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DC agirails-agent-payments

AI agent payment infrastructure — ACTP escrow, x402 instant payments, USDC settlement on Base L2. Interactive onboarding: asks your preferences, generates customized agent code, and verifies setup. Covers provider/requester patterns, adapter routing, 8-state machine, pricing, disputes, and identity.

davila7/claude-code-templates Agent Skills author: davila7 MIT 1 file body ≈ 8 527 tokens Open the sourcegithub.com↗ analyzed 6 d ago

AI agent payment infrastructure — ACTP escrow, x402 instant payments, USDC settlement on Base L2.

As a process C 61/100 · Has gaps — weak spots: result and completion, execution cost, running it twice

AnalyzerAI and agentsInfrastructureSoftware developmenttype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
D
77/100
safety, quality, tests
Safety 60%
90
Quality 40%
58
Run on models
none yet
Process rating
C
61/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
Running it twice w 4
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.
  2. 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 · 6

✓ No critical or high findings

Medium and low: 6
  • medium Risky intent intent-wallet-secrets SKILL.md:178
    Handles crypto-wallet secrets (seed / mnemonic / private key) — a classic stealer target
    Set the keystore password (testnet/mainnet only):
  • low Secrets in code secret-high-entropy-token SKILL.md:594
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    - Base Sepolia: `0x80…D9e`
    quoted
  • low Secrets in code secret-high-entropy-token SKILL.md:595
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    - Base Mainnet: `0x80…432`
    quoted
  • low Secrets in code secret-high-entropy-token SKILL.md:597
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    - Base Sepolia: `0x80…713`
    quoted
  • low Secrets in code secret-high-entropy-token SKILL.md:598
    High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
    - Base Mainnet: `0x80…b63`
    quoted
  • low Secrets in code secret-high-entropy-token SKILL.md:643
    High-entropy token-like string (may be an id, hash or a credential) (documentation table row)
    | ACTPKernel | `0x46…411` |
    table

Files scanned: 1. 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")
  • warning body-long SKILL.md body ≈ 8527 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 61/100

  • 0Result and completion. Does not say what the result is
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 25 mutating operations with no state check
  • 40Execution cost. Instruction body is 8527 tokens: crowds the task out of the window
  • 50When it triggers. No condition that starts the skill
  • 60Tools and files. Uses tools (bash, web, python, node) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Steps. 95 steps
  • 100Failures and branches. 3 branches, has a failure section
  • 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 27 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (11 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)
  • +3Output format is not stated: the model decides each time
  • -5TODO / placeholder text left in the skill
  • +2Single-language instructions
  • +3Description length 300: enough signal without eating the budget
  • +4Structure: 51 headings
  • +3Step-by-step instructions: 95 items
  • +4Has examples (29 code blocks)
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 58.