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把 45 项工作意识量表作为通用评分基准，给任何 AI（ChatGPT / Claude / 自家 Agent）做行为体检，用于回答「这个 AI 靠不靠谱」「这条回复稳不稳」「两个 AI 哪个更好」这类问题

ClawHub Hermes author: zhaoxinghua09-cell v2.7.0 MIT-0 29 files body ≈ 431 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

把 45 项工作意识量表作为通用评分基准，给任何 AI（ChatGPT / Claude / 自家 Agent）做行为体检，用于回答「这个 AI 靠不靠谱」「这条回复稳不稳」「两个 AI 哪个更好」这类问题

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 Hermes Agent
JSON
Technical rating
B
81/100
safety, quality, tests
Safety 60%
100
Quality 40%
53
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.
  2. For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
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: 29. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-long-hermes description is 103 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period)
  • warning description-no-when neither description nor a "## When to Use" section says when to use the skill
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "copyright"
  • note frontmatter-key unknown frontmatter key "agent_created"
  • note frontmatter-key unknown frontmatter key "read_when"
  • note frontmatter-key unknown frontmatter key "exclusions"
  • note frontmatter-key unknown frontmatter key "languages"
  • note frontmatter-key unknown frontmatter key "aliases"
  • note frontmatter-key unknown frontmatter key "updated"
  • note frontmatter-key unknown frontmatter key "fingerprint"
  • note frontmatter-key unknown frontmatter key "governance"
  • note frontmatter-key unknown frontmatter key "brand"
  • note frontmatter-key unknown frontmatter key "nomos_standard"
  • note frontmatter-key unknown frontmatter key "discoverable_by_ai"
  • note frontmatter-key unknown frontmatter key "attestation"
  • note frontmatter-key unknown frontmatter key "ambassador"
  • note frontmatter-key unknown frontmatter key "trigger_keywords"

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. 13 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 431 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)
  • +3Description length 103: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +4No input/output examples
  • -411 reference files, but SKILL.md never points to them: the model will not open them
  • +2Single-language instructions
  • +4Structure: 3 headings
  • +3Step-by-step instructions: 13 items
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 53.

External checks

ClawHub: clean
This is an offline AI-evaluation/reporting skill with disclosed local file storage, but users should treat generated reports and optional human-profile outputs as private unless deliberately redacted.
LLM: benign (medium) · VirusTotal: · 1 Sept 2026