Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add LerianStudio/ring --skill auditing-production-readinessgit clone --depth 1 https://github.com/LerianStudio/ringWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/lerianstudio/ring/auditing-production-readiness)<a href="https://agentmods.dev/skills/lerianstudio/ring/auditing-production-readiness"><img src="https://agentmods.dev/badge/skills/lerianstudio/ring/auditing-production-readiness/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/lerianstudio/ring/auditing-production-readiness"><img src="https://agentmods.dev/badge/skills/lerianstudio/ring/auditing-production-readiness.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00067 | $0.02208 |
| Opus 5 | $0.00034 | $0.01104 |
| Sonnet 5 | $0.00013 | $0.00442 |
| Haiku 4.5 | $0.00007 | $0.00221 |
Grade A, and why
ring:auditing-production-readiness scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 168 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
| Category | Count | Dimensions |
|---|---|---|
| A: Code Structure | 11 | Pagination, Errors, Routes, Bootstrap, Runtime, Core Deps, Naming, Domain Modeling, Nil Safety, API Versioning, Resource Leaks |
| B: Security | 9 (+1c) | Auth, IDOR, SQL, Input Validation, Secret Scanning, Data Encryption, Rate Limiting, CORS, Multi-Tenant* |
| C: Operations | 7 | Telemetry, Health, Config, Connections, Logging, Resilience, Graceful Degradation |
| D: Quality | 10 | Idempotency, API Docs, Tech Debt, Testing, Dependencies, Performance, Concurrency, Migrations, Linting, Caching |
| E: Infrastructure | 6 | Containers, HTTP Hardening, CI/CD, Async, Makefile, License |
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 168 lines · 67 tokens per session scan A aba09e1f9c41
ring:auditing-production-readiness is a skill published in the GitHub repository LerianStudio/ring (211 stars, last pushed 21d ago), licensed Apache-2.0. It adds 67 tokens to every session and 2,208 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
calibration
Internal dynos-work skill. Agent calibration — generates project-specific agents, benchmarks them, promotes/demotes based on performance, and manages auditor mode transitions. Runs only when the user explicitly types /dynos-work:calibration; never auto-triggered from conversation.
maintain
Internal dynos-work skill. Autonomous maintenance worker. Scans the repo for technical debt, security anti-patterns, and architectural drift; on a finding it creates a repair branch, implements the fix, verifies with audits/tests, and opens a Pull Request. Runs only when the user explicitly types /dynos-work:maintain…
ds-brief
Data-backed brief — research, source, double-verify, and render a visually rich single-file HTML report. Use when the user wants a sourced, fact-checked brief or report on a topic.
ds-ship
Ship orchestrator — classify the project, pick a mode (improve/release/launch/maintain), delegate only the skills the project's signals justify, consolidate findings, produce an audit report. Use for an end-to-end audit or ship pipeline across multiple skills.
ds-test
Universal test skill — generate, update, run, and fix tests for any stack. Use when writing, repairing, or running tests, or improving coverage.
local
Internal dynos-work skill. Manage the project daemon: start, stop, status, logs, run-once, dashboard. Runs only when the user explicitly types /dynos-work:local; never auto-triggered from conversation.