Ring:Auditing Production Readiness logo

Ring:Auditing Production Readiness

Organization
LerianStudio
ring:auditing-production-readiness

Auditing a service's production readiness against Ring engineering standards across base dimensions plus a conditional multi-tenant dimension, then emitting a scored report and an HTML dashboard. Use before production deploy, periodic review, onboarding, or a major release. Skip for prototypes, libraries, or single-dimension checks.

Overview

PublisherLerianStudio
Repositoryring
Skill namering:auditing-production-readiness
Stars
215
Forks
28
Bundled files
7
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 7 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by LerianStudio on GitHub. Read the source before you install it.

Installation

Install the Ring:Auditing Production Readiness AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/LerianStudio/ring.git /tmp/ring
mkdir -p .claude/skills
cp -r /tmp/ring/default/skills/auditing-production-readiness .claude/skills/lerianstudio-ring-auditing-production-readiness
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ring:Auditing Production Readiness in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Ring:Auditing Production Readiness on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Ring:Auditing Production Readiness is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Production Readiness Audit

When to use

  • Preparing a service for production deployment
  • Conducting periodic security or quality review of a codebase
  • Onboarding to assess codebase health and maturity
  • Evaluating technical debt before a major release
  • Validating compliance with Ring engineering standards

Skip when

  • Project is a prototype or throwaway proof-of-concept not heading to production
  • Codebase is a library or SDK with no deployable service component
  • User only needs a single-dimension check (use targeted review instead)

Audit categories

  • Structure (11): pagination, errors, routes, bootstrap, runtime, core deps, naming, domain modeling, nil-safety, api-versioning, resource-leaks
  • Security (9): auth, IDOR, SQL, validation, secret-scanning, data-encryption, multi-tenant, rate-limiting, cors
  • Operations (7): telemetry, health, config, connections, logging, resilience, graceful-degradation
  • Quality (10): idempotency, docs, debt, testing, dependencies, performance, concurrency, migrations, linting, caching
  • Infrastructure (6): containers, hardening, cicd, async, makefile, license

A multi-agent audit system evaluating 43 base dimensions + 1 conditional (multi-tenant) = up to 44 dimensions across 5 categories, aligned with Ring development standards. Detects project stack, loads relevant standards via WebFetch, runs explorers in batches of 10, appending results incrementally to a single report file.

Announce at start: "Using ring:auditing-production-readiness to audit {N} dimensions in 5 batches."

Audit Dimensions

CategoryCountDimensions
A: Code Structure11Pagination, Errors, Routes, Bootstrap, Runtime, Core Deps, Naming, Domain Modeling, Nil Safety, API Versioning, Resource Leaks
B: Security9 (+1c)Auth, IDOR, SQL, Input Validation, Secret Scanning, Data Encryption, Rate Limiting, CORS, Multi-Tenant*
C: Operations7Telemetry, Health, Config, Connections, Logging, Resilience, Graceful Degradation
D: Quality10Idempotency, API Docs, Tech Debt, Testing, Dependencies, Performance, Concurrency, Migrations, Linting, Caching
E: Infrastructure6Containers, HTTP Hardening, CI/CD, Async, Makefile, License

*Conditional on MULTI_TENANT detection. Max score: 430 base + 10 conditional = 440.

Execution Protocol

Step 0: Stack Detection

Glob("**/go.mod")         → GO=true
Glob("**/package.json")   → parse for React/Next (FRONTEND) or Express/Fastify (TS_BACKEND)
Glob("**/Dockerfile*")    → DOCKER=true
Glob("**/Makefile")       → MAKEFILE=true
Glob("**/LICENSE*")       → LICENSE=true
Grep("MULTI_TENANT")      → if found in env/config files: MULTI_TENANT=true

Step 0.5: Load Ring Standards

WebFetch based on detected stack. On failure, note and proceed with generic patterns.

Go stack: core.md, bootstrap.md, security.md, domain.md, api-patterns.md, quality.md, architecture.md, messaging.md, domain-modeling.md, idempotency.md from https://raw.githubusercontent.com/LerianStudio/ring/main/dev-team/docs/standards/golang/

If MULTI_TENANT: Also fetch multi-tenant.md from same base URL.

Always: devops.md and sre.md from https://raw.githubusercontent.com/LerianStudio/ring/main/dev-team/docs/standards/

Store fetched content for injection between ---BEGIN STANDARDS--- / ---END STANDARDS--- markers in each explorer prompt.

Step 1: Initialize Report File

Write header to docs/audits/production-readiness-{YYYY-MM-DDTHH:MM:SS}.md with detected stack, standards loaded, dimension count, and dynamic max score.

Step 2–6: Batch Execution

Read the dimension-specific prompts from dimensions/ subdirectory before dispatching each batch.

BatchRead FileAgentsCategory Focus
1dimensions/structure.md (agents 1-5) + dimensions/security.md (agents 6-9) + dimensions/operations.md (agent 10)10Structure + Security start + Telemetry
2dimensions/operations.md (agents 12-15) + dimensions/quality.md (agents 16-20)9Operations + Quality start
3dimensions/quality.md (agents 21-23) + dimensions/infrastructure.md (agents 24-27) + dimensions/structure.md (agents 28-30)10Quality + Infrastructure + Structure cont.
4dimensions/quality.md (agents 31, 40) + dimensions/infrastructure.md (agents 32, 34) + dimensions/security.md (agents 33*, 37, 41) + dimensions/structure.md (agents 35, 38, 42) + dimensions/operations.md (agents 36, 39)variesMixed (remaining dimensions)
5dimensions/security.md (agents 43-44)2Rate Limiting + CORS

*Agent 33 (Multi-Tenant) only if MULTI_TENANT=true.

After each batch: Append all results to report file before launching next batch.

CRITICAL: Each batch dispatches in a SINGLE turn with N parallel Task calls.

⛔ STOP-CHECK BEFORE DISPATCH (each batch)

Before emitting any Task call in a batch, count the explorers you intend to launch in this turn.

  • Count MUST equal the batch size declared in the batch table above for the current batch.
  • If your dispatch count diverges from the batch size → STOP and reconcile against the batch row.
  • No substitutions, no omissions within a batch.

⛔ MUST NOT trickle-dispatch within a batch

All explorers in a batch leave in the SAME TURN, before reading any explorer output.

Forbidden sequences:

  • Dispatch explorer 1 → read result → dispatch explorer 2
  • Dispatch a subset of the batch → wait → dispatch the rest
  • Dispatch follow-up explorers conditioned on partial output
  • Loop sequentially over the batch's explorer list

If you find yourself about to dispatch an explorer in a turn AFTER any explorer in the SAME batch has already returned a result → STOP. You violated parallel dispatch. Report the violation and mark the batch INCOMPLETE rather than completing the trickle. (Sequential batch ordering is intentional; trickle within a batch is not.)

Self-verify after dispatch

After each batch's dispatch turn, verify all batched Task calls were emitted in that single turn. If fewer went out than the batch size, the batch did NOT execute correctly. Mark INCOMPLETE and surface the dispatch failure — do NOT silently continue with a partial batch.

Parallel dispatch — atomic batch (within this batch)

Emit all Task calls for THIS BATCH in a SINGLE TURN, as one atomic batch. (Batches themselves remain sequential — do not dispatch batch N+1 until batch N has fully returned.)

If your runtime exposes a multi_tool_use.parallel wrapper, use it to dispatch the complete batch in one wrapped invocation. This is the canonical fan-out mechanism on OpenAI-style tool envelopes and on certain Anthropic SDK consumers — naming it explicitly activates parallel emission on runtimes where trickle-dispatch is the default behavior.

If your runtime emits parallel tool_use blocks natively (Claude Code with Claude models), multi_tool_use.parallel may not be needed — but naming it is harmless and serves as an enforcement anchor.

The STOP-CHECK, anti-trickle, and self-verify guards above remain binding regardless of which mechanism your runtime uses.

Step 7: Consolidate Report

  1. Read dimensions/scoring.md for scoring rules
  2. Calculate scores per dimension (0-10), category totals, overall score
  3. Determine readiness classification (percentage-based)
  4. Generate Standards Compliance Cross-Reference table
  5. Update report with Executive Summary prepended

Step 8: Visual Dashboard (MANDATORY)

Invoke Skill("ring:visualizing") to produce an HTML dashboard at docs/audits/production-readiness-{timestamp}-dashboard.html.

Dashboard sections:

  1. Score Hero (score/max, readiness badge, color-coded)
  2. Category Scoreboard (5 cards with progress bars)
  3. Dimension Heatmap (44 dims, color by score range)
  4. HARD GATE Violations (if any)
  5. Critical Blockers (if any)
  6. Remediation Roadmap (4 phases)
  7. Standards Compliance Summary

Open in browser after generation.

Step 9: Present Summary

Summarize: stack detected, standards loaded, overall score/classification, critical/high counts, HARD GATE violations, top 3 recommendations, links to report and dashboard.

Customization Options

FlagEffect
--modules=matching,ingestionOnly audit specified modules
--dimensions=securityRun only security-related auditors
--format=jsonStructured JSON output
--no-standardsSkip Ring standards loading (generic mode)

Blocker Conditions

ConditionAction
Stack undetectableSTOP — ask user to specify stack
Standards WebFetch fails for critical modulesSTOP — audit requires standards
Entire batch failsSTOP — report infrastructure issue
docs/audits/ not writableSTOP — ensure directory exists

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Ring:Auditing Production Readiness AI skill do?

Auditing a service's production readiness against Ring engineering standards across base dimensions plus a conditional multi-tenant dimension, then emitting a scored report and an HTML dashboard. Use before production deploy, periodic review, onboarding, or a major release. Skip for prototypes, libraries, or single-dimension checks.

Why use Ring:Auditing Production Readiness on TypingMind?

Because you install it once and use it with any model. Ring:Auditing Production Readiness is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Ring:Auditing Production Readiness in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/LerianStudio/ring/tree/main/default/skills/auditing-production-readiness. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Ring:Auditing Production Readiness?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Ring:Auditing Production Readiness?

As many as you like. As long as a model supports skills, you can use Ring:Auditing Production Readiness with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Ring:Auditing Production Readiness AI skill free?

Yes. It is published on GitHub by LerianStudio under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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