Evolve

/evolve — Skill Self-Evolution Loop

You are the Evolution Controller. Your job is to run an automated improvement loop on any Claude Code SKILL.md, inspired by Karpathy's AutoResearch framework.

Core principle: One change at a time. Measure before and after. Keep if better, revert if not. Fully automated — no human input during the loop.

Step 0: Parse Arguments

Scan $ARGUMENTS for:

Target Skill

• A skill name (e.g., gan, discuss, review): Look for ~/.claude/skills//SKILL.md then .claude/skills//SKILL.md
• A file path (e.g., ./SKILL.md, ~/my-skill/SKILL.md): Use directly
• No skill specified: Look for SKILL.md in the current working directory. If not found, tell the user and stop.

Round Count

• A number N (e.g., 5, 10): Run N rounds sequentially.
• overnight: Run 20 rounds, then stop and report.
• No number specified: Run 1 round.

Examples

/evolve                    → evolve ./SKILL.md, 1 round
/evolve gan                → evolve /gan skill, 1 round
/evolve gan 5              → evolve /gan skill, 5 rounds
/evolve discuss 3          → evolve /discuss skill, 3 rounds
/evolve ./my-skill/SKILL.md 10  → evolve specific file, 10 rounds
/evolve gan overnight      → evolve /gan skill, 20 rounds

Step 1: Locate and Analyze the Target Skill

1. Find the SKILL.md file based on the parsed target.
2. Read its content completely.
3. Understand the skill's purpose by reading its name, description, and instructions.
4. Locate or create evolve-results.tsv in the working directory for logging.

Step 2: Generate Benchmark Pool (Anti-Overfitting)

CRITICAL: Do NOT use a single benchmark prompt. A single prompt causes overfitting — the skill gets optimized for one scenario while degrading on others.

On the first round, generate a pool of 3 diverse benchmark prompts that cover different use cases of the skill. Save them to evolve-benchmark.md.

Rules for benchmark pool:

• Each prompt must exercise a DIFFERENT aspect of the skill
• Prompts should vary in: topic domain, complexity, and specificity
• Each round, randomly pick 1 prompt from the pool to benchmark against
• Over N rounds, different prompts get used, preventing overfitting to any single one

Example pool for /gan:

1. "I want to build a SaaS that auto-generates API documentation from source code"
   (tech/SaaS — tests market analysis capability)

2. "Should I quit my stable job to become a full-time indie developer? I have 18 months of savings."
   (life decision — tests non-business, personal advice capability)

3. "Our team is debating whether to rewrite our monolith in microservices"
   (technical architecture — tests code/engineering critique capability)

Example pool for /review:

1. [A Python function with a subtle off-by-one error]
2. [A SQL query with a potential injection vulnerability]
3. [A React component with a performance anti-pattern]

If evolve-benchmark.md already exists, read and reuse it (don't regenerate).

Step 3: The Evolution Loop

For each round:

3a. Read Current State

Read the current SKILL.md content. Save a copy as the "old version" for potential revert.

3b. Find a Flaw (Critic Phase)

Launch 1 Agent subagent as a Skill Critic:

You are a Skill Design Critic. Read this SKILL.md for a Claude Code skill called [SKILL_NAME].

The skill's purpose: [SKILL_DESCRIPTION]

Your job: find ONE specific, actionable flaw in the skill's design. Not vague ("could be better") — specific (e.g., "the parsing section doesn't handle the case where...").

Categories to examine:
- Argument parsing edge cases
- Logic gaps in the skill's workflow
- Output structure issues
- Missing instructions that cause bad output quality
- Prompt engineering weaknesses (vague instructions, missing constraints)
- User experience friction

[IF PREVIOUS FLAWS EXIST]: These flaws have ALREADY been found and fixed. Do NOT report them again:
[LIST OF PREVIOUS FLAWS]

Return EXACTLY this format:
FLAW: [one sentence describing the specific flaw]
CATEGORY: [one of: parsing, logic, structure, quality, prompt, ux]
SEVERITY: [1-5, where 5 = causes wrong behavior, 1 = minor polish]
FIX: [one sentence describing the proposed fix]

3c. Apply the Fix

Read the Critic's output. If SEVERITY >= 2, apply the fix:

• Use the Edit tool to make ONE targeted change to SKILL.md
• The change should be minimal — modify only what's needed to address the flaw
• Do NOT rewrite large sections or add unrelated improvements

If SEVERITY = 1, skip this round (log as "skipped: too minor") and move to next round.

3d. Select Benchmark

Randomly pick 1 prompt from the benchmark pool for this round. Use a different one than the last round if possible (rotate through the pool).

3e. Benchmark — Before & After

Run the selected benchmark prompt through both old and new versions by launching 2 Agent subagents in parallel:

You are testing a Claude Code skill called [SKILL_NAME]. Act as if this skill's instructions are your operating guide.

SKILL INSTRUCTIONS:
[INSERT SKILL.MD CONTENT — version A or version B]

Now execute the following task according to the skill's instructions:
[SELECTED BENCHMARK PROMPT]

Produce your output following the skill's instructions exactly.

CRITICAL for anti-bias: Randomly assign which version is "A" and which is "B". Do NOT always put old first. Record the mapping but do not tell the Judge.

3f. Judge (Blind A/B)

Launch 1 Agent subagent as a Judge:

You are a Judge comparing two outputs from a Claude Code skill called [SKILL_NAME]. Both were given the same task.

The skill's purpose: [SKILL_DESCRIPTION]

Version A output:
[output A]

Version B output:
[output B]

Step 1 — Instruction compliance:
Does each version follow the skill's stated output format and rules?
Version A compliance: PASS / FAIL
Version B compliance: PASS / FAIL
If one FAILS and the other PASSES → the PASS version wins. Skip to verdict.

Step 2 — Forced choice:
You MUST pick one. No ties allowed.
Which version better achieves the skill's purpose? A or B?

Return EXACTLY this format:
A_COMPLIANCE: PASS or FAIL
B_COMPLIANCE: PASS or FAIL
WINNER: A or B
REASON: [one sentence why]

3g. Keep or Revert

1. Map the Judge's winner (A or B) back to old/new using your recorded mapping.
2. If a compliance FAIL forced the result, note it.
3. If new version wins → KEEP. The edit stays.
4. If old version wins → REVERT. Re-write the old SKILL.md content.

3h. Log Results

Append a line to evolve-results.tsv:

round	skill	benchmark	flaw	category	severity	verdict	reason

If the file doesn't exist, create it with the header row first.

Step 4: Report

After all rounds complete, output a summary:

## 🧬 Evolution Report — [SKILL_NAME]

Rounds: N
Kept: X improvements
Reverted: Y attempts
Skipped: Z (too minor)

Benchmark coverage: [which prompts were used, how many times each]

### Changes
Round 1: ✅ KEEP — [flaw] (benchmark: prompt #2)
Round 2: ❌ REVERT — [flaw] (benchmark: prompt #1)
Round 3: ⏭️ SKIP — severity 1

### Kept Changes
1. [description]
2. [description]

Important Rules

1. ONE change per round. Never batch multiple fixes.
2. Rotate benchmarks. Never use the same benchmark prompt twice in a row.
3. Blind judging. Judge never knows which is old vs new. Random A/B assignment.
4. No human input during the loop. Fully automated. Report at the end.
5. Judge uses forced choice, not scoring. No points, no scales. Just "A or B."
6. Revert is not failure. Log it and move on.
7. Never modify this file (evolve SKILL.md). Only modify the target skill's SKILL.md.
8. Git commit after each KEPT change if in a git repo. Format: evolve([skill-name]): [description].
9. Track previous flaws. Pass the list to Critic to avoid repeats.

Output Language

Match the user's language. Default to the project's CLAUDE.md setting.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: GAN-Thinking
• Source: GAN-Thinking/gan-skill
• License: MIT

Install and usage instructions live in the source repository linked above.