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Agent skill

System Evolution Review

by coleam00 in coleam00/skills

Performs a meta-level review of how well an implementation followed its plan, classifying divergences and recommending AI-Layer improvements.

MITAuto-check passedDevelopment

Install System Evolution Review

skills CLI
$ npx skills add coleam00/skills --skill system-evolution-review -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install coleam00/skills system-evolution-review --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/coleam00/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/system-evolution-review .claude/skills/system-evolution-review && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
system-evolution-review
GitHub stars
676
Token cost
~1.4k tokens
SKILL.md length
595 words
Files
1
Skills in repo
34
Repo updated
First seen
Licence
MIT

At a glance

Performs a meta-level review of how well an implementation followed its plan, classifying divergences and recommending AI-Layer improvements.

  • Works in 5 steps: Understand the Planned Approach → Understand the Actual Implementation → Classify Each Divergence → …
  • Tasks that involve Debugging
  • SKILL.md covers Purpose, Context & Inputs, Analysis Workflow and Output Format, plus 1 more section
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

System Evolution Review is an agent skill from coleam00/skills. Performs 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.

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Development, covering Debugging. The repository describes itself as: The agent skills I actually use to build software with coding agents. The PIV loop, planning, worktrees, and the meta-skills for building your own AI Layer. The licence is MIT.

When your agent uses it

  • Tasks that involve Debugging

Example prompts

  • “Use the system-evolution-review skill to perform a meta-level review of how well an implementation followed its plan, classifying divergences and…”
  • “/system-evolution-review”

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Understand the Planned Approach
  2. Understand the Actual Implementation
  3. Classify Each Divergence
  4. Trace Root Causes
  5. Generate Process Improvements

What it can do on your machine

Read from SKILL.md and the folder at commit 847be08. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are yaml).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

System Evolution Review loads about 1.4k tokens when it runs. Until then it costs about 61 tokens; SKILL.md has 595 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~61
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from coleam00/skills at commit 847be08, republished under its MIT licence (© coleam00). 595 words, ~1,369 tokens.

Download SKILL.mdSave it as .claude/skills/system-evolution-review/SKILL.md (or your agent's skills folder).
name
system-evolution-review
description
Performs 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.
argument-hint
[plan-file] [execution-report-file]
arguments
plan, report

System 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/piv-plan-implementation/SKILL.md

Generated Plan: Read this to understand what the agent was SUPPOSED to do. Plan file: $plan

Execute Skill: Read this to understand the execution process and what instructions guide implementation. .claude/skills/piv-implement/SKILL.md

Execution Report: Read this to understand what the agent ACTUALLY did and why. Execution report: $report

Analysis Workflow

Step 1: Understand the Planned Approach

Read the generated plan ($plan) 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 ($report) 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
Show full SKILL.md (223 more words)Show less

Output Format

Save your analysis to: .claude/system-reviews/[feature-name]-review.md

Report Structure:
Meta Information
  • Plan reviewed: [path to $plan]
  • Execution report: [path to $report]
  • 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:

yaml
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 ($plan):

  • 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

© coleam00, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .claude/skills/system-evolution-review of coleam00/skills.

Open the folder on GitHubat commit 847be08

Compare with similar skills

System Evolution Review next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

System Evolution Review compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
System Evolution Review this skillcoleam00/skills676—~1.4kAutomated safety check: PassMIT
Trellis Session Insightmindfold-ai/Trellis15k4 repos~1.7kAutomated safety check: PassAGPL-3.0
Native Data FetchingCherryHQ/cherry-studio-app4k6 repos~2.9kAutomated safety check: NotesMIT
Debugging Executionsn8n-io/n8n207k—~2.6kAutomated safety check: PassCustom licence
Aoti Debugpytorch/pytorch104k1 repos~1.7kAutomated safety check: PassCustom licence
Herdr Throwaway Reproductionherdrdev/herdr43k—~2.4kAutomated safety check: PassApache-2.0

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Categories

Questions about System Evolution Review

What does System Evolution Review do?

Performs a meta-level review of how well an implementation followed its plan, classifying divergences and recommending AI-Layer improvements. System Evolution Review is an agent skill from coleam00/skills. Performs a meta-level review of how well an implementation followed its plan, classifying divergences and recommending AI-Layer improvements.

When should I use System Evolution Review?

System Evolution Review fits situations like: tasks that involve Debugging.

How do I install System Evolution Review in Claude Code?

Run `npx skills add coleam00/skills --skill system-evolution-review -a claude-code`. Or copy the skill folder (.claude/skills/system-evolution-review in coleam00/skills) into .claude/skills/system-evolution-review in your project. Claude Code loads it when a task matches its description.

How do I install System Evolution Review in Codex?

Run `npx skills add coleam00/skills --skill system-evolution-review -a codex`. Or copy the skill folder (.claude/skills/system-evolution-review in coleam00/skills) into .agents/skills/system-evolution-review in your project. Codex loads it when a task matches its description.

Can I use System Evolution Review in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add coleam00/skills --skill system-evolution-review -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/system-evolution-review, .gemini/skills/system-evolution-review, .github/skills/system-evolution-review and .opencode/skills/system-evolution-review in your project.

What does System Evolution Review need to run?

SKILL.md names no scripts, command-line tools or credentials: System Evolution Review is instructions for the agent only.

Does System Evolution Review access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is System Evolution Review safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does System Evolution Review use?

System Evolution Review is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does System Evolution Review use?

About 1.4k tokens (SKILL.md is roughly 5.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to System Evolution Review?

Skills that share tags, products or a category with System Evolution Review: Trellis Session Insight (mindfold-ai/Trellis, 15k stars), Native Data Fetching (CherryHQ/cherry-studio-app, 4k stars), Debugging Executions (n8n-io/n8n, 207k stars) and Aoti Debug (pytorch/pytorch, 104k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains System Evolution Review?

coleam00 (a GitHub user) maintains it in coleam00/skills, which has 676 GitHub stars. The repository holds 34 skills in this directory. The repository was last updated on October 7, 2026.

Source: coleam00/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.