SKILLEMALL.ai
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AF onchain-analysis

Analyze any EVM smart contract — auto-fetches ABI, discovers usage patterns, decodes function calls via Dune, generates AI-driven analytics (charts, stats, time series), and returns structured results. Supports Ethereum, Polygon, BSC, Arbitrum, Optimism, Base, Avalanche.

modbender/skill-library-mcp Agent Skills author: modbender MIT 1 file body ≈ 1 683 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Analyze any EVM smart contract — auto-fetches ABI, discovers usage patterns, decodes function calls via Dune, generates AI-driven analytics (charts, stats…

As a process F 49/100 · Will not run — References files that are not bundled: dashboardUrl

AnalyzerData and analyticstype 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
A
91/100
safety, quality, tests
Safety 60%
97
Quality 40%
81
Run on models
none yet
Process rating
F
49/100
Will not run
References files that are not bundled: dashboardUrl
Tools and files w 18
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
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 · 3

✓ No critical or high findings

Medium and low: 3
  • low Secrets in code secret-high-entropy-token SKILL.md:33
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "contractAddress": "0x00…0E2",
    quoted
  • low Secrets in code secret-high-entropy-token SKILL.md:129
    High-entropy token-like string (may be an id, hash or a credential)
    **User:** Analyze 0x7a…88D on ethereum
  • low Secrets in code secret-high-entropy-token SKILL.md:130
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    **Action:** Call the skill with `{ "contractAddress": "0x7a…88D", "chain": "ethereum" }`
    quoted

Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: dashboardUrl

Process rating: all ten parameters 49/100

Will not run. References files that are not bundled: dashboardUrl
  • 0Tools and files. 1 referenced file(s) missing: dashboardUrl
  • 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 (onchain-analysis) differs from the folder (on-chain-analyitcs)
  • 60Result and completion. Output format stated, no completion criterion
  • 100Steps. 26 steps
  • 100Failures and branches. 1 branches, has a failure section
  • 100Execution cost. Instruction body is 1683 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)
  • +1No license
  • +2Single-language instructions
  • +3Description length 271: enough signal without eating the budget
  • +4Structure: 16 headings
  • +3Step-by-step instructions: 26 items
  • +3Output format is stated explicitly
  • +4Has examples (2 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.