluating-candidates 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。

evaluating-candidates 用的人多吗?

公开数据是累计安装 783 次,近一周安装 32 次,下载 251 次,GitHub 3 星。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。

evaluating-candidates 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

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。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 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。
  • 需要处理待分类类任务、又不想每次都手写提示词的人。
  • 只想先试试效果、不希望它改动本地文件的人。

不太适合

  • 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。

待分类类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
infsh-cli

infsh CLI · Codex、Claude

4,374,576382权限未标注未明确未收录
ai-video-generation

AI 视频生成 · Codex、Claude

4,313,376382调用外部服务未明确有提醒
lark-openapi-explorer

云雀 OpenAPI 浏览器 · Codex、Claude

2,064,1209,011需要联网未明确有提醒
lark-workflow-standup-report

Lark 工作流程站立报告 · Codex、Claude

2,041,6328,978需要联网未明确通过
use-my-browser

使用我的浏览器 · Codex、Claude

1,848,49140操作浏览器未明确未通过
evaluating-candidates

评估候选人 · 本页

7833只读未明确通过

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

Evaluating Candidates

Scope

Covers

  • Defining an explicit hiring bar (what “great” means for this role at this company, right now)
  • Turning interviews, work samples/trials, and references into evidence, not vibes
  • Designing job-relevant work samples (and paid trials when appropriate)
  • Running high-signal reference checks and integrating them into the decision
  • Producing a decision-ready recommendation with clear risks and mitigations

When to use

  • “Help me decide whether to hire this candidate.”
  • “Create a scorecard and decision memo based on interview notes + references.”
  • “Design a work sample / take-home (or paid trial) and a scoring rubric.”
  • “Plan and run reference checks; give me a summary and recommendation.”
  • “Calibrate our hiring bar for a and compare candidates fairly.”

When NOT to use

  • You need to define the role outcomes or write the job description (use writing-job-descriptions)
  • You need to design/run structured interviews and question maps (use conducting-interviews)
  • You need legal/HR compliance guidance or to adjudicate high-risk employment issues (this skill is not legal advice)
  • You need compensation/offer negotiation strategy

Inputs

Minimum required

  • Role + level + function (e.g., “Senior PM”, “Founding AE”, “Staff ML Engineer”)
  • Company/team context and “what’s hard” (stage, constraints, velocity expectations)
  • Evaluation criteria (4–8 competencies) and any non-negotiables / red flags
  • Candidate materials available (resume/portfolio + interview notes, if already interviewed)
  • Which signals you want to include: interviews, work sample/take-home, paid trial, references
  • Constraints: timeline, confidentiality/PII rules, internal-only vs shareable output

Missing-info strategy

  • Ask up to 5 questions from references/INTAKE.md (3–5 at a time).
  • If criteria or notes are missing, propose a default criteria set and clearly label assumptions.
  • Do not request secrets. If notes contain sensitive info, ask for redacted excerpts or summaries.

Outputs (deliverables)

Produce a Candidate Evaluation Decision Pack in Markdown (in-chat; or as files if requested):

  1. Evaluation brief (role success definition, criteria, weights, red flags)
  2. Scorecard (rating anchors + evidence capture)
  3. Signal log (all signals normalized into one table with evidence)
  4. Work sample / take-home / paid trial plan + rubric (if used)
  5. Reference check kit (outreach, script, note form, summary)
  6. Candidate comparison (if multiple candidates)
  7. Hiring decision memo (recommendation + risks + mitigations)
  8. Risks / Open questions / Next steps (always included)

Templates: references/TEMPLATES.md Expanded guidance: references/WORKFLOW.md

Workflow (7 steps)

1) Intake + decision framing

  • Inputs: user context; references/INTAKE.md.
  • Actions: Confirm role, level, must-haves, and the decision timeline. Identify which signals exist vs need to be created (work sample, trial, references). Record constraints (PII, internal-only, fairness).
  • Outputs: Context snapshot + assumptions/unknowns list.
  • Checks: The decision and decision date are explicit (who decides, by when, using which signals).

2) Define the bar + criteria (don’t improvise later)

  • Inputs: role context; existing rubric/values (if any).
  • Actions: Choose 4–8 criteria; define what “strong / acceptable / weak” looks like with observable anchors. Add explicit red flags. Decide whether to prioritize raw ability + drive vs “years of experience” for this role.
  • Outputs: Evaluation brief + draft scorecard.
  • Checks: Every criterion is measurable via evidence; no criterion is “vibe” or “culture fit” without definition.

3) Build the signal plan + evidence log

  • Inputs: existing notes; planned stages.
  • Actions: Decide what each signal is responsible for (interviews = behavioral evidence; work sample = in-context execution; references = longitudinal performance). Create a single signal log so you can compare apples-to-apples.
  • Outputs: Signal plan + signal log table (empty or partially filled).
  • Checks: No single signal dominates by default; reference checks and work samples have defined weight when used.

4) Design (or evaluate) the work sample / take-home / paid trial

  • Inputs: role outputs; constraints; candidate seniority.
  • Actions: Create a job-relevant task with clear deliverables and scoring rubric. If the task is >2–3 hours or resembles real work, prefer a paid trial and clarify IP/confidentiality boundaries.
  • Outputs: Work sample/trial brief + scoring rubric.
  • Checks: Task predicts real performance, is fair across backgrounds, and has objective scoring anchors.

5) Run reference checks (highest-signal when done well)

  • Inputs: reference targets; outreach constraints; question bank.
  • Actions: Prioritize references who worked with the candidate for extended periods and in similar contexts. Ask for specific examples, deltas over time, strengths/limits, and “how would you staff them?” Capture verbatim evidence and calibrate for bias.
  • Outputs: Reference notes + reference summary.
  • Checks: Summary contains concrete examples and clear hire/no-hire signal, not generic praise.

6) Synthesize signals → recommendation + risk mitigation

  • Inputs: scorecard, signal log, work sample results, reference summary.
  • Actions: Write a decision memo that cites evidence, calls out disagreements/uncertainty, and proposes mitigations (onboarding plan, coaching, 30/60/90 checkpoints) if hiring.
  • Outputs: Hiring decision memo + candidate comparison (if applicable).
  • Checks: Recommendation matches the weighted evidence; red flags are explicitly addressed.

7) Quality gate + calibration + finalize pack

  • Inputs: full draft pack.
  • Actions: Run references/CHECKLISTS.md and score with references/RUBRIC.md. Add Risks / Open questions / Next steps. If uncertain, propose the smallest additional signal to resolve (targeted reference, scoped trial, specific follow-up interview).
  • Outputs: Final Candidate Evaluation Decision Pack.
  • Checks: Evidence is sufficient for the decision; limitations and fairness risks are explicit.

Quality gate (required)

Examples

Example 1 (final decision): “Here are interview notes for a Senior PM candidate. Create a scorecard, summarize signals, and write a hiring decision memo. Include risks and suggested mitigations.” Expected: scorecard with anchors + evidence, signal log, decision memo with explicit risks.

Example 2 (work sample + references): “We’re hiring a Founding Engineer. Design a 2-day paid trial task and rubric, plus a reference check script. Then show how we should combine those signals into a hire/no-hire decision.” Expected: trial brief + rubric, reference kit, and a synthesis framework.

Boundary example: “Tell me if this person is good. I only have their resume.” Response: require criteria + at least one high-signal input (structured interview notes, work sample plan/results, or references); propose a minimal evaluation plan and list assumptions/unknowns.

平台分布

Codex

37.65%
按下载量换算95

Claude

31.52%
按下载量换算79

Cursor

18.22%
按下载量换算46

Gemini CLI

9.17%
按下载量换算23

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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。目前只有一个公开来源,建议同时核对源仓库。

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