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BC Crab Catch

Crab Catch is a Web3 research skill that automatically collects and organizes project data and potential risks from social media, websites, code, and on-chain data, and produces a complete and objective research report.

ClawHub Agent Skills author: NotevenDe v0.1.2 MIT-0 13 files body ≈ 4 607 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 57/100 · Has gaps — weak spots: result and completion, when it triggers, consistency

GeneratorData and analyticsInfrastructureSoftware developmenttype 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
82/100
safety, quality, tests
Safety 60%
96
Quality 40%
62
Run on models
none yet
Process rating
C
57/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
When it triggers w 12
20
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 · 4

✓ No critical or high findings

Medium and low: 4
  • low Secrets in code secret-high-entropy-token API_EXPLORER.md:76
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "address": "0xdA…ec7"
    quoted
  • low Secrets in code secret-high-entropy-token API_EXPLORER.md:155
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "address": "0xd8…045",
    quoted
  • low Secrets in code secret-high-entropy-token API_EXPLORER.md:216
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "address": "vine…PTg",
    quoted
  • low Secrets in code secret-high-entropy-token API_EXPLORER.md:234
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "mint": "Es9v…NYB",
    quoted

Files scanned: 8. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 57/100

  • 0Result and completion. Does not say what the result is
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 40Consistency. Frontmatter name (Crab Catch) differs from the folder (crab)
  • 60Tools and files. Uses tools (bash, web, node) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 4607 tokens
  • 100Steps. 34 steps
  • 100Failures and branches. 3 branches, has a failure section
  • 100Running it twice. Mutating operations check current state
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 10 top-level sections: this looks like several domains in one skill

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
  • -215 emoji in the instructions: noise for the model
  • -31 of 3 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 219: enough signal without eating the budget
  • +4Structure: 20 headings
  • +3Step-by-step instructions: 34 items
  • +4Has examples (3 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 62.

External checks

ClawHub: suspicious
This Web3 research skill is mostly purpose-aligned, but it needs Review because it makes broad environment changes, stores reusable local credentials, saves investigation reports, and sends sensitive research targets to external services without enough upfront scoping or user control.
LLM: suspicious (high) · VirusTotal: · 29 May 2026