SKILLEMALL.ai

BD ai-policy-radar

当用户说『最近AI出了什么新规』『EU AI Act/脆监会/NMPA有没有新动作』『法规更新我得跟上』『帮我盯AI政策』,或要持续跟踪 AI 监管动态(EU AI Act / 中国 NMPA / 美国 FDA / GDPR 等)时使用。扫描法规库/更新日志,按主题(风险分级/透明度/数据/准入)归类变动并留痕(有证),输出「本月新增了什么、对你有何影响」。可运行脚本(policy_radar 扫描器)。理论根基:LGD 三律之有证(法规变动留痕可溯)。与 eu-ai-act-companion 互补(它管单法导航,本技能管跨法动态监测)。触发词:AI法规、政策雷达、监管动态、EU AI Act更新、合规追踪、policy radar、法规监测。

ClawHub Hermes author: zhaoxinghua09-cell v1.0.0 MIT-0 7 files body ≈ 476 tokens Open the sourceclawhub.ai analyzed 2 d ago

当用户说『最近AI出了什么新规』『EU AI Act/脆监会/NMPA有没有新动作』『法规更新我得跟上』『帮我盯AI政策』,或要持续跟踪 AI 监管动态(EU AI Act / 中国 NMPA / 美国 FDA / GDPR…

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

ProcedureAI and agentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
84/100
safety, quality, tests
Safety 60%
100
Quality 40%
61
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 327 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 "display_name_en"
  • note frontmatter-key unknown frontmatter key "summary"
  • note frontmatter-key unknown frontmatter key "agent_created"
  • note frontmatter-key unknown frontmatter key "copyright"
  • note frontmatter-key unknown frontmatter key "read_when"
  • 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. 15 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 476 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
  • -31 of 1 scripts are never mentioned in SKILL.md
  • +2Single-language instructions
  • +3Description length 327: enough signal without eating the budget
  • +4Structure: 10 headings
  • +3Step-by-step instructions: 15 items
  • +4Has examples (1 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 61.

External checks

ClawHub: suspicious
The skill itself is a local Markdown policy scanner, but its install instructions use unpinned global installation from mutable external sources.
LLM: suspicious (medium) · 11 Sept 2026