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全渠道选品 Agent — 拉齐社媒端、SEO端、投放端数据，帮助运营同学确定待上线需求。触发词：选品、社媒热点、SEO调研、竞品广告、Facebook Ads、TikTok趋势。

ClawHub Agent Skills author: lygjoey v1.0.0 MIT-0 30 files body ≈ 966 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

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

ProcedureMarketingAI and agentstype 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
C
64/100
safety, quality, tests
Safety 60%
60
Quality 40%
69
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

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Obfuscation medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The files contain invisible characters, encoded commands or comments hidden from readers but visible to the model. What you read differs from what the agent sees.

For the author

Remove invisible characters (they usually sneak in through copy-paste) and encoded strings: no catalog will pass them. Instructions for the model must be readable by a human too.

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 · 8

✓ No critical or high findings

Medium and low: 8
  • medium Obfuscation uni-mixed-script-word output/all_data_20260305_0313.json:4180
    Word mixing Latin and Cyrillic letters (homoglyph obfuscation) (6 occurrences)
    "music": "машуляmm",
  • medium Obfuscation uni-mixed-script-word output/all_data_20260305_0314.json:4180
    Word mixing Latin and Cyrillic letters (homoglyph obfuscation) (5 occurrences)
    "music": "машуляmm",
  • medium Obfuscation uni-mixed-script-word output/full_report_20260305_0255.txt:158
    Word mixing Latin and Cyrillic letters (homoglyph obfuscation) (1 occurrence)
    📈 中等趋势          2       46.6万  машуляmm
  • medium Obfuscation uni-mixed-script-word output/full_report_20260305_0313.txt:29
    Word mixing Latin and Cyrillic letters (homoglyph obfuscation) (2 occurrences)
    1   Ultra-Realistic AI Influencer dancing          90.5万  Everуpixel Workroo
  • medium Obfuscation uni-mixed-script-word output/full_report_20260305_0314.txt:148
    Word mixing Latin and Cyrillic letters (homoglyph obfuscation) (1 occurrence)
    📈 中等趋势          2       46.6万  машуляmm
  • medium Obfuscation uni-mixed-script-word output/social_20260305_0255.json:4179
    Word mixing Latin and Cyrillic letters (homoglyph obfuscation) (5 occurrences)
    "music": "машуляmm",
  • medium Obfuscation uni-mixed-script-word output/social_20260305_0313.json:4179
    Word mixing Latin and Cyrillic letters (homoglyph obfuscation) (6 occurrences)
    "music": "машуляmm",
  • medium Obfuscation uni-mixed-script-word output/social_20260305_0314.json:4179
    Word mixing Latin and Cyrillic letters (homoglyph obfuscation) (5 occurrences)
    "music": "машуляmm",

Files scanned: 30. 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
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 100Tools and files. No external tools needed
  • 100Steps. 23 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 966 tokens
  • 100Running it twice. No mutating operations

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)
  • +3Description length 90: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +4Structure: 14 headings
  • +3Step-by-step instructions: 23 items
  • +4Has examples (3 code blocks)

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

External checks

ClawHub: suspicious
This appears to be a real market-research skill, but it uses under-scoped credential handling, mutates the runtime environment, and stores off-purpose adult or sensitive trend data.
LLM: suspicious (high) · VirusTotal: · 29 May 2026