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Agent commerce action review

Skill fxjim/agent-commerce-guard/plugins/agent-commerce-guard/skills/agent-commerce-action-review

Free GitHub Action and 1 USDC x402 validator for AI-agent wallet spend, deployments, destructive commands, tokens, and credentials

Install
npx -y skills add fxjim/agent-commerce-guard --skill agent-commerce-action-review

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

2 things to look at

  • no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
  • 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Review proposed AI-agent actions locally before execution. Use for deployments, external publishing, financial actions, marketplace jobs, social actions, destructive commands, billing changes, elevated permissions, or credential-dependent work. Produce an allow, review, or deny decision with reasons and required evidence. Never request or reproduce secrets, customer data, credentials, or private wallet material.

SKILL.md

4.0 KB, 762 tokens by cl100k_base, as published. Nobody here has run it

Agent Commerce Action Review

Preflight proposed actions with a local, evidence-first approval rubric. This skill performs no network requests and never authorizes or executes an action.

Hard Boundaries

  • Review only. Do not execute, simulate execution, sign, publish, deploy, pay, trade, delete, or change external state.
  • Do not call a hosted evaluator or send the manifest to an external service.
  • Never ask for or reproduce secrets, tokens, passwords, cookies, credentials, private keys, seed phrases, recovery codes, customer data, or private wallet material.
  • Describe sensitive dependencies with booleans or labels, such as "credentialRequired": true; do not include the sensitive value.
  • Treat missing evidence as missing. Do not infer approval, reversibility, budgets, or target identity.

Normalize The Proposal

Convert each proposed action into this compact manifest when the user has not already supplied equivalent fields:

{
  "actions": [
    {
      "id": "deploy-production",
      "actor": "copilot",
      "action": "Deploy the customer portal to production",
      "target": "production",
      "readOnly": false,
      "externalStateChange": true,
      "destructive": false,
      "credentialRequired": false,
      "amountUsd": 0,
      "budgetUsd": 0,
      "approved": false,
      "validation": "Run the production smoke suite",
      "rollback": "Redeploy the previous release"
    }
  ]
}

Workflow

  1. Summarize each proposed action without executing it.
  2. Remove any sensitive value from the review context. If safe abstraction is impossible, stop and return deny.
  3. Read the decision rubric.
  4. Identify every applicable risk category. Do not stop after the first match.
  5. Apply decision precedence: deny overrides review, which overrides allow.
  6. Compare declared spend or budget only with a limit explicitly supplied by the user or repository policy. If no limit exists, do not invent one.
  7. List the missing evidence required to move the action toward a safer state.
  8. Report results in the required format below.
  9. Keep execution separate. A later execution request still requires explicit user approval and the safeguards appropriate to that action.

Use the worked examples to resolve ambiguous cases consistently.

Required Output

Start with a summary table:

| ID | Decision | Severity | Primary reason |
| --- | --- | --- | --- |
| deploy-production | REVIEW | high | Production deployment changes external state and lacks explicit approval. |

Then provide one section per action:

## deploy-production: REVIEW

- Risk categories: production-change, external-publish
- Reasons:
  - Production deployment changes external state.
  - Explicit approval is not present in the current conversation.
- Required evidence:
  - Exact target and proposed command
  - Current-conversation user approval
  - Validation plan and success criteria
  - Rollback command or recovery path
- Execution status: not executed

Always include Execution status: not executed.

Incomplete Proposals

Ask only for the non-sensitive facts needed to decide the review. When a critical fact remains unavailable:

  • use review when the action may be safe after ordinary clarification;
  • use deny when the proposal involves credentials, funds, live trading, public identity, destructive changes, or another irreversible boundary without explicit approval.

Never reinterpret a failed or incomplete review as approval.

What ships with it: 2 files

6.6 KB alongside SKILL.md

Gives 0 of the 12 instructions most context ai engineering skills give in 762 tokens

Counted across 1,193 of the 1,976 authors here whose files we hold, read 2026-08-07

  • Dispatch a fresh implementer subagent per taskin 48 of 1193, across 19 files
  • Dispatch a final code reviewer after all tasksin 33 of 1193, across 8 files
  • Provide full task text to the subagentin 30 of 1193, across 9 files
  • Review spec compliance before code qualityin 27 of 1193, across 10 files
  • Make the hook script executablein 26 of 1193, across 8 files
  • Re-snapshot after navigation or DOM changesin 25 of 1193, across 19 files
  • Read files before editing themin 22 of 1193, across 11 files
  • Answer subagent questions before proceedingin 22 of 1193, across 7 files
  • Mark task complete in TodoWrite after approvalin 22 of 1193, across 6 files
  • Merge hook into existing settingsin 21 of 1193, across 3 files
  • Ask if installation is global or projectin 20 of 1193, across 2 files
  • Copy the hook script to target locationin 20 of 1193, across 2 files

Said here and by no other author read

  • summarize each proposed action without executing
  • remove sensitive values from review context
  • read the decision rubric
  • identify every applicable risk category
  • apply decision precedence deny over review over allow
  • compare spend against explicit limits only

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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