SKILLEMALL.ai

BD publish-quality-gate

发布质量门禁:发布任何 Skill/专家包/文档/工具到外部(分享、上架市场、对外发布)前后的质量检查。发布前执行四层敏感信息检查(公司信息→本机信息→个人信息→机密信息),发布后按 TRACE 五维(Trust/Reliability/Adaptability/Convention/Effectiveness)执行 AI 自测。当用户说'准备发布'、'发布前检查'、'发布后自测'、'TRACE 评测'、'检查一下发布物'、'脱敏检查'、'发布质量'时使用。

ClawHub Hermes author: zhaoxinghua09-cell v1.0.2 MIT-0 7 files body ≈ 564 tokens Open the sourceclawhub.ai analyzed 3 d ago

发布质量门禁:发布任何 Skill/专家包/文档/工具到外部(分享、上架市场、对外发布)前后的质量检查。发布前执行四层敏感信息检查(公司信息→本机信息→个人信息→机密信息),发布后按 TRACE…

As a process D 43/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

ReferenceAI and agentsCustomer supporttype and topics are labelled automatically from the skill text
JSON
Technical rating
B
87/100
safety, quality, tests
Safety 60%
100
Quality 40%
67
Run on models
none yet
Process rating
D
43/100
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 231 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 "copyright"
  • note frontmatter-key unknown frontmatter key "agent_created"
  • note frontmatter-key unknown frontmatter key "homepage"

Process rating: all ten parameters 43/100

  • 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
  • 30Running it twice. 6 mutating operations with no state check
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 100Steps. 6 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 564 tokens

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
  • +2Single-language instructions
  • +3Description length 231: enough signal without eating the budget
  • +4Structure: 9 headings
  • +3Step-by-step instructions: 6 items
  • +4Has examples (4 code blocks)
  • +3All 1 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.

External checks

ClawHub: suspicious
The skill is a local release scanner with no exfiltration found, but its security gate can falsely report success on unscanned inputs and its install instructions use mutable remote commands.
LLM: suspicious (high) · 11 Sept 2026