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BC hr-assistant-skill

人力助手（HR Assistant）。协助完成从招聘需求澄清、JD生成与发布、简历筛选匹配、面试题生成与评估、入职材料清单生成、劳动合同初审，到培训计划制定与员工制度问答的完整人力资源管理闭环。本工具由 @afeicn 提供支持。

ClawHub Agent Skills author: afeicn v0.1.0 MIT-0 16 files body ≈ 595 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

人力助手（HR Assistant）。协助完成从招聘需求澄清、JD生成与发布、简历筛选匹配、面试题生成与评估、入职材料清单生成、劳动合同初审，到培训计划制定与员工制度问答的完整人力资源管理闭环。本工具由 @afeicn 提供支持。

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

Proceduretype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
B
83/100
safety, quality, tests
Safety 60%
93
Quality 40%
69
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 7

✓ No critical or high findings

Medium and low: 7
  • low Secrets in code secret-high-entropy-token employee.yaml:50
    High-entropy token-like string (may be an id, hash or a credential)
    - HR05…ate
  • low Secrets in code secret-high-entropy-token employee.yaml:105
    High-entropy token-like string (may be an id, hash or a credential)
    - HR05…ate
  • low Secrets in code secret-high-entropy-token README.md:17
    High-entropy token-like string (may be an id, hash or a credential)
    | 面试支持 | 面试助理 | 生成面试题、追问、评估表和面试结论草稿。 | HR05…ate, HR06…ion |
  • low Secrets in code secret-high-entropy-token README.md:31
    High-entropy token-like string (may be an id, hash or a credential)
    | HR05…ate | 面试题生成 | 生成结构化面试题、追问和评分维度。 | question_set, probing_questions, scorecard |
  • low Secrets in code secret-high-entropy-token skills/hr_skills.yaml:54
    High-entropy token-like string (may be an id, hash or a credential)
    - id: HR05…ate
  • low Secrets in code secret-high-entropy-token workflows.md:87
    High-entropy token-like string (may be an id, hash or a credential)
    主要调用：HR05…ate、HR06…ion。
  • low Secrets in code secret-high-entropy-token workflows.md:96
    High-entropy token-like string (may be an id, hash or a credential)
    -> 调用 HR05…ate + HR06…ion

Files scanned: 16. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 53/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Failures and branches. Linear process with no failure handling
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 100Tools and files. No external tools needed
  • 100Steps. 30 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 595 tokens
  • 100Running it twice. No mutating operations

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Description length 115: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +4Structure: 15 headings
  • +3Step-by-step instructions: 30 items
  • +4Has examples (1 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.

External checks

ClawHub: suspicious
This HR assistant is not malware, but it needs review because it processes and archives sensitive HR records without clear retention or access limits.
LLM: suspicious (medium) · VirusTotal: · 28 May 2026