experiment-iterative-coder
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-p
npx skills add BioTender-max/awesome-bio-agent-skills --skill experiment-iterative-coder --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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 stra
- When to Use This Skill
- The Iteration Mindset
- Before Starting: Load Context
- Phase Decomposition
- The Iteration Loop
- Step 1: Plan
- Step 2: Code
- Step 3: Evaluate
- Step 4: Score
- Step 5: Decide
- Step 6: Log
- Completion
- Counterintuitive Iteration Rules
- Skill Integration
ruff --version 2>&1; echo "---"; python -m pytest --version 2>&1 ruff check . 2>&1 | tail -20 echo "LINT_EXIT: $?" ruff format --check . 2>&1 | tail -10 echo "FORMAT_EXIT: $?" python -m pytest -x -q --tb=short 2>&1 | tail -30 echo "TEST_EXIT: $?"
What does the experiment-iterative-coder skill do?
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-p
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill experiment-iterative-coder --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.
