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Agents40k · #5414

verify-agent-action

Makes your agent review important actions before executing them. It checks commands, deployments, or data changes to ensure safety and accuracy.

What it does

Checks if an action planned by an AI or requested by a human is safe and correct before it's executed. It ensures everything is as it should be to prevent errors or unwanted actions.

When to use it

  • ✓Before an AI performs an important action (like sending an email or making a purchase).
  • ✓When you need to be sure an approval matches the exact action to be performed.
  • ✓When checking if evidence of an action is real and hasn't been tampered with.

Example of what to ask your AI

“Verify that the action of sending this report to client X is correct and safe.”

Want more? Open the sections below.

How to use

The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.

Skill link

https://github.com/github/awesome-copilot/blob/main/skills/verify-agent-action/SKILL.md

Ready-to-paste message

Use this skill to help me: https://github.com/github/awesome-copilot/blob/main/skills/verify-agent-action/SKILL.md Read the SKILL.md file at that link and follow its instructions for my task.
How it works

This system compares the details of the proposed action with available approvals and evidence. It checks if everything matches exactly, if permissions are correct, and if there are no signs of tampering or forgery.

Installation

Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.

In the terminal (Claude Code, Codex, Cursor), in your project folder:

npx skills add github/awesome-copilot --skill verify-agent-action

Or download just the file:

curl -L https://raw.githubusercontent.com/github/awesome-copilot/main/skills/verify-agent-action/SKILL.md -o SKILL.md
Technical content (original SKILL.md)

Original text by the author, straight from GitHub.

Verify Agent Action

Treat a plausible approval screen as a claim, not proof. Verify the complete decision path before a human or an external enforcement point decides whether to act.

Preserve the safety boundary

  • Never execute, approve, sign, send, purchase, deploy, or mutate anything.
  • Never convert this review into execution authority.
  • Never infer missing evidence, identities, timestamps, or parameters.
  • Treat a valid schema, checksum, or signature as insufficient by itself.
  • Treat signatures as evidence of attribution and integrity, not factual truth.
  • Keep supporting and refuting evidence separate; do not average conflict away.
  • Fail closed on a material mismatch. Use INCONCLUSIVE when required evidence is unavailable.

Set this field in every final result:

{"execution_authorized": false}

Collect the review packet

Request only the artifacts needed for the review:

  1. The original user or system request.
  2. The exact proposed action:
    • operation or tool name
    • target resource
    • complete parameters
    • filesystem and network scope
    • maximum execution count
    • not-before and expiry times
  3. The assessment that claims the action is justified.
  4. The source evidence and policy used by that assessment.
  5. The approval record, including approver identity, role, action digest, nonce, audience, issue time, expiry, and use count.
  6. The latest monitoring events and expected heartbeat interval.
  7. The current trusted time and any prior nonce-use record.

List missing fields before analysis. Do not silently substitute defaults.

Build the exact action identity

Create one normalized action object without dropping fields:

{
  "operation": "git.push",
  "target": "owner/repository",
  "parameters": {
    "branch": "fix/example",
    "commit": "40-character-sha",
    "remote": "origin"
  },
  "filesystem_scope": [],
  "network_scope": ["github.com:443"],
  "execution_count": 1,
  "not_before": "RFC3339 timestamp",
  "expires_at": "RFC3339 timestamp"
}

Use a project-specified canonicalization and digest algorithm when provided. Otherwise, report that cryptographic identity cannot be independently verified; still compare every field structurally.

Never normalize away a security-relevant distinction such as:

  • branch, commit, repository, environment, recipient, amount, currency, or host
  • recursive, force, overwrite, privileged, destructive, or dry-run flags
  • filesystem roots, CIDRs, ports, domains, execution counts, or expiry

Run the six controls

Evaluate every control as PASS, FAIL, INCONCLUSIVE, or NOT_APPLICABLE.

1. Recompute the assessment

  • Re-run the declared deterministic evaluator from the declared source inputs when its implementation is available.
  • Compare the complete canonical result, not selected fields.
  • Mark FAIL if the received result differs from recomputation.
  • Mark INCONCLUSIVE when only schema validation, an internal checksum, or an unverifiable evaluator claim is available.

2. Match the exact approved action

  • Compare the proposed action with the action bound into the approval.
  • Compare the complete normalized object and its digest.
  • Mark FAIL if any material field changed after approval.
  • Treat a broad target or scope as a mismatch when the evidence justifies only a narrower action.

3. Reject replay and identity ambiguity

  • Verify the nonce is unique and unused.
  • Verify subject, audience, issuer, approver role, issue time, not-before time, expiry, and maximum use count.
  • Mark FAIL for a reused nonce, wrong audience, expired approval, future-dated approval, excessive use count, revoked identity, or role mismatch.
  • Mark INCONCLUSIVE if no trustworthy replay store or time source exists.

4. Test reviewer independence

Build a dependence table for every reviewer or evaluator:

Dimension Compare
Model family, version, fine-tune
Provider account and control plane
Prompt shared template or ancestry
Retrieval overlapping sources and indexes
Tools shared evaluator code and runtime
Operator common owner or approval authority

Do not count correlated reviewers as independent quorum members. Mark FAIL if the policy requires independent approval and the remaining independent set is too small.

5. Preserve evidence and contradiction

  • Inventory every evidence identifier referenced by the assessment.

…

FAQ

What happens if something is incorrect?

If there are any inconsistencies or doubts, the action will not be allowed, and the system will flag the issue to be corrected before any execution.

Why can't I just trust the AI?

AI is a powerful tool, but an extra layer of security is important for consequential actions, ensuring the original intent is maintained and unexpected errors don't occur.

What does 'fail closed' mean?

It means that in case of doubt or a problem, the system refuses to execute the action for safety, rather than trying to guess or proceeding with a potentially risky action.

Details

Original description by the author

Review a proposed AI-agent action or human-approval packet before execution. Use when an agent wants to run a consequential tool, command, deployment, message, purchase, credential operation, or data mutation; when checking whether approval still matches the exact action; or when auditing action evidence for forged results, parameter swaps, replay, correlated reviewers, missing evidence, expiry, o

repo: github/awesome-copilot

Popularity

GitHub stars
40k
Ranking position
#5414
License
MIT
Updated
10/15/2026

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