跳到正文
cccskills
免费在 GitHub 公开

audit-agent-run-evidence

Audit an AI agent's completion claims using existing logs, artifacts, approvals, and deployment records. Reconstruct the run and assess each claim separately, distinguishing proven results, partial support, contradictory evidence, and missing proof without rerunning the work.

中文简介正在准备中。当前显示英语简介。

原文查看原文说明

Use when an agent, harness, gateway, MCP workflow, or multi-step automation claims completion and the available traces, checkpoints, approvals, tool calls, or deployment records must be judged without trusting self-reported success.

实测评分 未实测

查看安装方法

适用场景

  • Checking whether an agent's completion report has supporting evidence
  • Reviewing tool calls, retries, and checkpoint recovery
  • Matching approvals and deployments to the same artifact
  • Separating missing evidence from recorded failures

包含的文件(1)

  • SKILL.md8.3 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Audit Agent Run Evidence

Overview

Turn an end-to-end success statement into independently decidable claims. Reconstruct what happened from available records, grade each claim against the strongest witness, and keep missing evidence distinct from failure.

This is a read-only audit. Do not rerun tools, approve actions, resume workers, deploy artifacts, or modify evidence unless the user separately authorizes those actions.

When to Use

  • Auditing a completed or interrupted agent run from traces and artifacts.
  • Checking whether an agent's end-to-end success claim is actually supported.
  • Reviewing MCP, gateway, sandbox, checkpoint, retry, memory, approval, or deployment evidence.
  • Separating autonomous success from human-assisted or merely requested outcomes.

Do not use this skill to design instrumentation for a future run or to perform the missing actions. It evaluates evidence that already exists.

Establish the Contract

Record these inputs before judging the run:

  • declared goal and terminal success criteria;
  • run, workflow, task, and parent identifiers;
  • immutable code, configuration, model, prompt, tool-schema, and artifact revisions when available;
  • actors and trust boundaries: orchestrator, worker, sandbox, MCP server, gateway, human approver, CI, and deployment platform;
  • retry, deadline, token, cost, concurrency, and human-escalation budgets;
  • supplied evidence inventory and known collection gaps.

Do not silently strengthen the original success criteria. Do not weaken them to match the evidence that happens to exist.

Build a Claim Ledger

Split the overall claim into atomic predicates. Give every row a stable claim ID.

FieldRequired content
claim_idStable identifier
predicateOne falsifiable statement
required_witnessSource that can independently prove it
evidence_refsExact event, log, artifact, or record IDs
counterevidence_refsConflicting records
coverageRequired instances versus observed instances
verdictproven, partially_proven, contradicted, or not_proven
gapMissing field, actor, interval, or verification

Typical predicates include:

  • every required step reached its terminal postcondition;
  • sandbox isolation held for every executing worker;
  • each required MCP/tool call has a correlated response;
  • retries respected idempotency and did not duplicate committed effects;
  • a checkpoint was durably written, verified, and actually used for resume;
  • parallel branches satisfied the declared join policy;
  • memory reads cite a versioned source rather than an untracked summary;
  • retry, deadline, token, cost, and escalation budgets were respected;
  • approval was granted by an authorized human for the exact artifact and target;
  • the platform deployed that same artifact and passed the declared health checks.

Normalize Evidence

Preserve original records and create a normalized event view with:

{
  "run_id": "run-123",
  "event_id": "evt-42",
  "sequence": 42,
  "observed_at": "RFC3339 timestamp",
  "actor": {"type": "worker", "id": "worker-2"},
  "operation": "mcp.search",
  "state_before": "researching",
  "state_after": "researching",
  "attempt": 2,
  "request_id": "req-9",
  "idempotency_key": "task-7:search:2",
  "input_digest": "sha256:...",
  "output_digest": "sha256:...",
  "checkpoint_seq": 3,
  "parent_event_id": "evt-41",
  "status": "succeeded",
  "evidence_ref": "tool-log:991"
}

Use null or unknown for absent values. Never synthesize IDs, timestamps, digests, costs, approvals, or outcomes.

Verify bundle hashes or signatures when supplied. Check duplicate IDs, broken parent links, non-monotonic per-source sequences, impossible state transitions, unaccounted clock skew, and unexplained trace gaps. Treat an integrity failure as counterevidence for claims that depend on the affected records.

Rank Witnesses

Prefer the witness closest to the effect:

ClaimStrong witnessInsufficient alone
Code changedCommit/tree and diffAgent narration
Test passedComplete test result bound to revisionCommand invocation
MCP effect occurredServer or provider audit recordClient request
Checkpoint resumedDurable checkpoint plus verified load eventCheckpoint file exists
Human approvedAuthorization-system decision bound to artifact and targetApproval requested
Deployment succeededPlatform record plus required health checksDeployment started
Memory grounded a decisionVersioned memory read and citationFinal answer resembles memory

An orchestrator and its child worker are not independent witnesses when they repeat the same unverified result. A cryptographic digest proves byte identity, not semantic correctness.

Reconstruct the Run

  1. Order events by causal links and per-source sequence; use timestamps only as supporting evidence.
  2. Build the state-transition path and mark every gap or illegal transition.
  3. Link each retry chain by logical operation, request ID, and idempotency key.
  4. Link checkpoints to the state they contain and the resume event that consumes them.
  5. Preserve every parallel branch outcome; apply the declared all_required, quorum, first_success, or other join rule.
  6. Track remaining budgets at each transition. A late success after budget exhaustion is a budget violation.
  7. Bind approvals and deployment records to exact artifact digests and targets.

Do not infer successful completion from a final state label when required intermediate predicates are missing.

Assign Verdicts

  • proven: authentic evidence covers every instance of the predicate and no reliable counterevidence remains.
  • partially_proven: some required instances or fields are proven and the uncovered portion is named.
  • contradicted: reliable evidence conflicts with the predicate.
  • not_proven: evidence is absent, circular, unverifiable, or only self-reported.

Use not_proven, not contradicted, for missing logs. Use contradicted when the trace shows a failed health check, duplicate effect, unauthorized approver, corrupt checkpoint, skipped required branch, or exhausted budget.

The end-to-end verdict cannot be stronger than its weakest required predicate. Optional diagnostics may remain unproven without failing the run if they were never part of the declared contract.

Report

Return sections in this order:

  1. Scope and evidence inventory — run identity, declared criteria, records inspected, integrity checks.
  2. Claim ledger — one row per predicate with verdict and exact references.
  3. Reconstructed timeline — only state-changing, fault, retry, checkpoint, join, approval, and deployment events.
  4. Gaps and counterevidence — identify the affected claims and whether collection can still recover the evidence.
  5. Overall verdict — one sentence plus the blocking claim IDs.

Example conclusion:

partially_proven: repository steps C1-C18 and checkpoint recovery C22 are proven, but deployment success is not proven because C31 has only a client-side start event and no platform health result.

Common Mistakes

  • Treating a successful process exit as proof of the business postcondition.
  • Counting retries as separate successful logical operations.
  • Accepting a child agent's summary as independent corroboration.
  • Calling a checkpoint recoverable without observing a verified reload.
  • Calling an approval request an approval grant.
  • Reporting percentages without listing the denominator and missing instances.
  • Recommending instrumentation as though it were evidence from the completed run.

Limitations

  • An audit cannot recover facts that no trusted source recorded.
  • Provider logs may establish external effects without proving the agent's internal reasoning.
  • Redaction may be necessary for secrets and personal data; record the redaction scope and preserve stable references.
  • If evidence collection would mutate external state or expose sensitive data, stop and request authorization.

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

Assess a new or existing AI project by examining its code, goals, and constraints. Recommend skills confirmed to exist in the available registry, then outline tasks and implementation phases with dependencies, completion criteria, and checks.

中文简介正在准备中。当前显示英语简介。

  • Planning the first steps of a new AI project
  • Assessing technical issues in an existing project
  • Selecting relevant skills from an available registry
sickn33/agentic-awesome-skills4.8万2026年10月23日 更新

007

免费

Review code and infrastructure for security risks, map potential threats, and plan stronger defenses. The skill covers OWASP checks, attack and defense exercises, and incident response, with validation required for each environment.

中文简介正在准备中。当前显示英语简介。

  • Reviewing code and infrastructure security
  • Mapping threats with STRIDE or PASTA
  • Planning stronger security settings
sickn33/agentic-awesome-skills4.8万2026年10月23日 更新

Turn a repeatable task into an Andru.ia skill with clear inputs, outputs, examples, and limits. Draft SKILL.md, check the folder structure and required validations, and prepare a pull request describing the changes for review.

中文简介正在准备中。当前显示英语简介。

  • Creating a skill for a repeatable task
  • Improving an existing skill’s examples and limits
  • Validating skill files and preparing a pull request
sickn33/agentic-awesome-skills4.8万2026年10月23日 更新

Prepare an industry briefing once a project's sector is defined. It covers sector context, potentially applicable regulations, familiar interface patterns, technology choices, and risks, while separating verified evidence from hypotheses and questions requiring further review.

中文简介正在准备中。当前显示英语简介。

  • Researching industry requirements before product design
  • Checking regulations for a project's jurisdiction
  • Choosing interface patterns familiar to target users
sickn33/agentic-awesome-skills4.8万2026年10月23日 更新

Create presentation drafts from text, outlines, or documents through the 2slides API. Match a reference image's style, choose themes, add AI voice narration, and export slide images or audio for review and use.

中文简介正在准备中。当前显示英语简介。

  • Create a presentation from text or an outline
  • Summarize a document into slides
  • Match slide styling to a reference image
sickn33/agentic-awesome-skills4.8万2026年10月23日 更新

Plan a 360-degree or peer feedback process and create empty response templates from confirmed requirements. Produces consistent CSV, SQL, JSON Schema, or Notion property mappings while keeping visibility choices and scoring configuration separate.

中文简介正在准备中。当前显示英语简介。

  • Plan an anonymous peer feedback process
  • Create a CSV template for feedback responses
  • Align feedback fields across SQL and JSON Schema
sickn33/agentic-awesome-skills4.8万2026年10月23日 更新

查看 sickn33 的全部技能

报告此技能的问题