System Review
SkillDev toolsPerforms a meta-level review of how well an implementation followed its plan, classifying divergences and recommending AI-Layer improvements. Use after an execution report exists to find bugs in the process, not the code.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the System Review skill
What this skill tells your AI
The instructions your AI receives, as published by coleam00/ai-native-starter-pack in .claude/skills/system-review/SKILL.md and read by ahel’s review.
Perform a meta-level analysis of how well the implementation followed the plan and identify process improvements.
Purpose
System review is NOT code review. You're not looking for bugs in the code - you're looking for bugs in the process.
Your job:
- Analyze plan adherence and divergence patterns
- Identify which divergences were justified vs problematic
- Surface process improvements that prevent future issues
- Suggest updates to AI-Layer assets (CLAUDE.md, plan templates, skills)
Philosophy:
- Good divergence reveals plan limitations → improve planning
- Bad divergence reveals unclear requirements → improve communication
- Repeated issues reveal missing automation → create skills
Context & Inputs
You will analyze four key artifacts:
Plan Skill:
Read this to understand the planning process and what instructions guide plan creation.
.claude/skills/plan-feature/SKILL.md
Generated Plan: Read this to understand what the agent was SUPPOSED to do. Plan file: $1
Execute Skill:
Read this to understand the execution process and what instructions guide implementation.
.claude/skills/execute/SKILL.md
Execution Report: Read this to understand what the agent ACTUALLY did and why. Execution report: $2
Analysis Workflow
Step 1: Understand the Planned Approach
Read the generated plan ($1) and extract:
- What features were planned?
- What architecture was specified?
- What validation steps were defined?
- What patterns were referenced?
Step 2: Understand the Actual Implementation
Read the execution report ($2) and extract:
- What was implemented?
- What diverged from the plan?
- What challenges were encountered?
- What was skipped and why?
Step 3: Classify Each Divergence
For each divergence identified in the execution report, classify as:
Good Divergence ✅ (Justified):
- Plan assumed something that didn't exist in the codebase
- Better pattern discovered during implementation
- Performance optimization needed
- Security issue discovered that required different approach
Bad Divergence ❌ (Problematic):
- Ignored explicit constraints in plan
- Created new architecture instead of following existing patterns
- Took shortcuts that introduce tech debt
- Misunderstood requirements
Step 4: Trace Root Causes
For each problematic divergence, identify the root cause:
- Was the plan unclear, where, why?
- Was context missing, where, why?
- Was validation missing, where, why?
- Was manual step repeated, where, why?
Step 5: Generate Process Improvements
Based on patterns across divergences, suggest:
- CLAUDE.md updates: Universal patterns or anti-patterns to document
- Plan skill updates: Instructions that need clarification or missing steps
- New skills: Manual processes that should be automated
- Validation additions: Checks that would catch issues earlier
Output Format
Save your analysis to: .claude/system-reviews/[feature-name]-review.md
Report Structure:
Meta Information
- Plan reviewed: [path to $1]
- Execution report: [path to $2]
- Date: [current date]
Overall Alignment Score: __/10
Scoring guide:
- 10: Perfect adherence, all divergences justified
- 7-9: Minor justified divergences
- 4-6: Mix of justified and problematic divergences
- 1-3: Major problematic divergences
Divergence Analysis
For each divergence from the execution report:
divergence: [what changed]
planned: [what plan specified]
actual: [what was implemented]
reason: [agent's stated reason from report]
classification: good ✅ | bad ❌
justified: yes/no
root_cause: [unclear plan | missing context | etc]
Pattern Compliance
Assess adherence to documented patterns:
- Followed codebase architecture
- Used documented patterns (from CLAUDE.md)
- Applied testing patterns correctly
- Met validation requirements
System Improvement Actions
Based on analysis, recommend specific actions:
Update CLAUDE.md:
- Document [pattern X] discovered during implementation
- Add anti-pattern warning for [Y]
- Clarify [technology constraint Z]
Update Plan Skill ($1):
- Add instruction for [missing step]
- Clarify [ambiguous instruction]
- Add validation requirement for [X]
Create New Skill:
- A new skill for [manual process repeated 3+ times]
Update Execute Skill:
- Add [validation step] to execution checklist
Key Learnings
What worked well:
- [specific things that went smoothly]
What needs improvement:
- [specific process gaps identified]
For next implementation:
- [concrete improvements to try]
Important
- Be specific: Don't say "plan was unclear" - say "plan didn't specify which auth pattern to use"
- Focus on patterns: One-off issues aren't actionable. Look for repeated problems.
- Action-oriented: Every finding should have a concrete asset update suggestion
- Suggest improvements: Don't just analyze - actually suggest the text to add to CLAUDE.md or skills
Signals
- GitHub stars
- 69
- Forks
- 23
- Last commit
- Aug 2026
Advanced
- Catalog kind
- skill
- Gateway key
system-review- Source
- github.com/coleam00/ai-native-starter-pack