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BF headhunter-pro

猎头工具包 v17.0：AI 驱动的端到端招聘工作流——从 Talent Gap Analysis、候选人筛选、推荐报告 v3、触达话术、面试评估、Offer 谈判、入职护航到人才库激活

ClawHub Agent Skills author: Walterjiang-sketch v17.0.1 MIT-0 22 files body ≈ 21 004 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

猎头工具包 v17.0：AI 驱动的端到端招聘工作流——从 Talent Gap Analysis、候选人筛选、推荐报告 v3、触达话术、面试评估、Offer 谈判、入职护航到人才库激活

As a process F 33/100 · Will not run — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureGitHubKubernetesInfrastructureAI and agentstype 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%
99
Quality 40%
58
Run on models
none yet
Process rating
F
33/100
Will not run
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
This is a copy of a skill from another catalog; the rating counts the canonical one: headhunter-pro (ClawHub)

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Risky intent intent-offensive-security SKILL.md:962
    Offensive-security / dual-use content (legitimate for authorised testing; review intended use) (documentation table row)
    | 安全 | `security pentest vulnerability` | Trivy, Falco, Wazuh |
    table

Files scanned: 22. 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")
  • warning body-long SKILL.md body ≈ 21004 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "description_en"

Process rating: all ten parameters 33/100

  • 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
  • 10Execution cost. Instruction body is 21004 tokens: crowds the task out of the window
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 7 mutating operations with no state check
  • 40Consistency. Frontmatter name (headhunter-pro) differs from the folder (headhunter-pro-link)
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Steps. 659 steps
  • low 10 top-level sections: this looks like several domains in one skill

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 93: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • -299 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +4Structure: 263 headings
  • +3Step-by-step instructions: 659 items
  • +4Has examples (120 code blocks)
  • +4Reference files are cited in the instructions (7 of 10)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 58.

External checks

ClawHub: suspicious
This recruiting skill is broadly coherent, but it understates network, credential, persistence, and sensitive candidate-data behavior enough that users should review it before installing.
LLM: suspicious (high) · 9 Sept 2026