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BD linkfox-echotik-list-product

搜索和分析TikTok商品数据，包括销量、达人带货数据、定价和佣金比例，覆盖16个TikTok Shop站点。当用户提到TikTok商品搜索、TikTok Shop商品分析、TikTok销量数据、达人带货销售、TikTok选品、TikTok佣金比例、TikTok商品排名、EchoTik数据查询、TikTok product search, TikTok sales, influencer sales, TikTok commission, TikTok product selection, short-video e-commerce, TikTok data时触发此技能。即使用户未明确提及"EchoTik"或"TikTok"，只要其需求涉及在TikTok Shop上搜索商品或分析TikTok商品表现指标，也应触发此技能。

ClawHub Agent Skills author: linkfox-ai v1.0.9 MIT-0 6 files body ≈ 2 279 tokens Open the sourceclawhub.ai↗ analyzed 24 h ago

搜索和分析TikTok商品数据，包括销量、达人带货数据、定价和佣金比例，覆盖16个TikTok Shop站点。当用户提到TikTok商品搜索、TikTok Shop商品分析、TikTok销量数据、达人带货销售、TikTok选品、TikTok佣金比例、TikTok商品排名、EchoTik数据查询、TikTok…

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

ProcedureMarketingtype 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%
97
Quality 40%
78
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.
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: 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 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. 28 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2279 tokens
  • 100Running it twice. No mutating operations
  • 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

  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -31 of 2 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +5Description quotes 2 example trigger phrases
  • +3Description length 366: enough signal without eating the budget
  • +4Structure: 18 headings
  • +3Step-by-step instructions: 28 items
  • +4Has examples (6 code blocks)
  • +4Reference files are cited in the instructions (2 of 2)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.

External checks

ClawHub: suspicious
The skill mostly matches a paid TikTok product analytics use case, but it also handles credentials, onboarding, payments, persistent response storage, and automatic feedback reporting in ways users should review carefully.
LLM: suspicious (high) · 14 Aug 2026