attention-variants-from-papers
attention-variants-from-papers is a Model training & evaluation skill for AI agents, published by benchflow-ai in the skillsbench repository on GitHub. Skillspace rates attention-variants-from-papers D (68/100) and it passed the safety scan; the rating is based on static analysis only.
Implement paper-defined attention variants by extracting mechanism invariants, mapping tensor shapes, preserving module interfaces, validating numerical behavior, and integrating the custom attention into an existing transformer stack.
- Grade
- D
- Quality
- 68 / 100
- Safety
- Passed
- GitHub stars
- 1,769
Unproven: rated by static analysis only, not yet compared head-to-head.
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Our review
- Architecture
- 88 / 100
- Maturity
- Structured: ships references or scripts
- Security scan
- No issues found
- License
- Not declared. Check the repository before reusing it.
Not checked yet
- Category is a classifier prediction; no model has read this skill 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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