experiment-iterative-coder

name	experiment-iterative-coder
description	Iterative code refinement through plan → code → evaluate → refine cycles. Runs lint checks (ruff), tests (pytest), and structured self-evaluation each cycle, then diagnoses failures and refines. Decomposes complex tasks into sequential phases, iterates up to 3 times per phase (10 total). Use when: the main agent delegates a code task with 'MODE: MORE_EFFORT', the user selects 'More Effort' code generation mode, or the task explicitly requests iterative refinement for higher code quality. Do NOT use for single-pass code generation (Lite mode), experiment pipeline orchestration (use experiment-pipeline), or diagnosing a specific experiment failure (use experiment-craft).
allowed-tools	write_file edit_file read_file think_tool execute
metadata	{"author":"EvoScientist","version":"1.0.0","tags":["core","code-generation","iteration","refinement"]}

Iterative Coder

Iterative code refinement through structured plan → code → evaluate → refine cycles. Each cycle runs objective checks (lint, tests) and self-evaluation, then diagnoses failures and plans targeted improvements. Reaches production quality in 3-8 iterations.

When to Use This Skill

• Main agent delegates a code task prefixed with "MODE: MORE_EFFORT"
• User selected "More Effort" mode for code generation
• Task requires high code quality with verified correctness
• Task involves complex implementation (5+ files, multiple modules)
• You want to iterate on code quality rather than submit first-pass code
• You mention "iterative refinement", "code quality loop", "plan-code-evaluate"

The Iteration Mindset

Code quality comes from fast feedback loops, not careful first attempts. A fast plan → code → evaluate → fix cycle beats spending 30 minutes on a "perfect" first implementation. The evaluate step reveals problems you cannot predict by thinking alone — lint errors, import failures, test regressions, and missing edge cases all surface immediately when you actually run the code.

Before Starting: Load Context

1. Read /memory/experiment-memory.md for proven strategies from past cycles (skip if it doesn't exist)
2. Identify existing tests, linting config (pyproject.toml, ruff.toml), or CI setup in the workspace
3. Check available tools:

   ruff --version 2>&1; echo "---"; python -m pytest --version 2>&1
   

   If either is missing, you will skip that check during evaluation (do not fail the iteration).

Phase Decomposition

Before iterating, analyze the task and break it into sequential phases:

Task Complexity	Recommended Phases
Single file, well-defined function	1 phase
2-4 files, clear interfaces	2 phases
5+ files, multiple interacting modules	3-5 phases

For each phase, define:

• Name: concise label (e.g., "Data loading pipeline")
• Goal: what "done" looks like for this phase
• Verification signal: how to confirm the phase is complete (specific test, lint clean, output matches)

Order phases by dependency — later phases may build on earlier ones.

The Iteration Loop

For each phase, iterate up to 3 times. Global maximum: 10 iterations across all phases.

Step 1: Plan

Read current code and previous evaluation feedback (if any). Write a concise improvement plan.

First iteration of a phase: Write an initial implementation plan based on the phase goal.

Subsequent iterations: Analyze the last evaluation's feedback and diagnose the root cause of failures before planning changes. Do not repeat the same approach that already failed.

Adapt your plan based on the failure mode from the last evaluation:

Last Failure	Planned Response
Timeout	Add --quick/--smoke mode, reduce data size, add early stopping
Syntax Error	Simplify logic, run python -c "import ast; ast.parse(open('file.py').read())" to validate before running
Import Error	Check pip list, use only installed packages, add missing deps to requirements
Test Failure	Focus on the specific failing test, make minimal targeted changes
Lint Failure	Run ruff check --fix . && ruff format . before any logic changes
Low self-assessment	Re-read the original task requirements, check for missing functionality

Step 2: Code

Implement the plan. Keep changes focused on what the plan specifies.

• Do not rewrite working files unless the plan explicitly requires it
• After writing code, do a quick sanity read of the changed files

Step 3: Evaluate

CRITICAL: You MUST run these commands every iteration. Do not skip evaluation.

# 1. Lint check
ruff check . 2>&1 | tail -20
echo "LINT_EXIT: $?"

# 2. Format check
ruff format --check . 2>&1 | tail -10
echo "FORMAT_EXIT: $?"

# 3. Run tests (only if test files exist in workspace)
python -m pytest -x -q --tb=short 2>&1 | tail -30
echo "TEST_EXIT: $?"

If ruff is not installed, skip checks 1-2. If pytest is not installed or no test files exist, skip check 3. Record which checks were skipped.

Step 4: Score

Compute a composite score from objective signals and self-assessment.

Objective signals (from Step 3 exit codes):

• LINT_EXIT=0 → lint_score = 1.0, else lint_score = 0.0
• FORMAT_EXIT=0 → format_score = 1.0, else format_score = 0.0
• TEST_EXIT=0 → test_score = 1.0, else parse pass ratio from pytest output (e.g., "3 passed, 1 failed" → 0.75)

Self-assessment (rate 0.0 – 1.0): Evaluate on: correctness (does the code do what was asked?), completeness (all requirements addressed?), error handling (reasonable edge cases covered?), readability (clear names, structure).

Composite score — dynamic weighting based on available signals:

• Lint + tests available: 0.2 × lint + 0.1 × format + 0.3 × test + 0.4 × self
• Lint only (no tests): 0.3 × lint + 0.1 × format + 0.6 × self
• Tests only (no ruff): 0.4 × test + 0.6 × self
• Neither available: 1.0 × self

Self-assessment hard caps — prevent score inflation from self-assessment:

• If lint check FAILED → composite capped at 0.4, regardless of self-assessment
• If any test FAILED → composite capped at 0.6
• Only claim composite ≥ 0.85 if BOTH lint and tests pass AND implementation is complete
• Deductions: missing error handling for obvious cases (−0.1), hardcoded absolute paths (−0.05)

See references/evaluation-protocol.md for detailed scoring edge cases.

Step 5: Decide

• Composite score ≥ 0.85 → advance to next phase (or finish if last phase)
• Composite score < 0.85 → return to Step 1 with evaluation feedback
• Phase iteration limit reached (3 per phase) → advance to next phase anyway, note remaining issues
• Global iteration limit reached (10 total) → stop, output current best result

Step 6: Log

CRITICAL: Append to /artifacts/iteration_log.md after every iteration.

Use the template at assets/iteration-log-template.md:

## Iteration {N} (Phase {M}/{T})
- **Score**: {composite} (lint={X} format={X} test={X} self={X})
- **Lint**: passed/failed ({N} issues)
- **Tests**: passed/failed ({passed}/{total})
- **Changes**: [{files changed}]
- **Feedback**: [{key evaluation findings}]
- **Next**: continue / next_phase / done

Completion

After all phases complete or global iteration limit is reached:

1. Report to the caller:
   • Total iterations used
   • Final composite score
   • Key improvements per phase (1-2 sentences each)
2. List all output file paths (code, configs, tests)
3. Note remaining issues: lint warnings, missing tests, known limitations, TODOs

Counterintuitive Iteration Rules

1. Fix lint before logic: Lint errors compound — one import error masks all test failures downstream. Always run ruff check --fix . before investigating logic bugs.

2. 3 iterations is enough per phase: If you cannot fix it in 3 targeted iterations, the problem is architectural (wrong decomposition), not incremental. Advance to the next phase or re-plan rather than iterating further.

3. Tests reveal more than reading: Running tests for 10 seconds teaches you more about correctness than reading code for 5 minutes. Always run tests, even when you are confident the code is correct.

4. Score drops are information: If your composite score drops after a change, that is a signal about what matters. Analyze why it dropped before undoing the change.

5. Don't gold-plate: 0.85 is the target, not 1.0. Diminishing returns kick in hard above 0.9. Ship and iterate in the next conversation if needed.

Skill Integration

Before Starting (load memory)

Refer to evo-memory → Read /memory/experiment-memory.md for prior strategies

On Failure (stuck after max iterations)

Refer to experiment-craft → 5-step diagnostic flow to understand the root cause before retrying

On Success (all phases complete, score ≥ 0.85)

Report to the main agent → main agent continues pipeline (data-analysis, writing, etc.)

Handoff Artifacts

Artifact	Location	Used By
Iteration log	/artifacts/iteration_log.md	Main agent summary, evo-memory ESE
Final code	Workspace root	Next pipeline step
Test results	Iteration log entries	data-analysis-agent

Reference Navigation

Topic	Reference File	When to Use
Scoring rules and edge cases	evaluation-protocol.md	When scoring edge cases arise (partial tests, missing tools)
Iteration log template	iteration-log-template.md	Every iteration (Step 6)