Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add EvoScientist/EvoSkills --skill experiment-iterative-codergit clone --depth 1 https://github.com/EvoScientist/EvoSkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/evoscientist/evoskills/experiment-iterative-coder)<a href="https://agentmods.dev/skills/evoscientist/evoskills/experiment-iterative-coder"><img src="https://agentmods.dev/badge/skills/evoscientist/evoskills/experiment-iterative-coder/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/evoscientist/evoskills/experiment-iterative-coder"><img src="https://agentmods.dev/badge/skills/evoscientist/evoskills/experiment-iterative-coder.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00145 | $0.02360 |
| Opus 5.5 | $0.00058 | $0.00944 |
| Sonnet 5.5 | $0.00029 | $0.00472 |
| Haiku 4.5 | $0.00015 | $0.00236 |
Grade A, and why
experiment-iterative-coder scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- experiment-iterative-coder — 98% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 202 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
- Read
/memory/experiment-memory.mdfor proven strategies from past cycles (skip if it doesn't exist) - Identify existing tests, linting config (pyproject.toml, ruff.toml), or CI setup in the workspace
- Check available tools:
If either is missing, you will skip that check during evaluation (do not fail the iteration).ruff --version 2>&1; echo "---"; python -m pytest --version 2>&1
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)
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 202 lines · 145 tokens per session scan A 389ec2845a37
experiment-iterative-coder is a skill published in the GitHub repository EvoScientist/EvoSkills (481 stars, last pushed 19d ago), licensed Apache-2.0. It adds 145 tokens to every session and 2,360 once invoked, about $0.0006 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
testing-blocks
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diagnosing-bugs
Build a tight, red-capable feedback loop before diagnosing failures, then minimize, test ranked hypotheses, and lock the fix with a regression test.
execution-debugging
Scoped debugging methodology for when a test or build fails during execution/stabilization. Use to diagnose the failing check without scope-creeping — read the full error, reproduce in isolation, hypothesize before changing, localize, fix the root cause not the symptom, and guard with a regression test.
diagnosing-bugs
A method for investigating difficult software bugs and performance slowdowns by building a repeatable feedback loop and testing possible causes.
health
Audits agent config, instruction drift, hooks or MCP, and AI maintainability. Use when Claude, Codex, or Pi setup looks wrong. Not for application bugs or PR review.
migrate-xunit-to-xunit-v3
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