Iterative Code Evolution

Systematically improve code through structured analysis-mutation-evaluation loops. Adapted from ALMA (Automated meta-Learning of Memory designs for Agentic systems). Use when iterating on code quality, optimizing implementations, debugging persistent issues, or evolving a design through multiple improvement cycles. Replaces ad-hoc "try and fix" with disciplined reflection, variant tracking, and principled selection of what to change next.

A structured methodology for improving code through disciplined reflect → mutate → verify → score cycles, adapted from the ALMA research framework for meta-learning code designs.

When to Use This Skill

• Iterating on code that isn't working well enough (performance, correctness, design)
• Optimizing an implementation across multiple rounds of changes
• Debugging persistent or recurring issues where simple fixes keep failing
• Evolving a system design through structured experimentation
• Any task where you've already tried 2+ approaches and need discipline about what to try next
• Building or improving prompts, pipelines, agents, or any "program" that benefits from iterative refinement

When NOT to Use This Skill

• Simple one-shot code generation (just write it)
• Mechanical tasks with clear solutions (refactoring, formatting, migrations)
• When the user has already specified exactly what to change

Core Concepts

The Evolution Loop

Every improvement cycle follows this sequence:

text

┌─────────────────────────────────────────────────────┐
│  1. ANALYZE  — structured diagnosis of current code │
│  2. PLAN     — prioritized, concrete changes        │
│  3. MUTATE   — implement the changes                │
│  4. VERIFY   — run it, check for errors             │
│  5. SCORE    — measure improvement vs. baseline     │
│  6. ARCHIVE  — log what was tried and what happened │
│                                                     │
│  Loop back to 1 with new knowledge                  │
└─────────────────────────────────────────────────────┘

The Evolution Log

Track all iterations in .evolution/log.json at the project root. This is the memory that makes each cycle smarter than the last.

json

{
  "baseline": {
    "description": "Initial implementation before evolution began",
    "score": 0.0,
    "timestamp": "2025-01-15T10:00:00Z"
  },
  "variants": {
    "v001": {
      "parent": "baseline",
      "description": "Added input validation and error handling",
      "changes_made": [
        {
          "what": "Added type checks on all public methods",
          "why": "Runtime crashes from malformed input in 3/10 test cases",
          "priority": "High"
        }
      ],
      "score": 0.6,
      "delta": "+0.6 vs parent",
      "timestamp": "2025-01-15T10:30:00Z",
      "learned": "Input validation was the primary failure mode — most other logic was sound"
    },
    "v002": {
      "parent": "v001",
      "description": "Refactored parsing logic to handle edge cases",
      "changes_made": [
        {
          "what": "Rewrote parse_input() to use state machine instead of regex",
          "why": "Regex approach failed on nested structures (seen in test cases 7,8)",
          "priority": "High"
        }
      ],
      "score": 0.85,
      "delta": "+0.25 vs parent",
      "timestamp": "2025-01-15T11:00:00Z",
      "learned": "State machine approach generalizes better than regex for this grammar"
    }
  },
  "principles_learned": [
    "Input validation fixes give the biggest early gains",
    "Regex-based parsing breaks on recursive structures — prefer state machines",
    "Small targeted changes score better than large rewrites"
  ]
}

The Process in Detail

Phase 1: ANALYZE — Structured Diagnosis

Before changing anything, perform a structured analysis of the current code and its outputs. This is the most important phase — it prevents wasted mutations.

Step 1 — Learn from past edits (skip on first iteration)

Review the evolution log. For each previous change:

• Did the score improve or degrade?
• What pattern made it succeed or fail?
• Extract 2-3 principles to adopt and 2-3 pitfalls to avoid

Step 2 — Component-level assessment

For each meaningful component (function, class, module, pipeline stage), label it:

Label	Meaning
Working	Produces correct output, no issues observed
Fragile	Works on happy path but fails on edge cases or specific inputs
Broken	Produces wrong output or errors
Redundant	Duplicates logic found elsewhere, adds complexity without value
Missing	A needed component that doesn't exist yet

For each label, write a one-line explanation of why — linked to specific test outputs or observed behavior.

Step 3 — Quality and coherence check

Look for cross-cutting issues:

• Data flow: Do components pass structured data to each other, or rely on implicit state?
• Error handling: Are errors caught and handled, or silently swallowed?
• Duplication: Is the same logic repeated in multiple places?
• Hardcoding: Are there magic numbers, hardcoded paths, or environment-specific assumptions?
• Generalization: Which parts would work on new inputs vs. which are overfitted to test cases?

Step 4 — Produce prioritized suggestions

Based on Steps 1-3, produce concrete changes. Each suggestion must have:

text

- PRIORITY: High | Medium | Low
- WHAT: Precise description of the change (code-level, not vague)
- WHY: Link to a specific observation from Steps 1-3
- RISK: What could go wrong if this change is made incorrectly

Rule: Every suggestion must link to an observation. No "this might help" suggestions — only changes grounded in something you actually saw in the code or outputs.

Rule: Limit to 3 suggestions per cycle. More than 3 changes at once makes it impossible to attribute improvement or regression to specific changes.

Phase 2: PLAN — Select What to Change

Pick 1-3 suggestions from the analysis. Selection principles:

• High priority first — fix broken things before optimizing working things
• One theme per cycle — don't mix unrelated changes (e.g., don't fix parsing AND refactor error handling in the same mutation)
• Prefer targeted over sweeping — a surgical change to one function beats a rewrite of three modules
• If stuck, explore — if the last 2+ cycles showed diminishing returns on the same component, pick a different component to modify (this is the ALMA "visit penalty" principle — don't keep grinding on the same thing)

Phase 3: MUTATE — Implement Changes

Write the new code. Key discipline:

• Change only what the plan says. Resist the urge to "fix one more thing" while you're in there.
• Preserve interfaces. Don't change function signatures or return types unless the plan explicitly calls for it.
• Comment the rationale. Add a brief comment near each change referencing the evolution cycle (e.g., # evo-v003: switched to state machine per edge case failures)

Phase 4: VERIFY — Run and Check

Execute the modified code against the same inputs/tests used for scoring.

If it crashes (up to 3 retries):

Use the reflection-fix protocol:

1. Read the full error traceback
2. Identify the root cause (not the symptom)
3. Fix only the root cause — do not make unrelated improvements
4. Re-run

After 3 failed retries, revert to parent variant and log the failure:

json

{
  "attempted": "Description of what was tried",
  "failure_mode": "The error that couldn't be resolved",
  "learned": "Why this approach doesn't work"
}

This failure data is valuable — it prevents re-attempting the same broken approach.

If it runs but produces wrong output:

Don't immediately retry. Go back to Phase 1 (ANALYZE) with the new outputs. The wrong output is diagnostic data.

Phase 5: SCORE — Measure Improvement

Compare the new variant's performance against its parent (not just the baseline). Scoring depends on context:

Context	Score Method
Tests exist	Pass rate: tests_passed / total_tests
Performance optimization	Metric delta (latency, throughput, memory)
Code quality	Weighted checklist (correctness, edge cases, readability)
User feedback	Binary: better/worse/same per the user's judgment
LLM/prompt output quality	Sample outputs graded against criteria

Always compute delta vs. parent. This is how you learn which changes help vs. hurt.

Phase 6: ARCHIVE — Log and Learn

Update .evolution/log.json:

1. Record the new variant with parent, description, changes, score, delta
2. Write a learned field: one sentence about what this cycle taught you
3. If the score improved, add the underlying principle to principles_learned
4. If the score degraded, add the failure mode to principles_learned as a pitfall

Variant Management

When to Branch vs. Modify

• Modify in place (same file, new version): When the change is clearly incremental (fixing a bug, adding a check, tuning a parameter)
• Branch (copy to a new file): When trying a fundamentally different approach (different algorithm, different architecture, different strategy)

Keep branches in .evolution/variants/ with descriptive names. The evolution log tracks which is active.

Selection: Which Variant to Iterate On

If you have multiple variants, pick the next one to improve using:

text

score(variant) = normalized_reward - 0.5 * log(1 + visit_count)

Where:

• normalized_reward = variant score relative to baseline (0-1 range)
• visit_count = how many times this variant has been selected for iteration

This balances exploitation (iterating on the best variant) with exploration (trying variants that haven't been touched recently). It prevents getting stuck in local optima.

Quick Reference: Analysis Template

When performing Phase 1, structure your thinking as:

markdown

## Evolution Cycle [N] — Analysis

### Lessons from Previous Cycles
- Cycle [N-1] changed [X], score went [up/down] by [amount]
- Principle: [what we learned]
- Pitfall: [what to avoid]

### Component Assessment
| Component | Status | Evidence |
|-----------|--------|----------|
| function_a() | Working | All test cases pass |
| function_b() | Fragile | Fails on empty input (test #4) |
| class_C | Broken | Returns None instead of dict |

### Cross-Cutting Issues
- [Issue 1 with specific evidence]
- [Issue 2 with specific evidence]

### Planned Changes (max 3)
1. **[High]** WHAT: ... | WHY: ... | RISK: ...
2. **[Medium]** WHAT: ... | WHY: ... | RISK: ...

Example: Full Evolution Cycle

Context: User asks to improve a web scraper that's failing on 40% of target pages.

Cycle 1 — Analysis:

• Component assessment: parse_html() is Broken (crashes on pages with no <article> tag), fetch_page() is Working, extract_links() is Fragile (misses relative URLs)
• Cross-cutting: No error handling — one bad page kills the entire batch
• Past edits: None (first cycle)
• Plan: [High] Add fallback selectors in parse_html() for pages without <article>

Cycle 1 — Mutate: Add cascading selector logic: try <article>, fall back to <main>, fall back to <body>.

Cycle 1 — Verify: Runs without crashes.

Cycle 1 — Score: Pass rate 40% → 72%. Delta: +32%.

Cycle 1 — Archive: Learned: "Most failures were selector misses, not logic errors. Fallback chains are high-value."

Cycle 2 — Analysis:

• Lessons: Fallback selectors gave +32%. Principle: handle structural variation before fixing logic.
• Component assessment: parse_html() now Working. extract_links() still Fragile — relative URLs not resolved.
• Plan: [High] Resolve relative URLs using urljoin in extract_links()

Cycle 2 — Mutate: Add base URL resolution.

Cycle 2 — Score: 72% → 88%. Delta: +16%.

Cycle 2 — Archive: Learned: "URL resolution was second-biggest failure mode. Always normalize URLs at extraction time."

Key Principles

• Every change must link to an observation — no speculative fixes
• Max 3 changes per cycle — attribute improvements accurately
• Log everything — failed attempts are as valuable as successes
• Score against parent, not just baseline — track marginal improvement
• Explore when stuck — if 2+ cycles on the same component show diminishing returns, move to a different component
• Revert on 3 failed retries — don't spiral; log the failure and try a different approach
• Principles compound — the evolution log's principles_learned list is the most valuable artifact; it encodes what works for this specific codebase