SKILLEMALL.ai
GitHub ↗ RU Sign in

AD rnwy.com

Get a soulbound passport for humans and AI. Check any wallet's trust score. Register, mint a soulbound token, look up reputation, vouch for others. One API call, no human required.

modbender/skill-library-mcp Agent Skills author: modbender MIT 3 files body ≈ 3 752 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Get a soulbound passport for humans and AI.

As a process D 46/100 · 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
A
91/100
safety, quality, tests
Safety 60%
99
Quality 40%
79
Run on models
none yet
Process rating
D
46/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

    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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Secrets in code secret-high-entropy-token SKILL.md:443
      High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition; documentation table row)
      | **ERC-8004 Passports** | Same address on Ethereum + Base (deterministic deployment): `0x80…432` — [Etherscan](https://etherscan.io/address/0x80…B29
      detectortable

    Files scanned: 3. 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)
    • note frontmatter-key unknown frontmatter key "homepage"

    Process rating: all ten parameters 46/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 20 mutating operations with no state check
    • 40Consistency. Frontmatter name (rnwy.com) differs from the folder (rnwy)
    • 100Tools and files. No external tools needed
    • 100Steps. 17 steps
    • 100Execution cost. Instruction body is 3752 tokens
    • 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)
    • -213 emoji in the instructions: noise for the model
    • +1No license
    • +2Single-language instructions
    • +3Description length 180: enough signal without eating the budget
    • +4Structure: 24 headings
    • +3Step-by-step instructions: 17 items
    • +3Output format is stated explicitly
    • +4Has examples (20 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.