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 CamusGIT/EvoQuant --skill experiment-iterative-codergit clone --depth 1 https://github.com/CamusGIT/EvoQuantWrote 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/camusgit/evoquant/experiment-iterative-coder)<a href="https://agentmods.dev/skills/camusgit/evoquant/experiment-iterative-coder"><img src="https://agentmods.dev/badge/skills/camusgit/evoquant/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/camusgit/evoquant/experiment-iterative-coder"><img src="https://agentmods.dev/badge/skills/camusgit/evoquant/experiment-iterative-coder.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.02359 |
| 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.
This is a copy
98% identical to experiment-iterative-coder — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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 a488105f498f
experiment-iterative-coder is a skill published in the GitHub repository CamusGIT/EvoQuant (141 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 145 tokens to every session and 2,359 once invoked, about $0.0006 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 98% identical to experiment-iterative-coder, differing in 2 lines, and is treated as a copy.
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