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Ai output review

Skill ahtishamshahzad/agent_dev_flow/.ai/skills/ai-output-review

Tool-neutral AI Engineering System: 174 reusable skills (installable as Claude Code plugins) for planning and building software with AI agents. Classify → plan → approve → build under quality gates. Works with Claude Code, Codex, Cursor, Windsurf, Copilot & Antigravity. Canonical in .ai/.

Install
npx -y skills add ahtishamshahzad/agent_dev_flow --skill ai-output-review

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What its author says it does

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Use to critically review AI-generated output (plans, code, docs, decisions) for the failure modes AI agents are prone to — unsupported assumptions, invented facts, conflicting architecture, unnecessary dependencies, security risks, missing tests, over-engineering, scope expansion, outdated patterns, incomplete validation, and false claims of completion.

SKILL.md

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AI Output Review

Purpose

Act as a skeptical reviewer of AI-generated output before it is trusted or shipped. Catch the specific failure modes AI agents produce, and separate what is verified from what is asserted. Complements code-review, security-review, and final-quality-audit.

When to Use

  • After any AI agent produces a plan, code change, document, or decision.
  • Before acting on AI output that affects architecture, security, or scope.
  • As an independent-review step in multi-agent runs (../../system/MULTI_AGENT_RULES.md).

Inputs

  • The AI-generated output under review.
  • The original request, requirements, and approved plan/architecture.

Discovery Questions

  • What was actually requested vs what the output delivered?
  • Which claims are backed by evidence vs asserted?
  • Does the output contradict the approved architecture or prior decisions?

Responsibilities

Evaluate the output for each failure mode:

  1. Unsupported assumptions — claims presented as fact without basis.
  2. Invented facts — fabricated APIs, files, paths, results, or citations.
  3. Conflicting architecture — contradicts the approved design or itself.
  4. Unnecessary dependencies — libraries added without a justified need.
  5. Security risks — introduced or ignored (hand depth to security-review).
  6. Missing tests — changes without corresponding verification.
  7. Over-engineering — complexity beyond the requirement.
  8. Scope expansion — work beyond what was approved.
  9. Outdated patterns — deprecated/legacy approaches where current ones exist.
  10. Incomplete validation — checks claimed but not actually run.
  11. False claims of completion — "done"/"working" without evidence.

Report each hit with location, why it's a problem, and the correction.

Required Workflow

  1. Read the request/plan and the AI output side by side.
  2. Walk the 11 failure modes.
  3. For each finding: cite location, classify the failure mode, state the fix.
  4. Separate verified from asserted claims.
  5. Issue a verdict: accept / revise / reject, with blockers.

Decision Rules

  • Treat any "done/working/verified" without evidence as a false-completion finding until proven.
  • Fabricated file/API/result → reject that portion outright.
  • Architecture conflict or scope expansion → block until reconciled with the approved plan.
  • Route deep security/perf concerns to security-review / performance-review.

Rules

  • Be skeptical by default; assertion is not evidence.
  • Cite specifics; no vague "looks off."
  • Don't approve output you can't verify — mark it "unverified."

Anti-Patterns

  • Trusting confident phrasing as correctness.
  • Passing output with fabricated facts or unrun "validation."
  • Missing silent scope expansion because the result "looks good."
  • Reviewing only style while ignoring invented facts.

Validation Checklist

  • All 11 failure modes evaluated.
  • Findings cite location + failure mode + fix.
  • Verified vs asserted separated.
  • Dependency additions justified or flagged.
  • Completion claims backed by evidence or flagged false.
  • Verdict (accept/revise/reject) with blockers.

Definition of Done

A review report covering all 11 failure modes with located findings and fixes, a clean separation of verified vs asserted, and an accept/revise/reject verdict — no fabricated or unvalidated output passed as done.

Related Skills

code-review, security-review, performance-review, final-quality-audit, project-orchestrator, documentation.

Related Knowledge

../../projects/current/ (approved plan/architecture to check against).

Related References

../../checklists/ (review checklists).

Context Loading Guidance

  • Requires: the AI output, the original request, and the approved plan/architecture.
  • Does not require: the whole repo (unless verifying a specific claim), unrelated references.
  • May load: security-review/performance-review for deep concerns; the specific file to verify an invented-fact claim.
  • Stop when: the verdict and findings are delivered.

Token Efficiency Guidance

Compare output against the recorded plan, not the entire history. Verify suspicious claims by checking the specific file/command rather than reloading everything.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.