synthetic-self-improve-rl
synthetic-self-improve-rl is a Fine-tuning & training data skill for AI agents, published by vivekvkashyap in the synthetic-self-improve-rl repository on GitHub. Skillspace rates synthetic-self-improve-rl F (45/100) and the safety scan rated it risky; the rating is based on static analysis only.
Risky. This skill has patterns associated with unsafe behaviour. Read the source carefully before running it.
Teacher/student self-improvement loop. Claude post-trains a smaller student model on a real dataset via prime-rl, analyzes failed training traces, generates a 500-1000 row dataset targeting the model's weaknesses, wraps it as a verifiers environment, and re-trains in a loop until a wall-clock budget expires. Default student is Qwen/Qwen3-0.6B; override with --model. Use when the user types /synthetic-self-improve-rl <dataset>.
- Grade
- F
- Quality
- 45 / 100
- Safety
- Risky
- GitHub stars
- 26
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.
Our review
- Architecture
- 82 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- High-risk patterns
- License
- MIT
- Adoption
- Adopted by 0 other repositories, in 0 collections
What the scanner flagged
- dynamic code execution (eval/exec)
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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