Table of Contents

• 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

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

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:proof-of-work , 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 structured output format

Supporting Modules

• Review workflow core - standard 5-step workflow pattern for all pensive reviews

• Output format templates - finding entry, severity, action item templates

• Quality checklist patterns - pre-review, analysis, evidence, deliverable checklists

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.