Agent-Skills.md

Agent Skills: Unified Review Orchestration

'Use this skill when orchestrating multiple review types. Use when general

UncategorizedID: athola/claude-night-market/unified-review

Repository

athola/claude-night-market
atholaLicense: MIT
23423

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pnpm dlx add-skill https://github.com/athola/claude-night-market/tree/HEAD/plugins/pensive/skills/unified-review

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plugins/pensive/skills/unified-review/SKILL.md

Skill Metadata

Name
unified-review
Description
'Orchestrate multiple review types into a single multi-domain review.'

Table of Contents

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 | | Post-implementation audit | imbue:justify | High add/delete ratio, test changes, new abstractions |

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

Dispatch selected skills concurrently via the Agent tool. Use this mapping to resolve skill names to agent types:

| Skill Name | Agent Type | Notes | |---|---|---| | bug-review | pensive:code-reviewer | Covers bugs, API, tests | | api-review | pensive:code-reviewer | Same agent, API focus | | test-review | pensive:code-reviewer | Same agent, test focus | | architecture-review | pensive:architecture-reviewer | ADR compliance | | rust-review | pensive:rust-auditor | Rust-specific | | code-refinement | pensive:code-refiner | Duplication, quality | | math-review | general-purpose | Prompt: invoke Skill(pensive:math-review) | | makefile-review | general-purpose | Prompt: invoke Skill(pensive:makefile-review) | | shell-review | general-purpose | Prompt: invoke Skill(pensive:shell-review) |

Rules:

  • Never use skill names as agent types (e.g., pensive:math-review is NOT an agent)
  • When pensive:code-reviewer covers multiple domains, dispatch once with combined scope
  • For skills without dedicated agents, use general-purpose and instruct it to invoke the Skill tool
  • Maintain consistent evidence logging across all agents
  • Track progress via TodoWrite

4. Integrate Findings

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

Deferred capture for backlog findings: Findings that are triaged to the backlog (out-of-scope for the current review or deferred by the team) should be preserved so they are not lost between review cycles. For each finding assigned to the backlog, run:

python3 scripts/deferred_capture.py \
  --title "<finding title>" \
  --source review \
  --context "Review dimension: <dimension>. <finding description>"

The <dimension> value should match the review skill that surfaced the finding (e.g. bug-review, api-review, architecture-review). This runs automatically after the action plan is finalised, without prompting the user.

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:

  1. Establish proper context
  2. Execute all selected skills successfully
  3. Document findings with evidence
  4. Prioritize recommendations by impact
  5. 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

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.