octocode-benchmark
octocode-benchmark is an LLM evaluation & observability skill for AI agents, published by bgauryy in the octocode repository on GitHub. Skillspace rates octocode-benchmark D (68/100) and it passed the safety scan; the rating is based on static analysis only.
Use when planning, running, grading, or reporting the by-hand Octocode research benchmark — pairwise matchups (Octocode anchor vs one baseline: gh+RTK, gh+Headroom, or plain gh) over markdown questions, with a fresh isolated runner agent per (question, arm, pass), one blind judge per question grading two answers X/Y in randomized order, and an orchestrator that summarizes accuracy/quality/workflow/characters. Results measured in total characters through the model (model-in delivered + model-out commands/args + final answer).
- Grade
- D
- Quality
- 68 / 100
- Safety
- Passed
- GitHub stars
- 933
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
- 100 / 100
- Maturity
- Basic: a single SKILL.md
- 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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