SKILLEMALL.ai
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BC x402engine

Pay-per-call API gateway skill — calls 63 APIs (LLMs, image/video gen, flights, hotels, crypto, web search) via x402 micropayments. Each call costs $0.001-$0.60 in USDC/USDm, enforced by a local policy engine with spend caps.

ClawHub Agent Skills author: agentc22 v1.3.2 21 files body ≈ 1 071 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 59/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

IntegrationAI and agentsInfrastructureMedia and videotype 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
80/100
safety, quality, tests
Safety 60%
88
Quality 40%
68
Run on models
none yet
Process rating
C
59/100
Has gaps
Result and completion w 14
0
When it triggers w 12
20
Inputs and preconditions w 11
30
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Risky intent medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The purpose itself is risky: wallets, browser password stores, offensive security. Even an honest implementation gives the agent access to things that cost money.

For the author

Explain in the description why the access is needed and how it is limited; add tests that show refusals on dangerous requests.

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 · 8

✓ No critical or high findings

Medium and low: 8
  • medium Risky intent intent-wallet-secrets README.md:26
    Handles crypto-wallet secrets (seed / mnemonic / private key) — a classic stealer target
    - `EVM_PRIVATE_KEY` — funded EVM wallet private key (Base USDC or MegaETH USDm)
  • low Secrets in code secret-high-entropy-token package-lock.json:24
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…p81+8Ob3…L40/b0i7…gQw==",
    detector
  • low Secrets in code secret-high-entropy-token package-lock.json:49
    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 package-lock.json:111
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha512-+lF0B…kdX+94aG…d8c+B3KT…U6A==",
    detector
  • low Secrets in code secret-high-entropy-token package-lock.json:280
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…eyz+fNF6…T8Q==",
    detector
  • low Secrets in code secret-high-entropy-token package-lock.json:427
    High-entropy token-like string (may be an id, hash or a credential) (detector / deny-list definition)
    "integrity": "sha5…dAu+8DsC/ilFtEVHcH+wydc…8Fg==",
    detector
  • low Secrets in code secret-high-entropy-token POLICY.example.json:44
    High-entropy token-like string (may be an id, hash or a credential) (test fixture / example file; quoted — discussed, not commanded)
    "0x7d…629",
    fixturequoted
  • low Secrets in code secret-high-entropy-token POLICY.example.json:45
    High-entropy token-like string (may be an id, hash or a credential) (test fixture / example file; quoted — discussed, not commanded)
    "Gzeh…QWa"
    fixturequoted

Files scanned: 15. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 59/100

  • 0Result and completion. Does not say what the result is
  • 20When it triggers. No condition that starts the skill
  • 30Inputs and preconditions. Does not say what the process needs to start
  • 60Tools and files. Uses tools (web, python, node) that frontmatter does not declare
  • 70Failures and branches. 6 branches
  • 100Steps. 46 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1071 tokens
  • 100Running it twice. No mutating operations
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

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
  • +4No input/output examples
  • +1No license
  • +2Single-language instructions
  • +3Description length 225: enough signal without eating the budget
  • +4Structure: 12 headings
  • +3Step-by-step instructions: 46 items

Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.

External checks

ClawHub: suspicious
This is a disclosed paid API gateway, but it can automatically spend from a funded wallet on broad everyday requests without built-in per-call confirmation or default spending limits.
LLM: suspicious (high) · VirusTotal: · 29 May 2026