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production-audit

Local-evidence production readiness audit for shipped apps, pre-launch reviews, post-merge checks, and

production-audit 是什么?

production-audit is a Claude Code agent skill that local-evidence production readiness audit for shipped apps, pre-launch reviews, post-merge checks, and.

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Production Audit

Use this skill when the user asks whether an application is ready to ship, what could break in production, or what must be fixed before a launch. This is a maintainer-safe rewrite of the stale community production-audit idea: it keeps the useful production-readiness lens and removes unpinned external execution and third-party data sharing.

When to Use

  • The user asks "is this production-ready", "what would break in prod", "what did we miss", "audit this repo", or "ready to ship?"
  • A feature was merged and needs a pre-deploy or post-merge risk pass.
  • A public launch, demo, customer rollout, or investor walkthrough is close.
  • CI is green but the user wants production risk, not only test status.
  • A deployed URL, release branch, PR, or current checkout is available for evidence gathering.

When Not to Use

  • During active implementation when the right lens is line-level secure coding; use security-review first.
  • For pure libraries, templates, docs-only repos, or scaffolds unless the user wants packaging/release readiness rather than application readiness.
  • When the user asks for a formal compliance audit. This skill is engineering triage, not legal, financial, medical, or regulatory certification.
  • When the only available evidence is a product idea with no repo, deployment, CI, or runtime surface.

How It Works

Build the audit from local and user-authorized evidence. Do not run unpinned remote code, upload repository contents to third-party services, or call external scanners unless the user explicitly approves that specific tool and data flow.

Use this order:

  1. Establish the release surface.
  2. Read recent changes and current branch state.
  3. Inspect runtime, auth, data, payment, background-job, AI, and deployment boundaries that actually exist in the repo.
  4. Check CI, tests, migrations, environment documentation, and rollback path.
  5. Produce a short ship/block recommendation with specific fixes.

Evidence Checklist

Start with cheap, local signals:

git status --short --branch
git log --oneline --decorate -20
git diff --stat origin/main...HEAD

Then inspect the project-specific surface:

  • Package scripts, CI workflows, release scripts, Docker files, and deployment manifests.
  • API routes, webhooks, auth middleware, background workers, cron jobs, and database migrations.
  • Environment variable documentation and startup checks.
  • Observability hooks, error reporting, logs, health checks, and dashboards.
  • Rollback, seed, migration, and backfill instructions.
  • E2E coverage for the user paths that matter most.

If a deployed URL is in scope, use browser or HTTP checks only against that URL and avoid credentialed actions unless the user supplies a safe test account.

Risk Lenses

Security And Auth

  • Are public routes, API routes, and admin routes clearly separated?
  • Are auth and authorization enforced server-side?
  • Are secrets kept out of client bundles, logs, example output, and checked-in files?
  • Are rate limits, CSRF protections, CORS policy, and upload validation present where the app needs them?
  • Does the AI or agent surface defend against prompt injection, tool abuse, and untrusted content crossing into privileged actions?

Data Integrity

  • Do migrations run forward cleanly and have a rollback or recovery plan?
  • Are destructive migrations, backfills, and data imports staged safely?
  • Do database policies, grants, and service-role boundaries match the app's tenancy model?
  • Are retries idempotent for writes, jobs, and webhook handlers?

Payments And Webhooks

  • Are webhook signatures verified before parsing trusted payload fields?
  • Is each payment, subscription, or fulfillment webhook idempotent?
  • Are replay, duplicate delivery, and out-of-order delivery handled?
  • Are test-mode and live-mode credentials separated?

Operations

  • Can the app start from a clean checkout using documented commands?
  • Are required environment variables named, validated, and fail-fast?
  • Is there a health check that proves dependencies are reachable?
  • Are deploy, rollback, and incident-owner paths documented?
  • Are logs useful without leaking secrets or personal data?

User Experience

  • Are the launch-critical paths covered on desktop and mobile?
  • Are forms usable on mobile without input zoom, layout overlap, or blocked submission states?
  • Do loading, empty, error, and permission-denied states tell the user what happened?
  • Is there a support or recovery path when a critical operation fails?

Scoring

Use scores to force prioritization, not to imply mathematical certainty.

BandScoreMeaning
Blocked0-49Do not ship until the top risks are fixed
Risky50-69Ship only behind a small rollout or internal beta
Launchable With Caveats70-84Ship if owners accept the listed risks
Strong85-100No obvious launch blockers from available evidence

Cap the score at 69 if any of these are true:

  • Authentication or authorization is missing on sensitive data.
  • Payment or fulfillment webhooks are not idempotent.
  • Required migrations cannot be run safely.
  • Secrets are exposed in client bundles, logs, or committed files.
  • There is no rollback path for a high-impact release.

Cap the score at 84 if CI is not green or the launch-critical path was not tested end to end.

Output Format

Lead with one sentence:

Production audit: 76/100, launchable with caveats, with webhook idempotency and rollback docs as the two risks to fix before public launch.

Then list:

  • Blockers: must-fix items before deploy.
  • High-value fixes: next fixes if the user wants to improve the score.
  • Evidence checked: files, commands, CI, deployed URL, or PRs inspected.
  • Evidence missing: what would change confidence if provided.
  • Next action: one concrete fix or verification step.

Keep strengths short. The user asked for readiness, so the useful answer is the remaining risk and the next action.

Example

User:

is this ready to ship?

Response:

Production audit: 68/100, risky, because Stripe webhooks are verified but not idempotent and there is no rollback note for the pending migration.

Blockers:
- Add idempotency for `checkout.session.completed` before fulfilling orders.
- Write and test the rollback path for `20260511_add_billing_state.sql`.

High-value fixes:
- Add a health check that verifies database and payment-provider reachability.
- Add one E2E path for upgrade, webhook fulfillment, and billing-page refresh.

Evidence checked:
- `api/stripe/webhook.ts`
- `db/migrations/20260511_add_billing_state.sql`
- GitHub Actions run for the release branch

Next action: Want me to patch webhook idempotency first?

Anti-Patterns

  • Running npx <package>@latest or a remote scanner as the default audit path.
  • Uploading source, secrets, customer data, or private topology to an external audit service without explicit approval.
  • Producing a score without naming the evidence checked.
  • Treating green CI as production readiness.
  • Ending with a generic "let me know what you want to do."

See Also

  • Skill: security-review
  • Skill: deployment-patterns
  • Skill: e2e-testing
  • Skill: tdd-workflow
  • Skill: verification-loop

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/content-engine

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.

affaan-m/fal-ai-media

Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.

affaan-m/manim-video

日本語翻訳:このファイルは manim-video 用の日本語翻訳が必要です

affaan-m/remotion-video-creation

Remotion のベストプラクティス - React で動画を作成する。3D、アニメーション、音声、字幕、チャート、トランジションなどをカバーするドメイン固有の29のルール。

affaan-m/video-editing

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

agent-sort

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

ai-regression-testing

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

android-clean-architecture

Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns. Use when structuring modules, layers, or data flow in an Android or KMP project.

angular-developer

Generates Angular code and provides architectural guidance. Trigger when creating projects, components, or services, or for best practices on reactivity (signals, linkedSignal, resource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, or CLI tooling.

api-connector-builder

Build a new API connector or provider by matching the target repo

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