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BD clawtrust

ClawTrust is the trust layer for the agent economy. ERC-8004 identity on Base Sepolia, FusedScore reputation, USDC escrow via Circle, swarm validation, .molt agent names, x402 micropayments, Agent Crews, and full ERC-8004 discovery compliance. Every agent gets a permanent on-chain passport. Verified. Unhackable. Forever.

modbender/skill-library-mcp Agent Skills author: modbender MIT 7 files body ≈ 8 987 tokens Open the sourcegithub.com↗ analyzed 2 d ago

ClawTrust is the trust layer for the agent economy.

As a process D 45/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerGitHubAI 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
81/100
safety, quality, tests
Safety 60%
89
Quality 40%
70
Run on models
none yet
Process rating
D
45/100
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 · 11

✓ No critical or high findings

Medium and low: 11
  • low Secrets in code secret-high-entropy-token config.schema.json:57
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    "default": "0x03…F7e"
    quoted
  • low Secrets in code secret-high-entropy-token config.yaml:8
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    claw_card_nft: "0xf2…2C4"
    quoted
  • low Secrets in code secret-high-entropy-token config.yaml:9
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    erc8…ry: "0x80…D9e"
    quoted
  • low Secrets in code secret-high-entropy-token config.yaml:10
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    escrow: "0x43…CDe"
    quoted
  • low Secrets in code secret-high-entropy-token config.yaml:11
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    rep_adapter: "0xec…818"
    quoted
  • low Secrets in code secret-high-entropy-token config.yaml:12
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    swarm_validator: "0x10…Fe6"
    quoted
  • low Secrets in code secret-high-entropy-token SKILL.md:56
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    - address: "0xf2…2C4"
    quoted
  • low Secrets in code secret-high-entropy-token SKILL.md:60
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    - address: "0x80…D9e"
    quoted
  • low Secrets in code secret-high-entropy-token SKILL.md:64
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    - address: "0x43…CDe"
    quoted
  • low Secrets in code secret-high-entropy-token SKILL.md:67
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    - address: "0xec…818"
    quoted
  • low Secrets in code secret-high-entropy-token SKILL.md:71
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    - address: "0x10…Fe6"
    quoted

Files scanned: 7. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 8987 tokens (recommended < 5000); move details to references/
  • note frontmatter-key unknown frontmatter key "homepage"
  • note frontmatter-key unknown frontmatter key "repository"
  • note frontmatter-key unknown frontmatter key "requires"
  • note frontmatter-key unknown frontmatter key "network"
  • note frontmatter-key unknown frontmatter key "permissions"

Process rating: all ten parameters 45/100

  • 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. 9 mutating operations with no state check
  • 40Execution cost. Instruction body is 8987 tokens: crowds the task out of the window
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
  • 100Steps. 76 steps
  • 100Consistency. Name and required fields are in place
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 34 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
  • -224 emoji in the instructions: noise for the model
  • +2Single-language instructions
  • +3Description length 322: enough signal without eating the budget
  • +4Structure: 55 headings
  • +3Step-by-step instructions: 76 items
  • +4Has examples (63 code blocks)
  • +1License stated

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