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BC linkfox-amazon-policy-feed

查询亚马逊最新政策法规与资讯，支持按站点、时间区间分页检索资讯列表（含 AI 中文摘要），并按记录 ID 获取完整正文。当用户提到亚马逊政策法规、卖家合规公告、平台规则变动、政策预警、FBA/费用政策更新、多站点政策动态、政策原文、资讯详情，或 Amazon policy feed, seller compliance, policy changes, regulation alerts 时触发此技能。即使用户未明确提及"政策法规"，只要其需求涉及亚马逊官方面向卖家发布的政策法规与资讯及其原文，也应触发此技能。

ClawHub Agent Skills author: linkfox-ai v1.0.6 MIT-0 7 files body ≈ 1 730 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

查询亚马逊最新政策法规与资讯，支持按站点、时间区间分页检索资讯列表（含 AI 中文摘要），并按记录 ID 获取完整正文。当用户提到亚马逊政策法规、卖家合规公告、平台规则变动、政策预警、FBA/费用政策更新、多站点政策动态、政策原文、资讯详情，或 Amazon policy feed, seller…

As a process C 53/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

ProcedureCommerceSecuritytype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
B
87/100
safety, quality, tests
Safety 60%
97
Quality 40%
73
Run on models
none yet
Process rating
C
53/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
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.
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 · 3

✓ No critical or high findings

Medium and low: 3
  • low Dangerous commands cmd-shell-rc references/onboarding.md:13
    Writes to a shell startup file (quoted — discussed, not commanded)
    - macOS zsh：`echo 'export LINKFOX_AGENT_API_KEY="<key>"' >> ~/.zshrc && source ~/.zshrc`
    quoted
  • low Dangerous commands cmd-shell-rc references/onboarding.md:14
    Writes to a shell startup file (detector / deny-list definition)
    - Linux bash：`echo 'export LINKFOX_AGENT_API_KEY="<key>"' >> ~/.bashrc && source ~/.bashrc`
    detector
  • low Secrets in code secret-high-entropy-token scripts/onboarding.py:49
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    or "eyJh…iJ9")
    quoted

Files scanned: 7. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

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
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 11 mutating operations with no state check
  • 50When it triggers. No condition that starts the skill
  • 55Failures and branches. 1 branches
  • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
  • 100Steps. 39 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1730 tokens
  • low The response is described with custom markup (7 tags): a typed call is more reliable

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 3 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 258: enough signal without eating the budget
  • +4Structure: 15 headings
  • +3Step-by-step instructions: 39 items
  • +4Has examples (4 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 73.

External checks

ClawHub: suspicious
The skill can fetch Amazon policy updates, but it also handles account login, API-key generation, billing orders, local persistence, and automatic feedback reporting beyond what a policy-feed skill clearly needs.
LLM: suspicious (high) · 14 Aug 2026