SKILLEMALL.ai
GitHub ↗ RU Sign in

BF ecomseer

TikTok Shop e-commerce data assistant. Search products, find trending items, analyze influencers, explore shops, track video performance, and get ad insights via ecomseer.com. Triggers: 找商品, 搜商品, 爆品, 带货, TikTok电商, 达人分析, 视频带货, 店铺分析, 广告素材, 销量榜, 跨境电商, search products, find trending, TikTok Shop, influencer analysis, shop data, ad creatives, sales ranking, e-commerce analytics, product research.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 10 files body ≈ 3 987 tokens Open the sourcegithub.com↗ analyzed 2 d ago

TikTok Shop e-commerce data assistant.

As a process F 52/100 · Will not run — References files that are not bundled: {url}, url

AnalyzerMarketingtype 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%
99
Quality 40%
68
Run on models
none yet
Process rating
F
52/100
Will not run
References files that are not bundled: {url}, url
Tools and files w 18
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.
  2. The text references files that are not there: add them or drop the references.
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 SKILL.md:127
    Credential used in a network call (verify the destination is the intended service) (the skill's own vendor host)
    curl -s -o /dev/null -w "%{http_code}" "https://www.ecomseer.com/api/open/goods/filters?region=US" -H "X-API-Key: $ECOMSEER_API_KEY"
    vendor-host

Files scanned: 10. 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")
  • warning missing-ref reference to a missing file: {url}
  • warning missing-ref reference to a missing file: url

Process rating: all ten parameters 52/100

Will not run. References files that are not bundled: {url}, url
  • 0Tools and files. 2 referenced file(s) missing: {url}, url
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 3 mutating operations with no state check
  • 50When it triggers. No condition that starts the skill
  • 60Result and completion. Output format stated, no completion criterion
  • 85Steps. 75 steps, 2 vague phrases
  • 100Failures and branches. 3 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3987 tokens
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

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)
  • -212 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 394: enough signal without eating the budget
  • +4Structure: 23 headings
  • +3Step-by-step instructions: 75 items
  • +3Output format is stated explicitly
  • +4Has examples (10 code blocks)
  • +4Reference files are cited in the instructions (6 of 6)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.