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Review Verification Protocol

Organization
existential-birds
review-verification-protocol

Mandatory verification steps for all code reviews to reduce false positives. Load this skill before reporting ANY code review findings.

Overview

Publisherexistential-birds
Repositorybeagle
Skill namereview-verification-protocol
Stars
82
Forks
8
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by existential-birds on GitHub. Read the source before you install it.

Installation

Install the Review Verification Protocol 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/existential-birds/beagle.git /tmp/beagle
mkdir -p .claude/skills
cp -r /tmp/beagle/plugins/beagle-core/skills/review-verification-protocol .claude/skills/review-verification-protocol
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Review Verification Protocol 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 Review Verification Protocol 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 Review Verification Protocol 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.

Review Verification Protocol

This protocol MUST be followed before reporting any code review finding. Skipping these steps leads to false positives that waste developer time and erode trust in reviews.

Anti-confabulation (gate 0 — applies to ALL review/verify skills)

Before issuing any verdict — confirm, reject, sever, fix, or adjudicate — you MUST echo the exact artifact you are judging, quoted from a source you read in this turn:

  • For a code finding: the file:line plus the cited code, read freshly now (not recalled from earlier in the session).
  • For a diff review: the actual diff hunk under review.
  • For a structured report (e.g. verify-llm-artifacts adjudicating findings[]): the finding's id + file + line + description, printed from the parsed source file, not from memory.

The artifact is the only source of truth. Never infer what you are reviewing from the branch name, the working directory, surrounding files, or recollection. If your mental model differs from the freshly read source, the source wins. A verdict issued without a same-turn echo of its target is invalid — emit the echo first, or do not emit the verdict.

This gate exists because an LLM under contextual priming will confidently adjudicate things that are not in the file. It runs before the per-finding hard gates below. Skills that consume this protocol implement it concretely: verify-llm-artifacts (Load + ECHO + ID-lock gate), review-llm-artifacts (echo finding before writing JSON), llm-artifacts-detection (anchor FILE:LINE from an opened buffer).

Hard gates (sequence)

Apply once per finding before it may appear in the review. If a gate fails, omit the finding, downgrade to Informational (per Severity Calibration), or rephrase as a question—do not ship soft accusations.

StepWhat you doPass condition (objective)
1. AnchorRead the full enclosing symbol or module, not only the diff hunk.You can state file path and line range (or symbol name + file) you are judging.
2. EvidenceFor this finding’s type, run the checks in Verification by Issue Type.Each required check has an artifact: pasted tool output, file:line citation, or explicit "none" / "N matches" after a repo search—not a claim you "looked."
3. SeverityAssign severity using Severity Calibration.Label matches the table; requests for net-new code that did not exist in scope are Informational only.
4. FormatDraft the finding for the report.Matches [FILE:LINE] ISSUE_TITLE; Informational items do not add to the actionable count.

Style-only or preference items must fail gate 2 or map to Do NOT Flag At All—they do not get a severity.

Pre-Report Verification Checklist

Before flagging ANY issue, verify (these items are what gate 2 must produce evidence for):

  • I read the actual code - Not just the diff context, but the full function/class
  • I searched for usages - Before claiming "unused", searched all references
  • I checked surrounding code - The issue may be handled elsewhere (guards, earlier checks)
  • I verified syntax against current docs - Framework syntax evolves (Tailwind v4, TS 5.x, React 19)
  • I distinguished "wrong" from "different style" - Both approaches may be valid
  • I considered intentional design - Checked comments, project conventions (e.g. AGENTS.md or CLAUDE.md), architectural context

Verification by Issue Type

"Unused Variable/Function"

Before flagging, you MUST:

  1. Search for ALL references in the codebase (grep/find)
  2. Check if it's exported and used by external consumers
  3. Check if it's used via reflection, decorators, or dynamic dispatch
  4. Verify it's not a callback passed to a framework

Common false positives:

  • State setters in React (may trigger re-renders even if value appears unused)
  • Variables used in templates/JSX
  • Exports used by consuming packages

"Missing Validation/Error Handling"

Before flagging, you MUST:

  1. Check if validation exists at a higher level (caller, middleware, route handler)
  2. Check if the framework provides validation (Pydantic, Zod, TypeScript)
  3. Verify the "missing" check isn't present in a different form

Common false positives:

  • Framework already validates (FastAPI + Pydantic, React Hook Form)
  • Parent component validates before passing props
  • Error boundary catches at higher level

"Type Assertion/Unsafe Cast"

Before flagging, you MUST:

  1. Confirm it's actually an assertion, not an annotation
  2. Check if the type is narrowed by runtime checks before the point
  3. Verify if framework guarantees the type (loader data, form data)

Valid patterns often flagged incorrectly:

typescript
// Type annotation, NOT assertion
const data: UserData = await loader()

// Type narrowing makes this safe
if (isUser(data)) {
  data.name  // TypeScript knows this is User
}

"Potential Memory Leak/Race Condition"

Before flagging, you MUST:

  1. Verify cleanup function is actually missing (not just in a different location)
  2. Check if AbortController signal is checked after awaits
  3. Confirm the component can actually unmount during the async operation

Common false positives:

  • Cleanup exists in useEffect return
  • Signal is checked (code reviewer missed it)
  • Operation completes before unmount is possible

"Performance Issue"

Before flagging, you MUST:

  1. Confirm the code runs frequently enough to matter (render vs click handler)
  2. Verify the optimization would have measurable impact
  3. Check if the framework already optimizes this (React compiler, memoization)

Do NOT flag:

  • Functions created in click handlers (runs once per click)
  • Array methods on small arrays (< 100 items)
  • Object creation in event handlers

Severity Calibration

Critical (Block Merge)

ONLY use for:

  • Security vulnerabilities (injection, auth bypass, data exposure)
  • Data corruption bugs
  • Crash-causing bugs in happy path
  • Breaking changes to public APIs

Major (Should Fix)

Use for:

  • Logic bugs that affect functionality
  • Missing error handling that causes poor UX
  • Performance issues with measurable impact
  • Accessibility violations

Minor (Consider Fixing)

Use for:

  • Code clarity improvements
  • Documentation gaps
  • Inconsistent style (within reason)
  • Non-critical test coverage gaps

Informational (No Action Required)

Use for:

  • Improvements that require adding new dependencies or modules
  • Suggestions for net-new code that didn't exist in the codebase before (new modules, test suites, abstractions)
  • Architectural ideas for future consideration
  • Test infrastructure suggestions (new mock libraries, behaviour extraction)
  • Optimizations without measurable impact in the current context

These are NOT review blockers. They should be noted for the author's awareness but must not appear in the actionable issue count. The Verdict should ignore informational items entirely.

Do NOT Flag At All

  • Style preferences where both approaches are valid
  • Optimizations with no measurable benefit
  • Test code not meeting production standards (intentionally simpler)
  • Library/framework internal code (shadcn components, generated code)
  • Hypothetical issues that require unlikely conditions

Valid Patterns (Do NOT Flag)

TypeScript

PatternWhy It's Valid
map.get(key) || []Map.get() returns T | undefined, fallback is correct
Class exports without separate type exportClasses work as both value and type
as const on literal arraysCreates readonly tuple types
Type annotation on variable declarationNot a type assertion
satisfies instead of asType checking without assertion

React

PatternWhy It's Valid
Array index as key (static list)Valid when: items don't reorder, list is static, no item identity needed
Inline arrow in onClickValid for non-performance-critical handlers (runs once per click)
State that appears unusedMay be set via refs, external callbacks, or triggers re-renders
Empty dependency array with refsRefs are stable, don't need to be dependencies
Non-null assertion after checkTypeScript narrowing may not track through all patterns

Testing

PatternWhy It's Valid
toHaveTextContent without regexHandles nested text correctly
Mock at module levelDefined once, not duplicated
Index-based test dataTests don't need stable identity
Simplified error messagesTest clarity over production polish

General

PatternWhy It's Valid
+? lazy quantifier in regexPrevents over-matching, correct for many patterns
Direct string concatenationSimpler than template literals for simple cases
Multiple returns in functionCan improve readability
Comments explaining "why"Better than no comments

Context-Sensitive Rules

React Keys

Flag array index as key ONLY IF ALL of these are true:

  • Items CAN be reordered (sortable list, drag-drop)
  • Items CAN be inserted/removed from middle
  • Items HAVE stable identifiers available (id, uuid)
  • The list is NOT completely replaced atomically

useEffect Dependencies

Flag missing dependency ONLY IF:

  • The value actually changes during component lifetime
  • Stale closure would cause incorrect behavior
  • The value is NOT a ref (refs are stable)
  • The value is NOT a stable callback (useCallback with empty deps)

Error Handling

Flag missing try/catch ONLY IF:

  • No error boundary catches this at a higher level
  • The framework doesn't handle errors (loader errorElement)
  • The error would cause a crash, not just a failed operation
  • User needs specific feedback for this error type

Before Submitting Review

Final verification: 0. Each finding passed Anti-confabulation (gate 0) — its target was echoed from a source read in this turn, not recalled or inferred.

  1. Each finding passed Hard gates (sequence) (anchor, evidence with artifacts, severity, format).
  2. Re-read each finding and ask: "Did I verify this is actually an issue?"
  3. For each finding, can you point to the specific line that proves the issue exists?
  4. Would a domain expert agree this is a problem, or is it a style preference?
  5. Does fixing this provide real value, or is it busywork?
  6. Format every finding as: [FILE:LINE] ISSUE_TITLE
  7. For each finding, ask: "Does this fix existing code, or does it request entirely new code that didn't exist before?" If the latter, downgrade to Informational.
  8. If this is a re-review: ONLY verify previous fixes. Do not introduce new findings.

If uncertain about any finding, either:

  • Remove it from the review
  • Mark it as a question rather than an issue
  • Verify by reading more code context

Frequently asked questions

What does the Review Verification Protocol AI skill do?

Mandatory verification steps for all code reviews to reduce false positives. Load this skill before reporting ANY code review findings.

Why use Review Verification Protocol on TypingMind?

Because you install it once and use it with any model. Review Verification Protocol 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 Review Verification Protocol in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/existential-birds/beagle/tree/main/plugins/beagle-core/skills/review-verification-protocol. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Review Verification Protocol?

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 Review Verification Protocol?

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

Is the Review Verification Protocol AI skill free?

Yes. It is published on GitHub by existential-birds 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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