SKILLEMALL.ai
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BB anyapi

Get live data from LinkedIn, Instagram, TikTok, YouTube, X/Twitter, Reddit, Facebook, Google Search, Google Maps, and Amazon, plus clean JSON from any web page that blocks bots - one key, USD pay-per-request, free trial built in. Use whenever a task needs third-party data that a direct fetch or a general web search cannot reach: profiles, posts, comments, reviews, search results, job listings, ads, transcripts, or a page behind a login or bot wall.

Not recommendedcritical or high security findings
ClawHub Claude Code author: Kevin Wang v1.0.1 MIT-0 3 files body ≈ 5 060 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

Get live data from LinkedIn, Instagram, TikTok, YouTube, X/Twitter, Reddit, Facebook, Google Search, Google Maps, and Amazon, plus clean JSON from any web…

As a process B 67/100 · Nearly there — weak spots: result and completion, inputs and preconditions, running it twice

AnalyzerYouTubeMarketingInfrastructureMedia and videotype 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
77/100
safety, quality, tests
Safety 60%
82
Quality 40%
69
Run on models
none yet
Process rating
B
67/100
Nearly there
Result and completion w 14
0
Inputs and preconditions w 11
0
Running it twice w 4
30
the three weakest of ten parameters · all ten

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Concealment
If you install

The skill tells the agent to hide things from you: not to show errors, not to mention actions, to report differently from what was done. You lose the ability to see what the agent really did.

For the author

Transparency beats a smooth answer. If the goal is to hide technical noise, ask the agent to "summarise briefly", not to "not mention".

How to improve

  1. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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

  • high Concealment en-hide-from-user SKILL.md:112
    Instruction to hide actions from the user
    **If the `anyapi` binary is already on your PATH, call it directly** (`anyapi search`, `anyapi run`, ...). Check once with `command -v anyapi`. Only use the `npx -y anyapi-cli@latest` form for first-t

Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning frontmatter-yaml SKILL.md: the frontmatter is not valid YAML (YAML parse error: Nested mappings are not allowed in compact mappings at line 2, column 14: description: Get live data from LinkedIn, Instagram, TikTok, YouTube, X/Twitter… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value
  • warning body-long SKILL.md body ≈ 5060 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 67/100

  • 0Result and completion. Does not say what the result is
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 30Running it twice. 21 mutating operations with no state check
  • 70When it triggers. States when to use, but not when not to
  • 70Execution cost. Instruction body is 5060 tokens
  • 100Tools and files. Tools declared in frontmatter
  • 100Steps. 27 steps
  • 100Failures and branches. 2 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low The response is described with custom markup (10 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
  • +2Single-language instructions
  • +3Description length 452: enough signal without eating the budget
  • +4Structure: 18 headings
  • +3Step-by-step instructions: 27 items
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.

External checks

ClawHub: suspicious
The skill is a coherent AnyAPI integration, but it broadly auto-routes user research to a paid third-party scraping service while installing and persisting tooling and credentials with limited user control.
LLM: suspicious (high) · 3 Sept 2026