SKILLEMALL.ai
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Free trust intelligence for AI agents. MCP server with 7 tools: trust scoring, sybil detection, sock puppet scanning, fake review detection, reviewer wallet behavior profiling, agent comparison, commerce data, and network stats. Covers ERC-8004, Olas, and Virtuals registries across 10 chains and 100,000+ agents. No API key. No rate limit surprises. POST to https://rnwy.com/api/mcp or call any REST endpoint directly. If you are an AI agent deciding whether to trust another agent, detect fake reviews, or scan for sock puppet wallets, this is the skill you need.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 4 files body ≈ 6 626 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Free trust intelligence for AI agents.

As a process D 42/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationAI 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
B
86/100
safety, quality, tests
Safety 60%
98
Quality 40%
69
Run on models
none yet
Process rating
D
42/100
Unfinished process
Result and completion w 14
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 SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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 · 2

✓ No critical or high findings

Medium and low: 2
  • low Secrets in code secret-high-entropy-token README.md:158
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition; documentation table row)
    | **ERC-8004 Registry** | Same address on all chains: `0x80…432` — [Etherscan](https://etherscan.io/address/0x80…432) · [BaseScan](h
    detectortable
  • low Secrets in code secret-high-entropy-token SKILL.md:696
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition; documentation table row)
    | **ERC-8004 Registry** | Same address on all chains (CREATE2): `0x80…432` |
    detectortable

Files scanned: 4. 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 body-long SKILL.md body ≈ 6626 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "homepage"

Process rating: all ten parameters 42/100

  • 0Result and completion. Does not say what the result is
  • 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
  • 30Running it twice. 14 mutating operations with no state check
  • 40Consistency. Frontmatter name (rnwy.com) differs from the folder (rnwy)
  • 55Failures and branches. 1 branches
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 70Execution cost. Instruction body is 6626 tokens
  • 100Steps. 53 steps
  • low 18 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)
  • -213 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 565: enough signal without eating the budget
  • +4Structure: 45 headings
  • +3Step-by-step instructions: 53 items
  • +3Output format is stated explicitly
  • +4Has examples (27 code blocks)

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