reward-engineering
reward-engineering is a Model training & evaluation skill for AI agents, published by johnsutor in the so101-nexus repository on GitHub. Skillspace rates reward-engineering D (68/100) and it passed the safety scan; the rating is based on static analysis only.
Use when designing or reviewing a reward function for a new so101-nexus environment (or auditing an existing one), especially any task with a multi-phase or dwelling-prone completion condition (grasp-then-release, reach-then-hold, multi-object sequencing). Covers dwelling-vs-potential-based shaping, keeping the potential monotone along the ideal trajectory, how to spot a reward-hacking trap before it costs a training run, and the primitives/citations to use.
- Grade
- D
- Quality
- 68 / 100
- Safety
- Passed
- GitHub stars
- 23
Unproven: rated by static analysis only, not yet compared head-to-head.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 100 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- No issues found
- License
- Apache-2.0
- Adoption
- Adopted by 0 other repositories, in 0 collections
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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