✨ Consolidate and expand review agent set

[TechNickAI]

Summary

• Replaces the generic code-reviewer with 9 specialized, focused review agents
• Each agent has a single responsibility, reducing confusion about which to use
• Absorbs best ideas from external plugins (pr-review-toolkit, feature-dev) to become the canonical review agent source

Changes

Renamed

• code-reviewer → security-reviewer (now focused exclusively on OWASP top 10, vulnerabilities)

Added 8 New Specialized Agents

Agent	Focus	Absorbed From
observability-reviewer	Logging, Sentry, breadcrumbs, tracing patterns	New (fills gap)
style-reviewer	Conventions, formatting, project patterns	pr-review-toolkit:code-reviewer
logic-reviewer	Bugs, correctness, edge cases, null safety	feature-dev:code-reviewer
error-handling-reviewer	Silent failures, catch blocks, fallback behavior	pr-review-toolkit:silent-failure-hunter
simplifier	Reduce complexity while preserving functionality	pr-review-toolkit:code-simplifier
performance-reviewer	N+1 queries, re-renders, bundle size, algorithms	New (fills gap)
test-analyzer	Coverage gaps, test quality, brittle tests	pr-review-toolkit:pr-test-analyzer
comment-analyzer	Stale comments, accuracy, value assessment	pr-review-toolkit:comment-analyzer

Design Decisions

Why split the old code-reviewer? The original was a 165-line jack-of-all-trades that tried to cover security, bugs, style, performance, testing, and maintainability. Specialized agents are more focused, easier to invoke correctly, and produce more targeted feedback.

Why these 9 agents? Analysis of existing external plugins identified overlapping "code-reviewer" implementations with different focuses. By separating concerns:

• Users know exactly which agent to invoke
• Each agent can have deeper expertise in its domain
• Cross-referencing between agents is clearer

Confidence thresholds: All agents use 80%+ confidence thresholds to reduce noise and only report issues that truly matter.

Kept Unchanged

• architecture-auditor
• design-reviewer
• mobile-ux-reviewer
• test-engineer
• debugger
• git-writer
• prompt-engineer
• seo-specialist
• site-keeper
• ux-designer
• autonomous-developer
• test-runner

Testing

• Reviewed all agent files against frontmatter requirements (description < 75 chars)
• Verified prompt engineering best practices (goal-focused, no anti-patterns, consistent terminology)
• Confirmed single responsibility per agent

Notes

After merging, external plugins (pr-review-toolkit, feature-dev) can be uninstalled from projects since ai-coding-config now absorbs their best ideas.

🤖 Generated with Claude Code

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[claude]

Code Review: Specialized Review Agent Consolidation

This is a well-architected refactoring that significantly improves the clarity and usability of code review agents. The split from one monolithic agent into 9 focused specialists is a strong design decision.

Strengths

Excellent Single Responsibility Design ✅
Each agent has a clear, focused purpose. The separation eliminates confusion about which agent to invoke and allows each to develop deeper expertise in its domain. The 80%+ confidence threshold across agents is a smart pattern to reduce noise.

Strong Documentation Quality ✅
All agents follow consistent structure:

• Clear "What I Review" sections
• Explicit scope definitions
• Practical "What I Look For" guidance
• Cross-references to other agents
• Concrete output format specifications

The writing is clear, direct, and follows prompt engineering best practices from rules/prompt-engineering.mdc.

Good Frontmatter Compliance ✅
All agent descriptions meet the <75 character requirement and are action-oriented.

Thoughtful Coverage ✅
The new agents fill real gaps (observability-reviewer, performance-reviewer) that the old code-reviewer couldn't adequately address.

Issues Found

1. Code Examples Violate LLM Pattern Teaching Principles 🚨

Severity: High
Location: plugins/core/agents/observability-reviewer.md:48-71

The TypeScript code examples create a dangerous pattern-teaching issue. According to rules/prompt-engineering.mdc, LLMs encode patterns from what they see regardless of context. When another LLM reads this agent file, the TypeScript patterns will strongly anchor its responses to TypeScript, even when reviewing Python, Go, or Ruby code.

Evidence:

logger.info({ userId, action: 'checkout', cartId }, 'User initiated checkout')

This teaches the agent "structured logging looks like this TypeScript pattern." When reviewing Python code, the agent may struggle to recognize valid structured logging that uses different syntax.

Impact:

• Agents will be biased toward TypeScript patterns
• Reduced effectiveness when reviewing other languages
• Potential false negatives/positives based on syntax differences

Fix:
Replace language-specific examples with language-agnostic descriptions:

## Patterns I Validate

Structured logging: Context should be in structured fields separate from the message string. Include relevant IDs (user, request, transaction) in the context object, not interpolated into the message.

Error tracking: Attach relevant context before capturing exceptions. Preserve stack traces and include related identifiers.

Breadcrumbs: Record user actions leading to errors with categorization and descriptive messages.

Request correlation: Use child loggers or context propagation to maintain request/trace IDs through async operations.

Same issue affects:

• observability-reviewer.md:48-71 (4 TypeScript examples)
• Any other agents with language-specific code (didn't spot others but worth verifying)

2. Missing Test Coverage for Critical Functionality ⚠️

Severity: Medium
Files: All new agent files

There are no automated tests validating that these agents work as intended. While these are markdown prompt files (not executable code), their quality is critical since they guide AI behavior.

Suggestions:

• Integration tests that invoke agents on sample code and validate output quality
• Test cases covering each agent's specialty (security issues for security-reviewer, etc.)
• Regression tests for the confidence threshold behavior

Not blocking for merge, but important for long-term maintainability.

3. Version Bump Inconsistency (Minor) 💡

Location: .claude-plugin/marketplace.json

The marketplace version jumps from 8.1.0 → 9.0.0 (major bump), and core plugin from 7.1.0 → 8.0.0 (major bump). Both are major version changes, which is appropriate for this breaking change (removing code-reviewer). The inconsistency in version numbers between marketplace and core plugin is a bit confusing but not incorrect.

Suggestion: Document the versioning strategy - why marketplace is at v9 while core is at v8. Are they intended to track together or independently?

Architecture Review

Plugin Absorption Strategy ✅
The decision to absorb functionality from external plugins (pr-review-toolkit, feature-dev) into this canonical source is sound. It reduces dependency fragmentation and gives users one authoritative source.

Agent Naming ✅
Names are clear and follow consistent -reviewer / -analyzer / -engineer patterns. The exception is simplifier which is appropriately distinct.

Cross-References ✅
Agents consistently reference each other in "What I Skip" sections, creating a clear separation of concerns.

Recommendations

Before Merge:

1. [Required] Fix the TypeScript code examples in observability-reviewer.md to use language-agnostic descriptions

After Merge:

1. Add integration tests for agent quality validation
2. Document the versioning strategy for marketplace vs core plugin
3. Consider a migration guide for users who had the old code-reviewer agent

Performance Considerations

No performance concerns. These are prompt files that will be read once per agent invocation. The file sizes are reasonable (<120 lines each).

Security Assessment

No security vulnerabilities identified. These agents don't execute code or handle sensitive data - they generate review feedback.

---

Overall Assessment: Strong refactoring with excellent separation of concerns. One critical fix needed for language-agnostic pattern teaching, otherwise ready to merge.

The specialized agent approach is significantly better than the monolithic code-reviewer. Users will have much clearer guidance on which agent to invoke for their specific needs.

[claude]

PR Review: Consolidate and expand review agent set

Overall Assessment

Verdict: Approve with minor observations ✅

This is an excellent refactoring that transforms a monolithic 165-line code-reviewer agent into 9 focused, single-responsibility agents. The separation of concerns is well-executed, and the agents follow strong prompt engineering principles from your prompt-engineering.mdc guidelines.

---

Strengths

1. Single Responsibility Design 🎯
Each agent has a clear, focused purpose. The separation between security-reviewer, logic-reviewer, error-handling-reviewer, and others eliminates the confusion of which agent to invoke for specific concerns.

2. Excellent Prompt Engineering 📝
All agents follow the project's prompt engineering best practices:

• Goal-focused rather than over-prescriptive
• Positive framing ("I find bugs" vs "DON'T write bugs")
• Descriptive over directive (no aggressive "CRITICAL" language)
• Clear structure with semantic headings
• Confidence thresholds (80%+) to reduce noise

3. Clear Cross-References 🔗
Each agent includes a "What I Skip" section that points users to the appropriate specialized agent for other concerns. This helps users understand the ecosystem.

4. Consistent Structure ✅
All 9 agents follow the same organizational pattern:

• What I Review
• Review Scope
• What I Look For / Core Principles
• Output Format
• What I Skip

This consistency makes the agents easy to understand and use.

5. Appropriate Version Bump 📦
The major version bump (8.1.0 → 9.0.0) correctly reflects the breaking change of removing code-reviewer.

---

Code Quality Observations

1. Description Length Compliance ✅
All frontmatter descriptions are under 75 characters as required:

• security-reviewer: 42 chars
• observability-reviewer: 37 chars
• style-reviewer: 40 chars
• logic-reviewer: 36 chars
• error-handling-reviewer: 47 chars
• simplifier: 51 chars
• performance-reviewer: 40 chars
• test-analyzer: 42 chars
• comment-analyzer: 44 chars

2. Color Coding 🎨
Colors provide useful visual categorization:

• Red (security) - critical/dangerous
• Yellow/Orange (error-handling, logic) - warnings
• Purple (performance, security) - optimization
• Cyan (observability, test) - quality metrics
• Green (simplifier, comment) - improvements
• Blue (style) - conventions

3. Confidence Thresholds 📊
Each agent appropriately filters noise:

• security-reviewer: 80%+
• logic-reviewer: 80%+
• style-reviewer: 80%+
• performance-reviewer: 80-89% (with 90-100% for critical)
• test-analyzer: 7/10+ (well-calibrated scale)

---

Minor Observations

1. Observability-Reviewer Specificity 🔍

The observability-reviewer references specific tools (Pino, Winston, Sentry, Datadog) which might not apply to all codebases. This is actually good - it shows the agent understands the ecosystem - but users might wonder if the agent still works without these tools.

Suggestion: Consider adding a brief note like "I adapt to whatever logging/monitoring tools your project uses" to set expectations.

2. Comment-Analyzer Scope 💭

The comment-analyzer is narrowly focused on comment quality, which is appropriate. However, some might expect it to also suggest adding comments to complex code. The current scope (reviewing existing comments only) is clear from the description.

3. Simplifier Philosophy 🧹

The simplifier has a great philosophy section about avoiding nested ternaries and over-abstraction. The principle "Abstractions should earn their existence" is gold. The balance section (lines 63-70) nicely prevents over-simplification.

4. Performance-Reviewer Scale Awareness ⚡

The performance-reviewer correctly asks "At what data size this becomes a problem?" This prevents premature optimization complaints while catching real issues.

---

Security Considerations

No security concerns. These are agent prompt files that guide LLM behavior. The security-reviewer itself appropriately focuses on OWASP top 10 and follows the principle of high confidence thresholds to avoid false positives.

---

Test Coverage

Not applicable - These are prompt files for agent behavior. The real test will be using these agents in practice to verify they provide focused, actionable feedback in their respective domains.

Recommendation: Consider dog-fooding these agents on a few test PRs before the full rollout to validate:

• Do users find the right agent for their needs?
• Is the feedback appropriately scoped?
• Are confidence thresholds calibrated correctly?

---

Performance Considerations

Token efficiency: Breaking the 165-line monolith into 9 focused agents (87-104 lines each) is actually more token-efficient when you only need specific expertise. Users invoke security-reviewer (88 lines) instead of code-reviewer (165 lines) when doing security reviews.

---

Documentation Impact

PR Description is Excellent 📚

The table showing which agents absorbed functionality from external plugins (pr-review-toolkit, feature-dev) provides clear migration guidance. Users can uninstall those plugins after adopting ai-coding-config 9.0.

Suggestion: Consider adding a migration guide document or blog post showing:

• "I used to invoke code-reviewer, which agent do I use now?"
• Mapping of old use cases to new specialized agents

---

Final Thoughts

This refactoring represents a maturation of the agent ecosystem. The single-responsibility approach will scale better as new review concerns emerge (accessibility-reviewer, i18n-reviewer, etc.). The consistent structure makes it easy to add new agents following the established pattern.

The prompt engineering quality is high - these agents will be effective teaching tools for LLMs executing them.

Recommendation: Merge ✅

---

Minor Nits (Optional)

1. Version consistency: marketplace.json shows core plugin version as 8.0.0 while marketplace version is 9.0.0. Is this intentional semantic versioning where the marketplace has its own version scheme?

2. Color collision: Both performance-reviewer and security-reviewer use purple. Consider using different colors for visual distinction (e.g., magenta for performance).

---

Great work on this refactoring! The specialized agents are a significant improvement over the monolithic approach.

🤖 Generated with Claude Code

[claude]

Code Review: Specialized Review Agent Set

This is an excellent architectural improvement. The shift from a monolithic code-reviewer to 9 specialized agents represents a significant quality upgrade with clear separation of concerns.

Strengths

1. Single Responsibility Principle
Each agent has a well-defined, focused purpose. The 165-line jack-of-all-trades is now 9 expert agents averaging ~90 lines each. This makes it clear which agent to invoke and what feedback to expect.

2. Excellent Prompt Engineering
The agent prompts follow best practices from rules/prompt-engineering.mdc:

• Goal-focused over prescriptive (agents describe what to review, not how)
• Consistent terminology throughout each agent
• No anti-patterns shown in examples
• Clear confidence thresholds (80%+) to reduce noise
• Positive framing ("I focus on X" vs "Don't do Y")

3. Well-Structured Agent Design
Each agent follows a consistent, clean structure:

• Clear "What I Review" section
• Explicit review scope (defaults to git diff)
• Focused analysis criteria
• Standardized output format
• Cross-references to other specialized agents

4. Smart Domain Coverage
The 9 agents comprehensively cover:

• Security (OWASP top 10)
• Logic correctness and bugs
• Style and conventions
• Error handling and silent failures
• Observability (logging, Sentry, tracing)
• Performance and efficiency
• Test coverage quality
• Code simplification
• Comment accuracy

Notably, observability-reviewer and performance-reviewer fill gaps that didn't exist before.

5. Proper Version Bump
Marketplace version 8.1.0 → 9.0.0 and plugin version 7.1.0 → 8.0.0 correctly reflect the breaking change from renaming/removing code-reviewer.

Minor Observations

1. Frontmatter Compliance
All descriptions are under 75 characters as required. Well done.

2. Color Choices
Each agent has a distinct color for CLI differentiation:

• security-reviewer: red (appropriate for critical issues)
• logic-reviewer: orange
• style-reviewer: blue
• error-handling-reviewer: yellow
• observability-reviewer: cyan
• performance-reviewer: magenta
• test-analyzer: cyan (⚠️ duplicates observability-reviewer)
• comment-analyzer: green (duplicates simplifier)
• simplifier: green

Minor improvement: test-analyzer and comment-analyzer have duplicate colors. Consider unique colors for better visual distinction in CLI output, though this is cosmetic.

3. Agent Cross-References
Excellent practice of directing users to specialized agents in "What I Skip" sections. This creates a clear agent ecosystem where users know exactly which tool to reach for.

4. Consistency with Existing Patterns
Maintained structure consistency with other agents like architecture-auditor, design-reviewer, etc. New developers can easily understand the agent patterns.

Security Considerations

✅ No concerns

• No hardcoded secrets or credentials
• Agent prompts appropriately scoped to their domains
• Security-reviewer correctly focuses exclusively on OWASP top 10 and vulnerabilities

Performance Considerations

✅ Well optimized

• Default scope of git diff prevents scanning entire codebases
• Confidence thresholds (80%+) reduce unnecessary processing
• Clear focus prevents agents from overlapping work

Test Coverage

Consideration: These are prompt files, not executable code, so traditional unit tests don't apply. However, the PR description mentions:

"Reviewed all agent files against frontmatter requirements (description < 75 chars)"

Suggestion: Consider adding an automated test that validates:

• Frontmatter schema (name, description, version, color)
• Description length < 75 chars
• Version format compliance
• No duplicate agent names

This would prevent regression as the agent set grows.

Documentation

✅ Excellent PR description
The PR summary clearly explains:

• Motivation for the change
• What was renamed vs added
• Design decisions with rationale
• Migration path for users (uninstall pr-review-toolkit, feature-dev)

Recommendations

1. Add Color Uniqueness Check
Consider making test-analyzer and comment-analyzer colors unique for better CLI UX.

2. Consider Agent Discovery Command
With 9 review agents, users might benefit from a command that suggests which agents to run based on file types or change patterns. For example:

• .ts/.tsx changes → suggest style-reviewer, logic-reviewer, test-analyzer
• Database queries → suggest performance-reviewer
• Error handling code → suggest error-handling-reviewer

This could be a follow-up enhancement.

3. Documentation Update
Ensure any documentation that referenced the old code-reviewer is updated to guide users to the appropriate specialized agent.

Conclusion

Strong approval ✅

This PR represents thoughtful architectural evolution:

• Eliminates confusion about which agent to use
• Improves focus and quality of reviews
• Follows project prompt engineering standards
• Fills gaps in observability and performance review
• Maintains consistency with existing patterns

The breaking change from code-reviewer → security-reviewer is well-justified and clearly communicated. Users will have a much better experience with focused, specialized agents that provide targeted, high-confidence feedback.

The only minor improvements would be unique colors for all agents and potential automated frontmatter validation, but these are enhancement opportunities, not blockers.

Excellent work on this refactoring! 🎉