Ring:Reviewing Code logo

Ring:Reviewing Code

Organization
LerianStudio
ring:reviewing-code

Reviewing code by dispatching the default reviewer subagents in parallel (plus conditional specialists for lib-observability, lib-systemplane, or lib-streaming when the diff triggers them), then aggregating findings by severity into a report. Use as Gate 8 of ring:running-dev-cycle at epic cadence over the cumulative diff, or before merging. Report-only. Skip for a single-command Go pre-merge verdict (use ring:verifying-code).

Overview

PublisherLerianStudio
Repositoryring
Skill namering:reviewing-code
Stars
215
Forks
28
Bundled files
1
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.

  • 1 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:Reviewing Code 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/reviewing-code .claude/skills/lerianstudio-ring-reviewing-code
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Ring:Reviewing Code 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:Reviewing Code 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:Reviewing Code 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.

Code Review (Gate 8)

When to use

  • Gate 8 of development cycle
  • After completing major feature implementation
  • Before merge to main branch
  • After completing complex bug work

Skip when

  • Task is purely conversational or informational with no code changes
  • Changes are limited to documentation or comments with zero logic modifications
  • Code has not been modified since the last completed review cycle

Sequence

Runs after: ring:implementing-tasks Runs before: ring:validating-acceptance-criteria

Related

Complementary: ring:running-dev-cycle, ring:implementing-tasks

Dispatch the 9 default reviewer subagents in parallel, plus any triggered conditional specialists. Dispatch count is dynamic: 9 + triggered specialists, max 12. Do not say or imply all 12 always dispatch.

Announce at start: "Using ring:reviewing-code to dispatch 9 default reviewers plus triggered conditional specialists."

Report-only boundary: This skill does not remediate findings, dispatch implementation work, write comments into source files, generate external artifacts, invoke secondary review tools, or re-run reviewers automatically. It only dispatches the selected reviewers once and reports their findings in the current session.

Default Reviewers (Hard Gate)

#AgentFocus
1ring:code-reviewerArchitecture, design patterns, code quality
2ring:logic-reviewerDomain correctness, business rules, edge cases
3ring:security-reviewerVulnerabilities, authentication, OWASP risks
4ring:test-reviewerTest quality, coverage, edge cases, anti-patterns
5ring:nil-reviewerNil/null pointer safety for Go and TypeScript
6ring:dead-code-reviewerOrphaned code detection, reachability analysis
7ring:perf-reviewerPerformance hotspots, allocations, goroutine leaks, N+1
8ring:tenancy-reviewerMulti-tenant patterns, tenantId propagation, DB isolation
9ring:commons-reviewerlib-commons package usage and reinvented-wheel opportunities

Base hard gate: all 9 default reviewers must PASS.

Conditional Specialist Reviewers

Run these only when the diff matches their trigger. If triggered, include the specialist in aggregation and require PASS for overall PASS.

AgentTrigger
ring:obs-reviewerDiff touches tracing, metrics, logging, runtime recovery/panic safety, redaction, observability constants, or goroutines with recover/SafeGo implications.
ring:systemplane-reviewerDiff touches runtime config, hot-reload knobs, admin config surface, tenant-scoped settings, or systemplane imports/config.
ring:streaming-reviewerDiff touches business events, outbox, event producers, broker publishing, CloudEvents, or event manifests/catalogs.

Role Clarification

WhoResponsibility
This SkillSelect triggered specialists, dispatch reviewers once, aggregate findings, report all severities in-session
Reviewer AgentsAnalyze code, report issues with severity

Step 1: Gather Context (Auto-Detect if Not Provided)

Auto-detect: unit_id (generate if missing), base_sha (git merge-base HEAD main), head_sha (git rev-parse HEAD), implementation_files (git diff --name-only), implementation_summary (git log --oneline).

Display context banner before dispatching.

Step 2: Select Reviewers and Initialize Review State

Start with the 9 default reviewers. Inspect changed files and diff content to decide which conditional specialists are triggered. Track unit_id, base/head SHA, selected reviewer list, reviewer verdicts, and aggregated issues by severity: Critical, High, Medium, Low.

Step 3: Dispatch Selected Reviewers in Parallel

STOP-CHECK BEFORE DISPATCH

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

  • Count MUST equal 9 + triggered_specialists.
  • Count MUST be at least 9 and at most 12.
  • If count < 9 or count does not include every triggered specialist -> STOP. Reconcile against the default reviewer table and trigger table above.

MUST NOT trickle-dispatch

All selected reviewers leave in the SAME TURN, before reading any reviewer output.

Forbidden sequences:

  • Dispatch reviewer 1 -> read result -> dispatch reviewer 2
  • Dispatch a subset -> wait -> dispatch the rest
  • Dispatch conditional specialists after partial reviewer output
  • Loop sequentially over the reviewer list

If you find yourself about to dispatch a reviewer in a turn AFTER any reviewer has already returned a result -> STOP. You violated parallel dispatch. Report the violation to the user and mark the gate INCOMPLETE rather than completing the trickle.

Self-verify after dispatch

After the dispatch turn, verify all selected Task calls were emitted in that single turn. If fewer than selected reviewers went out, the gate did NOT execute correctly. Mark the run INCOMPLETE and surface the dispatch failure.

Parallel dispatch — atomic batch

Emit all selected Task calls in a SINGLE TURN, as one atomic batch.

If your runtime exposes a multi_tool_use.parallel wrapper, use it to dispatch the complete selected pool in one wrapped invocation. The STOP-CHECK, anti-trickle, and self-verify guards remain binding regardless of runtime.

Read reviewers/dispatch-prompts.md for the prompt templates. Inject:

  • Epic-level scope header (when scope=epic)
  • base_sha / head_sha from cumulative_diff_range when epic-level
  • Ring standards slice (cache-first per shared-patterns/standards-cache-protocol.md)
  • Explicit instruction that reviewers must report findings only and must not modify files

Step 4: Wait and Parse Output

Parse VERDICT and Issues for all selected reviewers. Normalize every issue into one of four severity buckets: Critical, High, Medium, Low.

For each issue, preserve:

  • Severity
  • Title or short description
  • File:line when provided
  • Reviewer
  • Evidence or reasoning
  • Recommendation

If a reviewer returns COSMETIC, map it to Low.

Step 5: Report Results In Session

Produce a detailed Markdown report in the current session. The report must include all Critical, High, Medium, and Low issues.

Do not dispatch any follow-up agent to remediate findings. Do not edit files. Do not create reports on disk. Do not open a browser. Report back only.

Completion Rules

  • Complete after all selected reviewer outputs are collected and summarized.
  • PASS means all 9 default reviewers completed with zero issues, and every triggered specialist also completed with zero issues.
  • ISSUES_FOUND means at least one Critical, High, Medium, or Low issue was reported by any selected reviewer.
  • INCOMPLETE means one or more selected reviewers did not return a parseable result.
  • Low issues are still reported; never omit them from the session report.
  • No automatic remediation, source-file changes, reviewer reruns, external artifacts, or secondary validation tools are part of this skill.

Red Flags — STOP

  • You are about to dispatch any non-reviewer agent.
  • You are about to edit source files.
  • You are about to create or open a separate report artifact.
  • You are about to invoke a secondary review or validation tool.
  • You are about to re-run reviewers without an explicit new user request.

All of these mean: stop and produce the session report instead.

Output Format

markdown
## Review Summary
**Status:** [PASS|ISSUES_FOUND|INCOMPLETE]
**Unit ID:** [unit_id]
**Base:** [base_sha]
**Head:** [head_sha]
**Scope:** [epic|branch|provided]
**Reviewers Dispatched:** [9-12]
**Conditional Specialists Triggered:** [none|list]

## Issues by Severity
| Severity | Count |
|----------|-------|
| Critical | N |
| High     | N |
| Medium   | N |
| Low      | N |

## Critical Issues
[List every Critical issue. If none: None.]

| Issue | File:Line | Reviewer | Evidence | Recommendation |
|-------|-----------|----------|----------|----------------|
| [actual issue description] | [file:line] | [ring:xxx-reviewer] | [why it matters] | [recommended action] |

## High Issues
[List every High issue. If none: None.]

## Medium Issues
[List every Medium issue. If none: None.]

## Low Issues
[List every Low issue. If none: None.]

## Reviewer Verdicts
| Reviewer | Verdict | Issues |
|----------|---------|--------|
| ring:code-reviewer | PASS/FAIL/INCOMPLETE | N |
[...selected reviewer rows]

## Report Boundary
No files were changed. No remediation agents were dispatched. No external report artifacts were generated.

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:Reviewing Code AI skill do?

Reviewing code by dispatching the default reviewer subagents in parallel (plus conditional specialists for lib-observability, lib-systemplane, or lib-streaming when the diff triggers them), then aggregating findings by severity into a report. Use as Gate 8 of ring:running-dev-cycle at epic cadence over the cumulative diff, or before merging. Report-only. Skip for a single-command Go pre-merge verdict (use ring:verifying-code).

Why use Ring:Reviewing Code on TypingMind?

Because you install it once and use it with any model. Ring:Reviewing Code 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:Reviewing Code in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/LerianStudio/ring/tree/main/default/skills/reviewing-code. 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:Reviewing Code?

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:Reviewing Code?

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

Is the Ring:Reviewing Code 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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