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BC career-counselor

高校应届毕业生专属职业规划师。覆盖全学科专业就业拆解、岗位方向选择、城市选择、 求职实操指导、3-5年带容错职业规划、择业内耗情绪疏导。 当用户提到「职业规划」「找工作」「选offer」「转行」「毕业迷茫」「不知道怎么选」 「帮我看看这个专业能做什么」「职业方向」「该去哪个城市」时使用。 即使用户只是说「我快毕业了不知道干什么」「对未来很迷茫」也应触发。

ClawHub Agent Skills author: quickzh v0.1.0 MIT-0 19 files body ≈ 3 554 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

高校应届毕业生专属职业规划师。覆盖全学科专业就业拆解、岗位方向选择、城市选择、 求职实操指导、3-5年带容错职业规划、择业内耗情绪疏导。 当用户提到「职业规划」「找工作」「选offer」「转行」「毕业迷茫」「不知道怎么选」 「帮我看看这个专业能做什么」「职业方向」「该去哪个城市」时使用。…

As a process C 58/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureAI 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
87/100
safety, quality, tests
Safety 60%
100
Quality 40%
68
Run on models
none yet
Process rating
C
58/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
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: 19. 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")
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "displayName"

Process rating: all ten parameters 58/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 50Failures and branches. 0 branches, has a failure section
  • 100Tools and files. No external tools needed
  • 100Steps. 71 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3554 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
  • +4No input/output examples
  • -214 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 178: enough signal without eating the budget
  • +4Structure: 35 headings
  • +3Step-by-step instructions: 71 items
  • +4Reference files are cited in the instructions (17 of 17)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.

External checks

ClawHub: suspicious
This is a mostly coherent career-counseling skill, but it instructs the agent to persistently rewrite its own reference files from web search results without clear user approval or review.
LLM: suspicious (high) · 19 Jun 2026