early-experience-data
early-experience-data is a Fine-tuning & training data skill for AI agents, published by OSU-NLP-Group in the EarlyExperience repository on GitHub. Skillspace rates early-experience-data D (62/100) and it passed the safety scan; the rating is based on static analysis only.
Use this skill whenever the user asks to generate, collect, inspect, or prepare early-experience training data (Implicit World Modeling or Self-Reflection, in the sense of arXiv:2510.08558) for an agent environment. Trigger phrases include "rollout", "collect expert trajectories", "generate reflection", "smoke test the pipeline", "set up a new env for early experience", or any work that produces expert / IWM / reflection SFT JSONL files. Do NOT trigger for model training, evaluation, or unrelated work.
- Grade
- D
- Quality
- 62 / 100
- Safety
- Passed
- GitHub stars
- 117
Unproven: rated by static analysis only, not yet compared head-to-head.
Our two independent classifiers differ on this skill's category, so treat the placement as provisional.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 84 / 100
- Maturity
- Structured: ships references or scripts
- Security scan
- Low-risk patterns
- License
- MIT
- Adoption
- Adopted by 0 other repositories, in 0 collections
What the scanner flagged
- mentions secrets in text
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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