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 agentmods add skills/simple-agent-lab/autotrainess/iteration_plannpx skills add simple-agent-lab/AutoTrainess --skill iteration_plangit clone --depth 1 https://github.com/simple-agent-lab/AutoTrainessWrote 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/simple-agent-lab/autotrainess/iteration_plan)<a href="https://agentmods.dev/skills/simple-agent-lab/autotrainess/iteration_plan"><img src="https://agentmods.dev/badge/skills/simple-agent-lab/autotrainess/iteration_plan.svg" alt="Measured on agentmods" 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 | $0.00017 | $0.00309 |
| Opus 5 | $0.00009 | $0.00154 |
| Sonnet 5 | $0.00003 | $0.00062 |
| Haiku 4.5 | $0.00002 | $0.00031 |
Grade A, and why
iteration_plan 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 6d 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.
What it actually says
iteration_plan
Purpose
Define a clear goal and concrete action plan for the current experiment iteration based on real evidence from previous experiments.
Inputs
- Results from previous experiments.
- Prior evaluation evidence and analysis.
- The current training and data context available in the workspace.
Required outputs
- The main problems observed in previous experiments.
- The main objective of the current iteration.
- The changes planned for the current iteration.
- Whether this iteration mainly changes data, training, or both.
- The outcome that will count as success.
- Concise guidance for downstream data or training work.
Rules
- Base the plan on real evidence from previous experiments rather than speculation.
- Focus on the main objective of the current iteration rather than trying to address every issue at once.
- Separate previous problems, current objective, planned changes, and success criteria clearly.
- Define the direction for the current iteration, but do not directly execute data construction or training in this skill.
- Keep the plan concrete enough that downstream skills can act on it.
Procedure
- Review previous experiment results and identify the main problems.
- Decide what the current iteration is mainly trying to improve.
- Define the main changes to make in this iteration.
- State what outcome will count as success for this iteration.
- Provide concise guidance for downstream data and training work.
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.
- 6d ago First seen · 39 lines · 17 tokens per session scan A ab5e8663da37
iteration_plan is a skill published in the GitHub repository simple-agent-lab/AutoTrainess (21 stars, last pushed 1mo ago), licensed MIT. It adds 17 tokens to every session and 309 once invoked, about $0.0001 per session on Opus 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.
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