•Quick Start
•When to Use
•Review Skill Selection Matrix
•Workflow
•1. Analyze Repository Context
•2. Select Review Skills
•3. Execute Reviews
•4. Integrate Findings
•Review Modes
•Auto-Detect (default)
•Focused Mode
•Full Review Mode
•Quality Gates
•Deliverables
•Executive Summary
•Domain-Specific Reports
•Integrated Action Plan
•Modular Architecture
•Exit Criteria

Unified Review Orchestration

Intelligently selects and executes appropriate review skills based on codebase analysis and context.

Quick Start

bash

# Auto-detect and run appropriate reviews
/full-review

# Focus on specific areas
/full-review api          # API surface review
/full-review architecture # Architecture review
/full-review bugs         # Bug hunting
/full-review tests        # Test suite review
/full-review all          # Run all applicable skills

Verification: Run pytest -v to verify tests pass.

When To Use

•Starting a full code review
•Reviewing changes across multiple domains
•Need intelligent selection of review skills
•Want integrated reporting from multiple review types
•Before merging major feature branches

When NOT To Use

•
Specific review type known
- •use bug-review
•Test-review
•Architecture-only focus - use architecture-review
•
Specific review type known
- •use bug-review

Review Skill Selection Matrix

Codebase Pattern	Review Skills	Triggers
Rust files (`*.rs`, `Cargo.toml`)	rust-review, bug-review, api-review	Rust project detected
API changes (`openapi.yaml`, `routes/`)	api-review, architecture-review	Public API surfaces
Test files (`test_.py`, `_test.go`)	test-review, bug-review	Test infrastructure
Makefile/build system	makefile-review, architecture-review	Build complexity
Mathematical algorithms	math-review, bug-review	Numerical computation
Architecture docs/ADRs	architecture-review, api-review	System design
General code quality	bug-review, test-review	Default review

Workflow

1. Analyze Repository Context

•Detect primary languages from extensions and manifests
•Analyze git status and diffs for change scope
•Identify project structure (monorepo, microservices, library)
•Detect build systems, testing frameworks, documentation

2. Select Review Skills

python

# Detection logic
if has_rust_files():
    schedule_skill("rust-review")
if has_api_changes():
    schedule_skill("api-review")
if has_test_files():
    schedule_skill("test-review")
if has_makefiles():
    schedule_skill("makefile-review")
if has_math_code():
    schedule_skill("math-review")
if has_architecture_changes():
    schedule_skill("architecture-review")
# Default
schedule_skill("bug-review")

Verification: Run pytest -v to verify tests pass.

3. Execute Reviews

•Run selected skills concurrently
•Share context between reviews
•Maintain consistent evidence logging
•Track progress via TodoWrite

4. Integrate Findings

•Consolidate findings across domains
•Identify cross-domain patterns
•Prioritize by impact and effort
•Generate unified action plan

Review Modes

Auto-Detect (default)

Automatically selects skills based on codebase analysis.

Focused Mode

Run specific review domains:

•/full-review api → api-review only
•/full-review architecture → architecture-review only
•/full-review bugs → bug-review only
•/full-review tests → test-review only

Full Review Mode

Run all applicable review skills:

•/full-review all → Execute all detected skills

Quality Gates

Each review must:

•Establish proper context
•Execute all selected skills successfully
•Document findings with evidence
•Prioritize recommendations by impact
•Create action plan with owners

Deliverables

Executive Summary

•Overall codebase health assessment
•Critical issues requiring immediate attention
•Review frequency recommendations

Domain-Specific Reports

•API surface analysis and consistency
•Architecture alignment with ADRs
•Test coverage gaps and improvements
•Bug analysis and security findings
•Performance and maintainability recommendations

Integrated Action Plan

•Prioritized remediation tasks
•Cross-domain dependencies
•Assigned owners and target dates
•Follow-up review schedule

Modular Architecture

All review skills use a hub-and-spoke architecture with progressive loading:

•pensive:shared: Common workflow, output templates, quality checklists
•Each skill has modules/: Domain-specific details loaded on demand
•Cross-plugin deps: imbue:evidence-logging, imbue:diff-analysis/modules/risk-assessment-framework

This reduces token usage by 50-70% for focused reviews while maintaining full capabilities.

Exit Criteria

•All selected review skills executed
•Findings consolidated and prioritized
•Action plan created with ownership
•Evidence logged per pensive:shared/modules/output-format-templates

Troubleshooting

Common Issues

If the auto-detection fails to identify the correct review skills, explicitly specify the mode (e.g., /full-review rust instead of just /full-review). If integration fails, check that TodoWrite logs are accessible and that evidence files were correctly written by the individual skills.

unified-review

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