SKILLEMALL.ai

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.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 8 files body ≈ 4 607 tokens Open the sourcegithub.com analyzed 2 d ago

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…

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

AnalyzerGitHubAI and agentsData and analyticstype and topics are labelled automatically from the skill text
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. 1 branches, has a failure section
  • 100Running it twice. Mutating operations check current state
  • 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.