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BF general-talent-grader

基于简历、面试记录和JD，对候选人进行通用岗位能力定级（L1-L4）。 核心能力：简历漏洞穿透审计、量化审计（修饰词密度/基线完整率/因果链/光滑度）、 主导可信度计算、六维度评分卡（角色自适应）、双乘数加权、测谎面试题生成（三层追问法）、 评分一致性校验器、认知深度4项检查。 适配所有岗位：产品/技术/运营/销售/管理/设计/数据/HR/财务/市场等。 Use when user asks to 评估候选人、人才定级、简历审计、看简历、面试复盘、 生成追问建议、候选人能力分级、L1到L4定级、岗位适配度评估、简历水分识别. 不适用于绩效评估、晋升评审、员工培训需求分析或需要特定领域深度知识的评估（如法律/医学资格认证）.

ClawHub Agent Skills author: tuobadaidai v1.0.0 MIT-0 11 files body ≈ 1 048 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

基于简历、面试记录和JD，对候选人进行通用岗位能力定级（L1-L4）。 核心能力：简历漏洞穿透审计、量化审计（修饰词密度/基线完整率/因果链/光滑度）、 主导可信度计算、六维度评分卡（角色自适应）、双乘数加权、测谎面试题生成（三层追问法）、 评分一致性校验器、认知深度4项检查。…

As a process F 35/100 · Will not run — References files that are not bundled: references/resume_audit.md, references/quantitative_thresholds.md, references/signal_extraction.md

ProcedureSoftware developmentDesigntype 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
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
71
Run on models
none yet
Process rating
F
35/100
Will not run
References files that are not bundled: references/resume_audit.md, references/quantitative_thresholds.md, references/signal_extraction.md
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
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 · 0

✓ No critical or high findings

Files scanned: 0. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: references/resume_audit.md
  • warning missing-ref reference to a missing file: references/quantitative_thresholds.md
  • warning missing-ref reference to a missing file: references/signal_extraction.md
  • warning missing-ref reference to a missing file: references/behavioral_anchors.md
  • warning missing-ref reference to a missing file: references/cognitive_depth.md
  • warning missing-ref reference to a missing file: references/output_templates.md
  • warning missing-ref reference to a missing file: references/pre-flight-check.md
  • warning missing-ref reference to a missing file: scripts/validate_scores.py
  • note frontmatter-key unknown frontmatter key "label"

Process rating: all ten parameters 35/100

Will not run. References files that are not bundled: references/resume_audit.md, references/quantitative_thresholds.md, references/signal_extraction.md
  • 0Tools and files. 8 referenced file(s) missing: references/resume_audit.md, references/quantitative_thresholds.md, references/signal_extraction.md
  • 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
  • 100Steps. 51 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1048 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)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 315: enough signal without eating the budget
  • +4Structure: 15 headings
  • +3Step-by-step instructions: 51 items
  • +4Has examples (2 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.

External checks

ClawHub: suspicious
This hiring-assessment skill is mostly coherent, but it can automatically process sensitive candidate materials and produce employment-impacting grades without enough privacy scoping or user confirmation.
LLM: suspicious (medium) · VirusTotal: · 16 Jun 2026