verify-agent-action

作成者: github

提案されたAIエージェントのアクションまたは人間の承認パケットを実行前にレビューする。エージェントが重要なツール、コマンド、デプロイ、メッセージなどを実行しようとする際に使用する。

npx skills add https://github.com/github/awesome-copilot --skill verify-agent-action

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:

DimensionCompare
Modelfamily, version, fine-tune
Provideraccount and control plane
Promptshared template or ancestry
Retrievaloverlapping sources and indexes
Toolsshared evaluator code and runtime
Operatorcommon 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.
  • Confirm each item is present, authenticatable, within its validity window, and relevant to the claim.
  • Record support and refutation independently:
SupportRefutationEpistemic state
absentabsentUNDETERMINED
presentabsentSUPPORTED_ONLY
absentpresentREFUTED_ONLY
presentpresentCONFLICTED
  • Mark FAIL if evidence was removed, altered, expired, or concealed in a way that changes the result.
  • Never convert CONFLICTED into a numeric average that appears safe.

6. Verify lifecycle and monitoring

  • Confirm the action is inside its validity window.
  • Verify monitoring-event signatures or integrity evidence when available.
  • Check sequence numbers, previous-event digests, and expected heartbeat cadence.
  • Treat missing, stale, reordered, or broken-chain telemetry as a failure when policy requires continuous monitoring.
  • Do not interpret silence as health.

Challenge convenient conclusions

Before producing the final result, attempt these mutations mentally or with project-provided test fixtures:

  1. Replace a blocked assessment with an allowed result.
  2. Change one approved target, parameter, scope, amount, or commit.
  3. Reuse an otherwise valid approval nonce.
  4. Replace independent reviewers with correlated copies.
  5. Remove one refuting evidence item.
  6. Stop the monitoring heartbeat after approval.

If any mutation would pass the reviewed controls, record the affected control as FAIL; do not merely recommend future hardening.

Determine the review result

Use exactly one result:

  • ELIGIBLE_FOR_HUMAN_DECISION: all required controls pass.
  • ELIGIBLE_WITH_CONTROLS: no required control fails, and explicit external controls can resolve the listed conditions before execution.
  • BLOCKED: at least one required control fails or the action exceeds the justified scope.
  • INCONCLUSIVE: no required control is proven false, but evidence needed for a safe decision is missing or unverifiable.

ELIGIBLE_FOR_HUMAN_DECISION is not approval. A human authority and a separate enforcement point remain responsible for any real action.

Report in this format

# Agent Action Review

## Result
- Review result: BLOCKED | INCONCLUSIVE | ELIGIBLE_WITH_CONTROLS |
  ELIGIBLE_FOR_HUMAN_DECISION
- Execution authorized: false
- Exact action digest: <verified value or NOT_VERIFIED>

## Action
- Operation:
- Target:
- Material parameters:
- Scope:
- Validity window:
- Maximum uses:

## Control matrix
| Control | Status | Evidence | Reason |
|---|---|---|---|
| Recomputed assessment | PASS/FAIL/INCONCLUSIVE/N/A | ... | ... |
| Exact action binding | ... | ... | ... |
| Replay and identity | ... | ... | ... |
| Reviewer independence | ... | ... | ... |
| Evidence completeness | ... | ... | ... |
| Monitoring freshness | ... | ... | ... |

## Supporting evidence
- ...

## Refuting evidence and defeaters
- ...

## Required next action
- State the smallest concrete step that could change the result.

## Boundaries
- State what this review did not prove.

Lead with the result and the exact reason. Prefer a reproducible blocker over a confidence score.

githubのその他のスキル

debugging-workflows
github
GitHubのエージェンティックワークフローをデバッグするためのガイド - ログの分析、実行の監査、問題のトラブルシューティング
go-codemod
github
gh aw fix コマンド用のGoコードモッドを実装およびテストする。
acreadiness-policy
github
ユーザーがAgentRCポリシーを選択、作成、または適用するのを支援します。ポリシーは、関連性のないチェックを無効にしたり、影響度/レベルを上書きしたり、設定することで、レディネススコアリングをカスタマイズします。
ai-ready
github
あらゆるリポジトリをAI対応にします。コードベースを分析し、AGENTS.md、copilot-instructions.md、CIワークフロー、Issueテンプレートなどを生成します。PRレビューを解析し…
create-oo-component-documentation
github
オブジェクト指向コンポーネント向けに、業界のベストプラクティスとアーキテクチャ文書化標準に従った、包括的で標準化されたドキュメントを作成します。
dependabot
github
DependabotはGitHubに組み込まれた依存関係管理ツールであり、3つの主要機能を備えています。
doublecheck
github
AI出力のための3層検証パイプライン。検証可能な主張を抽出し、ウェブ検索で支持または反証する情報源を見つけ、敵対的レビューを実行…
foundry-agent-sync
github
ローカルのJSONマニフェストから、REST APIを介してAzure AI Foundry内でプロンプトベースのAIエージェントを直接作成および同期します。スキャフォールディングスキルとは異なり、単に…