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针对特定技术主题的单一公司技术画像与研发评估。当用户请求公司技术画像、公司技术分析、研发方向评估、技术尽职调查简报，或任何按公司加技术主题开展的单一公司技术评估时使用——即使用户仅提及公司名称与技术领域，未明确要求「画像」也适用。

ClawHub Claude Code author: yuanzhian-patsnap v1.0.1 MIT-0 18 files body ≈ 1 345 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

针对特定技术主题的单一公司技术画像与研发评估。当用户请求公司技术画像、公司技术分析、研发方向评估、技术尽职调查简报，或任何按公司加技术主题开展的单一公司技术评估时使用——即使用户仅提及公司名称与技术领域，未明确要求「画像」也适用。

As a process C 53/100 · 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
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
70
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: 18. 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 "copyright"
  • note frontmatter-key unknown frontmatter key "provider"
  • note frontmatter-key unknown frontmatter key "deliverable-default"
  • note frontmatter-key unknown frontmatter key "fallback-policy"

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. 107 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1345 tokens
  • 100Running it twice. No mutating operations
  • 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 115: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +4Structure: 26 headings
  • +3Step-by-step instructions: 107 items
  • +4Has examples (3 code blocks)
  • +4Reference files are cited in the instructions (7 of 7)

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

External checks

ClawHub: clean
This skill is a disclosed research workflow that writes local evidence files and uses search tools to build company technology reports, with only a scope caution around ambiguous activation.
LLM: benign (high) · VirusTotal: · 13 Aug 2026