SKILLEMALL.ai
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AC mandate

Use when enforcing spend limits on AI agent wallets, validating transactions before signing, configuring allowlists or approval workflows, detecting prompt injection in agent reasoning, scanning codebases for unprotected wallet calls, or auditing agent transaction history. Works with OpenClaw, Claude Code, GOAT, AgentKit, ElizaOS. Supports Bankr, Locus, Sponge, CDP wallets. Non-custodial: private keys never leave your machine.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 2 files body ≈ 4 799 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Use when enforcing spend limits on AI agent wallets, validating transactions before signing, configuring allowlists or approval workflows, detecting prompt…

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

AnalyzerAI 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%
95
Quality 40%
86
Run on models
none yet
Process rating
C
51/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
20
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 · 5

    ✓ No critical or high findings

    Medium and low: 5
    • low Secrets in code secret-high-entropy-token SKILL.md:141
      High-entropy token-like string (may be an id, hash or a credential)
      --to 0x03…F7e \
    • low Secrets in code secret-high-entropy-token SKILL.md:155
      High-entropy token-like string (may be an id, hash or a credential)
      --token 0x03…F7e \
    • low Secrets in code secret-high-entropy-token SKILL.md:444
      High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
      | Ethereum | 1 | `0xA0…B48` | 6 |
      table
    • low Secrets in code secret-high-entropy-token SKILL.md:445
      High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
      | Sepolia | 11155111 | `0x1c…238` | 6 |
      table
    • low Secrets in code secret-high-entropy-token SKILL.md:446
      High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
      | Base | 8453 | `0x83…913` | 6 |
      table

    Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "homepage"
    • note edit-residue the text marks something as outdated (lines 18, 35, 36, 124, 126, 229): check that old rules are not kept next to new ones — the full check reads the text for contradictions

    Process rating: all ten parameters 51/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 6 mutating operations with no state check
    • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
    • 70Execution cost. Instruction body is 4799 tokens
    • 85Steps. 34 steps, 2 vague phrases
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Progress reporting. Reports progress
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
    • low 24 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)
    • +1No license
    • +2Single-language instructions
    • +3Description length 430: enough signal without eating the budget
    • +4Structure: 43 headings
    • +3Step-by-step instructions: 34 items
    • +3Output format is stated explicitly
    • +4Has examples (26 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.