generate-synthetic-data
generate-synthetic-data is a Test strategy & case design skill for AI agents, published by hamelsmu in the evals-skills repository on GitHub. Skillspace rates generate-synthetic-data D (59/100) and it passed the safety scan; the rating is based on static analysis only.
Create diverse synthetic test inputs for LLM pipeline evaluation using dimension-based tuple generation. Use when bootstrapping an eval dataset, when real user data is sparse, or when stress-testing specific failure hypotheses. Do NOT use when you already have 100+ representative real traces (use stratified sampling instead), or when the task is collecting production logs.
- Grade
- D
- Quality
- 59 / 100
- Safety
- Passed
- GitHub stars
- 1,665
Unproven: rated by static analysis only, not yet compared head-to-head.
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Our review
- Architecture
- 84 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- No issues found
- License
- Not declared. Check the repository before reusing it.
Not checked yet
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- 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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