SKILLEMALL.ai

BD linkfox-mercado-product-selection

Mercado Libre(美客多)选品数据查询与分析,通过 LinkFox 网关统一调用 24 个商品、官链、关键词、类目、趋势、店铺、评论、汇率与套餐用量工具,覆盖墨西哥、巴西、阿根廷、智利、哥伦比亚站点。当用户提到 Mercado Libre、美客多、美客多选品、商品搜索、类目趋势、关键词热搜、流量词反查、店铺查询、评论查询、汇率、套餐用量时触发此技能。

ClawHub Agent Skills author: linkfox-ai v1.0.3 MIT-0 7 files body ≈ 1 966 tokens Open the sourceclawhub.ai analyzed 2 d ago

Mercado Libre(美客多)选品数据查询与分析,通过 LinkFox 网关统一调用 24 个商品、官链、关键词、类目、趋势、店铺、评论、汇率与套餐用量工具,覆盖墨西哥、巴西、阿根廷、智利、哥伦比亚站点。当用户提到 Mercado…

As a process D 49/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches

ProcedureSoftware developmentData and analyticsWriting and documentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
87/100
safety, quality, tests
Safety 60%
97
Quality 40%
73
Run on models
none yet
Process rating
D
49/100
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: 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 49/100

  • 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
  • 30Running it twice. 1 mutating operations with no state check
  • 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
  • 70When it triggers. States when to use, but not when not to
  • 100Steps. 32 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1966 tokens
  • low 12 top-level sections: this looks like several domains in one skill
  • 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 2 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 181: enough signal without eating the budget
  • +4Structure: 14 headings
  • +3Step-by-step instructions: 32 items
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (3 of 3)

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

External checks

ClawHub: suspicious
The skill mostly matches a paid Mercado Libre analytics tool, but it also includes sensitive account onboarding, API-key provisioning, payment-order creation, and broader local persistence than advertised.
LLM: suspicious (high) · 18 Aug 2026