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BD lgd-badge-verify

lgd-badge-verify — 验徽章证书：指纹重算防篡改 + 证据哈希格式校验 + 台账对账（serial 存在且未吊销）。

ClawHub Hermes author: zhaoxinghua09-cell v1.0.0 MIT-0 7 files body ≈ 411 tokens Open the sourceclawhub.ai↗ analyzed 34 h ago

lgd-badge-verify — 验徽章证书：指纹重算防篡改 + 证据哈希格式校验 + 台账对账（serial 存在且未吊销）。

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Hermes Agent
JSON
Technical rating
B
83/100
safety, quality, tests
Safety 60%
100
Quality 40%
58
Run on models
none yet
Process rating
D
46/100
Unfinished process
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: 7. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-long-hermes description is 66 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 "slug"
  • note frontmatter-key unknown frontmatter key "display_name"
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "title"
  • note frontmatter-key unknown frontmatter key "summary"
  • note frontmatter-key unknown frontmatter key "display_name_en"
  • note frontmatter-key unknown frontmatter key "agent_created"
  • note frontmatter-key unknown frontmatter key "copyright"
  • note frontmatter-key unknown frontmatter key "homepage"

Process rating: all ten parameters 46/100

  • 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
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Steps. 7 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 411 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 66: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • -31 of 1 scripts are never mentioned in SKILL.md
  • +2Single-language instructions
  • +4Structure: 10 headings
  • +3Step-by-step instructions: 7 items
  • +4Has examples (1 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 58.

External checks

ClawHub: suspicious
This badge-verification skill is small and not visibly malicious, but its verifier can label self-consistent certificates as verified without authoritative registry, revocation, or evidence-hash validation.
LLM: suspicious (high) · 11 Sept 2026