Benchmark Logging
SkillMonitoring & opsDefine benchmark runs and log outcomes with consistent metrics, acceptance criteria, and reproducible artifact references.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Benchmark Logging skill
What this skill tells your AI
The instructions your AI receives, as published by drpedapati/sciclaw in skills/benchmark-logging/SKILL.md and read by ahel’s review.
Use this skill to run and document benchmark comparisons between sciClaw and baseline workflows.
When to use
- "run benchmark"
- "compare baseline vs sciclaw"
- "log benchmark outcomes"
- "add acceptance criteria"
Minimum benchmark record
- Benchmark ID and date.
- Task category and scenario definition.
- Baseline command sequence.
- sciClaw command sequence.
- Metrics: task success, reproducibility, latency, and resource usage.
- Acceptance decision (pass/fail) with rationale.
Workflow
- Freeze scenario definitions before running.
- Execute baseline and sciClaw runs with the same inputs.
- Record metric values and artifact paths.
- Log failures with root-cause notes and retry policy.
- Add manuscript-ready summary sentences only after data is logged.
Signals
- GitHub stars
- 88
- Forks
- 17
- Last commit
- Jul 2026
Advanced
- Catalog kind
- skill
- Gateway key
benchmark-logging- Source
- github.com/drpedapati/sciclaw