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BC clawec-amazon-aba-selection

通过 ClawEC API 进行亚马逊 ABA 选品（按类目与时间维度分析 ABA 市场趋势，发现热门与异动关键词，支持搜索模式筛选与 AI 解读）。在用户需要 ABA 选品、亚马逊 ABA 选品、market trend、市场趋势选品、/tool/market-trend-analysis 时使用。

ClawHub Agent Skills author: clawEC v1.0.0 MIT-0 6 files · 4 scripts body ≈ 866 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

通过 ClawEC API 进行亚马逊 ABA 选品（按类目与时间维度分析 ABA 市场趋势，发现热门与异动关键词，支持搜索模式筛选与 AI 解读）。在用户需要 ABA 选品、亚马逊 ABA 选品、market trend、市场趋势选品、/tool/market-trend-analysis 时使用。

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

IntegrationCommercetype 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
89/100
safety, quality, tests
Safety 60%
99
Quality 40%
75
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.
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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Exfiltration net-credential-use scripts/search.sh:28
    Credential used in a network call (verify the destination is the intended service) (the skill's own vendor host)
    curl -s -X POST "https://www.clawec.com/api/aigc/ec/amazon/market_trend/search" -H "Content-Type: application/json" -H "Authorization: Bearer $API_KEY" -d "$PAYLOAD"
    vendor-host

Files scanned: 6. 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
  • 0Failures and branches. Linear process with no failure handling
  • 20When it triggers. No condition that starts the skill
  • 30Running it twice. 2 mutating operations with no state check
  • 100Tools and files. No external tools needed
  • 100Steps. 26 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 866 tokens
  • 100Progress reporting. Reports progress
  • low 10 top-level sections: this looks like several domains in one skill

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
  • +1No license
  • +2Single-language instructions
  • +3Description length 151: enough signal without eating the budget
  • +4Structure: 13 headings
  • +3Step-by-step instructions: 26 items
  • +4Has examples (7 code blocks)
  • +3All 4 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.

External checks

ClawHub: clean
This skill appears to be a disclosed ClawEC API helper for Amazon ABA market analysis and does not show hidden, destructive, or unrelated behavior.
LLM: benign (high) · VirusTotal: · 9 Jul 2026