multi-reviewer-patterns

Overview of multi-reviewer-patterns skill

What multi-reviewer-patterns is for

The multi-reviewer-patterns skill gives an AI a structured way to run parallel code review across multiple quality dimensions, then merge the results into one usable review. Instead of asking for a single broad review and getting a mixed, uneven answer, this skill separates concerns like security, performance, architecture, testing, and accessibility so each review track can stay focused.

Who should use this skill

This multi-reviewer-patterns skill is best for people who need more than a quick lint-style pass:

• engineers reviewing non-trivial pull requests
• tech leads coordinating review quality across a team
• AI users who want multi-reviewer-patterns for Code Review instead of one generic reviewer
• teams handling changes that touch auth, data access, frontend UX, or system structure at the same time

If your change is tiny and low-risk, a normal single-pass review prompt may be faster.

The real job to be done

Most users do not need “more comments.” They need a review workflow that helps them:

• choose the right review dimensions
• avoid duplicate findings from overlapping reviewers
• keep severity consistent
• produce one final report a developer can act on

That is the practical value of multi-reviewer-patterns: it improves review organization, not just review volume.

What makes it different from a generic prompt

The biggest differentiator is that the skill encodes a review allocation pattern rather than only a review checklist. The repository includes:

• dimension selection guidance in SKILL.md
• detailed dimension-specific checklists in references/review-dimensions.md

That means the skill is useful both for planning who or what should review a change and for improving the consistency of the actual findings.

How to Use multi-reviewer-patterns skill

multi-reviewer-patterns install context

The upstream SKILL.md does not publish its own install command, so users typically add it from the parent skill repository context. If your environment supports Skills installation from GitHub, use the repository path for wshobson/agents and then invoke multi-reviewer-patterns from that installed set.

A common pattern is:

npx skills add https://github.com/wshobson/agents

Then use the multi-reviewer-patterns skill by name in your agent environment if that runtime exposes installed skills individually.

Read these files first

For a fast multi-reviewer-patterns guide, read in this order:

1. plugins/agent-teams/skills/multi-reviewer-patterns/SKILL.md
2. plugins/agent-teams/skills/multi-reviewer-patterns/references/review-dimensions.md

Why this order matters:

• SKILL.md tells you when to use the pattern and which dimensions exist
• references/review-dimensions.md gives the actual review checklists that improve output quality

If you skip the reference file, you may understand the workflow but still get shallow reviews.

What input the skill needs

The multi-reviewer-patterns usage quality depends heavily on the inputs you provide. At minimum, give the agent:

• the code diff or PR description
• affected files or modules
• change type: backend, frontend, infra, data, auth, API, UI
• risk areas you already suspect
• desired output format: findings list, consolidated report, or prioritized action plan

The skill becomes much more valuable when the agent knows what changed and which dimensions are likely relevant.

How to choose review dimensions well

Do not ask for every dimension by default. Pick dimensions based on the change:

• Security: auth, input handling, secrets, user-controlled data
• Performance: queries, hot paths, caching, memory-heavy flows
• Architecture: new modules, large refactors, coupling changes
• Testing: new behavior, regression risk, edge-case handling
• Accessibility: UI, forms, keyboard flow, screen-reader impact

This is where multi-reviewer-patterns for Code Review beats a generic review prompt: it helps avoid both under-review and noisy over-review.

Turn a rough goal into a strong prompt

Weak prompt:

“Review this PR with multi-reviewer-patterns.”

Stronger prompt:

“Use multi-reviewer-patterns to review this PR in parallel across Security, Performance, and Testing. Focus on changed files only. Deduplicate overlapping findings, assign severity consistently, and produce one final report with: issue, evidence, risk, and recommended fix. Changes include new login flow, token validation, and database query updates.”

Why this works better:

• names the review dimensions
• narrows scope
• requests consolidation
• asks for actionable reporting instead of raw reviewer notes

Recommended workflow in practice

A practical workflow for the multi-reviewer-patterns skill is:

1. summarize the change and affected surfaces
2. select 2 to 4 review dimensions
3. run dimension-specific review passes
4. merge and deduplicate findings
5. calibrate severity across dimensions
6. produce one final developer-facing report

This avoids the common failure mode where every reviewer repeats the same high-level concern in different words.

What good output should look like

Good multi-reviewer-patterns usage usually ends with a consolidated report that includes:

• finding title
• affected file or code area
• review dimension
• severity
• evidence from the change
• why it matters
• suggested fix or follow-up

If the output is just a long mixed list of comments, the skill was not used to its full value.

Use the checklist file deliberately

references/review-dimensions.md is the highest-value support file in this skill. It contains concrete checks such as:

• input validation and auth checks for security
• N+1 queries and pagination checks for performance
• test coverage and edge-case checks for testing

Use it to tell the agent how deep to go. For example:

“Use the Security checklist from references/review-dimensions.md, especially input handling, auth, and secrets checks, against the changed files.”

That produces more specific findings than “do a security review.”

Best-fit scenarios

The multi-reviewer-patterns skill is especially useful for:

• medium to large pull requests
• cross-cutting changes touching backend and frontend
• releases where review consistency matters
• AI-assisted review flows that need a final merged report
• teams trying to standardize review quality without creating a heavy process

Misfit scenarios

Skip multi-reviewer-patterns install or use it lightly when:

• the change is trivial and low-risk
• you only need one dimension, such as a pure accessibility pass
• you do not have enough code or change context to support real review
• you need formal static analysis rather than review heuristics

This skill improves review structure, but it does not replace tests, scanners, or human domain judgment.

multi-reviewer-patterns skill FAQ

Is multi-reviewer-patterns better than a normal review prompt

Usually yes for complex changes. A normal prompt often blends concerns together and gives inconsistent severity. multi-reviewer-patterns is better when you want specialized passes and one deduplicated final report.

Is the skill beginner-friendly

Yes, but beginners should keep scope narrow. Start with 2 dimensions, such as Testing plus Security, instead of trying every available review track. The checklist file makes the review criteria more concrete than a blank-prompt approach.

Do I need multiple agents to use multi-reviewer-patterns

Not necessarily. The pattern is useful even with one agent simulating separate review roles, then consolidating findings. If your environment supports true parallel agent workflows, the skill becomes even more natural.

What does this skill not do

The multi-reviewer-patterns skill does not automatically inspect runtime behavior, execute benchmarks, or verify production configuration. It is a structured review pattern, not a full validation pipeline.

When should I avoid using multi-reviewer-patterns

Avoid it when the overhead is larger than the change. For a one-line fix or a cosmetic rename, a focused ordinary prompt is usually faster and clearer.

How to Improve multi-reviewer-patterns skill

Give sharper change context

The fastest way to improve multi-reviewer-patterns usage is to stop asking for “a review” and instead specify:

• what changed
• what could break
• which dimensions matter
• what output format you want

A skill like this gets stronger as your scoping improves.

Reduce duplicate findings at the prompt level

If you know dimensions may overlap, tell the agent how to merge them:

“Combine duplicate findings from Security and Architecture. Keep the strongest evidence, choose one owner dimension, and note cross-dimension relevance only when it changes remediation.”

That instruction directly supports the skill’s main value proposition.

Ask for severity rules up front

Severity calibration is one of the hardest parts of multi-review output. Improve results by defining simple rules before the review starts, for example:

• Critical: exploitable security issue or data-loss risk
• High: likely production failure or serious user impact
• Medium: meaningful correctness or maintainability issue
• Low: minor improvement or edge-case concern

Without this, different review dimensions may score similar problems very differently.

Provide repository-specific standards

The reference checklist is useful, but the multi-reviewer-patterns skill gets better when you add your own constraints, such as:

• approved auth model
• performance budget
• testing expectations
• accessibility baseline
• architecture rules for module boundaries

This helps the agent judge the code against your standards rather than generic best practice alone.

Iterate after the first consolidated report

The first pass should not be the last pass. A strong follow-up prompt is:

“Re-run multi-reviewer-patterns on the top 3 findings only. Validate whether each is a true issue, reduce false positives, and rewrite fixes so they are implementation-ready.”

This improves trust and cuts noise before you share the review.

Common failure modes to watch for

Typical weak outputs include:

• every dimension reviewing the entire codebase instead of the change
• duplicated issues with different wording
• severity inflation
• generic advice with no code evidence
• accessibility or performance comments on changes that do not touch those areas

If you see these, the fix is usually better scoping, fewer dimensions, and clearer consolidation rules.

A strong prompt template to adapt

Use a prompt like this for higher-quality multi-reviewer-patterns guide workflows:

“Use multi-reviewer-patterns for this PR. Review only the changed files. Apply Security, Performance, and Testing dimensions. Use the relevant checklists from references/review-dimensions.md. Return a consolidated report with deduplicated findings, consistent severity, evidence, and recommended fixes. Exclude speculative issues unless they are clearly supported by the diff and PR context.”

This is usually much better than simply invoking the skill name and hoping the agent infers the workflow.