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看相分析技能。通过用户上传的人体部位图片，识别图片类型（面相/手相/体相/骨相），调用相应的相术规则知识库，结合图片内容进行专业分析。支持面相（五官、三庭、十二宫）、手相（掌线、指形、掌丘）、骨相（头骨、体骨、九骨）、体相（体型、体态、气质）的综合分析。基于《麻衣神相》《柳庄相法》《神相全编》《水镜神相》《冰鉴》等经典相书整理。

ClawHub Agent Skills author: timerzz v1.0.0 MIT-0 8 files body ≈ 778 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

ProcedureInfrastructuretype 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
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
73
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: 8. 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 "trigger_keywords"
  • note frontmatter-key unknown frontmatter key "input_types"
  • note frontmatter-key unknown frontmatter key "examples"

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. 65 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 778 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 165: enough signal without eating the budget
  • +4Structure: 29 headings
  • +3Step-by-step instructions: 65 items
  • +4Has examples (8 code blocks)
  • +4Reference files are cited in the instructions (4 of 4)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.

External checks

ClawHub: suspicious
This is a markdown-only physiognomy photo-analysis skill, but it needs Review because it can analyze sensitive personal images through broad photo triggers and make health, personality, career, and fortune inferences without clear opt-in or privacy boundaries.
LLM: suspicious (high) · VirusTotal: · 29 May 2026