SKILLEMALL.ai

BC asset-controlled-export-pipeline

把 Ardot 画布上的品牌 / 社媒设计资产批量导出为受控本地素材库,用于回答「素材外发怎么防盗用」「VI 资料怎么受控分发」「这张图是谁做的、什么时候做的」这类问题

ClawHub Hermes author: zhaoxinghua09-cell v1.0.0 MIT-0 6 files body ≈ 430 tokens Open the sourceclawhub.ai analyzed 3 d ago

把 Ardot 画布上的品牌 / 社媒设计资产批量导出为受控本地素材库,用于回答「素材外发怎么防盗用」「VI 资料怎么受控分发」「这张图是谁做的、什么时候做的」这类问题

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
JSON
Technical rating
B
82/100
safety, quality, tests
Safety 60%
100
Quality 40%
54
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: 6. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-long-hermes description is 84 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 "updated"
  • note frontmatter-key unknown frontmatter key "fingerprint"
  • note frontmatter-key unknown frontmatter key "governance"
  • note frontmatter-key unknown frontmatter key "languages"
  • note frontmatter-key unknown frontmatter key "aliases"
  • 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 430 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 84: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +4No input/output examples
  • -32 of 2 scripts are never mentioned in SKILL.md
  • +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: 54.

External checks

ClawHub: clean
This skill is a disclosed local asset-export and metadata-injection workflow, with no evidence of hidden exfiltration, credential access, persistence, or destructive behavior.
LLM: benign (high) · VirusTotal: · 31 Aug 2026