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 OSU-NLP-Group/EarlyExperience --skill skillgit clone --depth 1 https://github.com/OSU-NLP-Group/EarlyExperienceWrote 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/osu-nlp-group/earlyexperience/skill)<a href="https://agentmods.dev/skills/osu-nlp-group/earlyexperience/skill"><img src="https://agentmods.dev/badge/skills/osu-nlp-group/earlyexperience/skill.svg" alt="Measured on agentmods" 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 | $0.00125 | $0.03647 |
| Opus 5 | $0.00063 | $0.01824 |
| Sonnet 5 | $0.00025 | $0.00729 |
| Haiku 4.5 | $0.00013 | $0.00365 |
Grade A, and why
early-experience-data 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 8d 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.
How it starts
The opening of the file, as written. The whole thing — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Early Experience Data Generation
This skill governs how data generation for the early-experience paradigm is done. Read this entire file before starting work on a new env.
What this skill is for
This skill produces SFT-ready training data for the two methods defined in the paper (https://arxiv.org/abs/2510.08558) and recapped in METHOD.md:
- Implicit World Modeling (IWM) — train the policy to predict the next state given the current state and an action.
- Self-Reflection (SR) — train the policy to produce a chain-of-thought reasoning over expert vs alternative actions, then the expert action.
The output of the workflow is JSONL files in three categories: expert, IWM, reflection. Where you put them and how you organize the surrounding pipeline scripts is up to your project — the skill is agnostic about layout. Training itself is out of scope. If a task requires running SFT, evaluating checkpoints, or tuning training hyperparameters, that belongs elsewhere — surface it to the user rather than acting on it inside this skill.
Required reading order
Before starting a new env, read in this order:
METHOD.md— what IWM and SR actually are, and the reflection prompt template. This is the source of truth for the method.method_recap.md— short list of decisions where the right answer is non-obvious. Use as a runbook, not as a tutorial.pitfalls.md— accumulated gotchas from previous envs. Skim before each new env in case something carries over.- This env's own
NOTES.md(if you or a previous agent already started one) — env-specific decisions already recorded.NOTES_TEMPLATE.mdis the skeleton to copy when you're starting fresh.
If any of these files is missing or empty (aside from NOTES.md on a fresh env), surface it as a question rather than guessing.
On conflicts between layers. For skill-wide mechanics (gates, output shape), this skill wins. For env-specific content (state representation, K, sampling strategy, prompt extensions, filtering rationale, anything tied to one env's interface or paper section), the env's NOTES.md is closer to truth than the generic guidance here. But never silently resolve a conflict. If NOTES.md says one thing and SKILL.md / METHOD.md says another, stop and surface the conflict to the user before proceeding — the usual outcome is that NOTES.md needs a correction, and the user will edit it.
What ships with it
6 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.
- 8d ago First seen · 164 lines · 125 tokens per session scan A 56b191665b38
early-experience-data is a skill published in the GitHub repository OSU-NLP-Group/EarlyExperience (124 stars, last pushed 2mo ago), licensed MIT. It adds 125 tokens to every session and 3,647 once invoked, about $0.0006 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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