SKILLEMALL.ai
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BC arbiscan

Comprehensive crypto market scanner across Binance, OKX, Bybit, and Bitget. 12 scan types covering arbitrage (funding rate, basis, spot spread, futures spread), market monitoring (open interest, price movers, volume anomaly, stablecoin depeg, funding extreme), and trading signals (funding trend, long/short ratio, new listing detection). Read-only — no trading, no API keys needed.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 22 files body ≈ 4 474 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Comprehensive crypto market scanner across Binance, OKX, Bybit, and Bitget.

As a process C 56/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, consistency

IntegrationInfrastructuretype 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
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
70
Run on models
none yet
Process rating
C
56/100
Has gaps
Inputs and preconditions w 11
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 · 0

✓ No critical or high findings

Files scanned: 22. 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")
  • note frontmatter-key unknown frontmatter key "display_name"
  • note frontmatter-key unknown frontmatter key "composable_with"

Process rating: all ten parameters 56/100

  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 20When it triggers. No condition that starts the skill
  • 40Consistency. Frontmatter name (arbiscan) differs from the folder (arbiscan-skill)
  • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 70Execution cost. Instruction body is 4474 tokens
  • 85Steps. 65 steps, 1 vague phrases
  • 100Failures and branches. 1 branches, has a failure section
  • 100Running it twice. No mutating operations
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 12 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)
  • -315 of 16 scripts are never mentioned in SKILL.md
  • +1No license
  • +2Single-language instructions
  • +3Description length 382: enough signal without eating the budget
  • +4Structure: 25 headings
  • +3Step-by-step instructions: 65 items
  • +3Output format is stated explicitly
  • +4Has examples (36 code blocks)

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