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

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 |

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.