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AC liepin-assistant

猎聘求职助手，封装 Liepin MCP 服务。用于搜索职位、查看 JD、投递简历、管理简历。 **触发场景**： (1) 用户提到"猎聘"、"liepin"、"liepin求职"、"猎聘求职"、"猎聘助手"、"liepin助手"、"找工作"、"搜职位"、"投简历"、"查看简历" (2) 用户提供猎聘 token 并要求配置 (3) 用户要搜索职位、查看 JD、投递岗位 **必须配置凭证**。凭证获取：https://www.liepin.com/mcp/server → 登录 → 生成凭证。设置环境变量 LIEPIN_TOKEN（推荐）或运行 set-token.js 将 token 写入 config.json。

ClawHub Agent Skills author: wang v1.0.11 MIT-0 5 files body ≈ 393 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

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

ProcedureAI and agentsInfrastructuretype 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
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
76
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 · 0

✓ No critical or high findings

Files scanned: 5. 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. 9 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 393 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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -41 reference files, but SKILL.md never points to them: the model will not open them
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 3 example trigger phrases
  • +3Description length 314: enough signal without eating the budget
  • +4Structure: 12 headings
  • +3Step-by-step instructions: 9 items
  • +4Has examples (8 code blocks)
  • +3All 2 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.

External checks

ClawHub: suspicious
This appears to be a real Liepin job-search helper, but it needs review because it handles a long-lived account token and can change resumes or submit applications.
LLM: suspicious (high) · VirusTotal: · 29 May 2026