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BC ERC-8004 Identity Skill

Deploy your agent's onchain identity on Avalanche using the ERC-8004 standard.

modbender/skill-library-mcp Agent Skills author: modbender MIT 6 files body ≈ 889 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Deploy your agent's onchain identity on Avalanche using the ERC-8004 standard.

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, failures and branches

ReferenceInfrastructureAI 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
83/100
safety, quality, tests
Safety 60%
95
Quality 40%
64
Run on models
none yet
Process rating
C
51/100
Has gaps
Result and completion w 14
0
Failures and branches w 10
0
Progress reporting w 2
0
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 · 5

✓ No critical or high findings

Medium and low: 5
  • low Secrets in code secret-high-entropy-token cli.js:8
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    const IDENTITY_REGISTRY = "0x80…432";
    quoted
  • low Secrets in code secret-high-entropy-token cli.js:9
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    const REPUTATION_REGISTRY = "0x80…b63";
    quoted
  • low Secrets in code secret-high-entropy-token package-lock.json:40
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…jgR+Mkmf+D189…ihQ==",
    detector
  • low Secrets in code secret-high-entropy-token SKILL.md:123
    High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
    | Identity Registry | `0x80…432` |
    table
  • low Secrets in code secret-high-entropy-token SKILL.md:124
    High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
    | Reputation Registry | `0x80…b63` |
    table

Files scanned: 6. 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 description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 51/100

  • 0Result and completion. Does not say what the result is
  • 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. 9 mutating operations with no state check
  • 40Consistency. Frontmatter name (ERC-8004 Identity Skill) differs from the folder (erc8004-identity)
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 85Steps. 23 steps, 1 vague phrases
  • 100Tools and files. No external tools needed
  • 100Execution cost. Instruction body is 889 tokens
  • low 11 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (4 tags): a typed call is more reliable

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)
  • +3Description length 78: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +4Structure: 19 headings
  • +3Step-by-step instructions: 23 items
  • +4Has examples (10 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 64.