Optimize · scaledown-team
Optimize the prompt you pass to a ScaleDown SLM (extract, classify, summarize, or compress) and benchmark it against a baseline — the frontier-model output it replaced, or your ground truth — on the user's own data. Collects sample data and a ScaleDown API key; if the user has no samples, walks them through creating and validating a small labeled set first. Then runs repeatable evals with a metric appropriate to the task type, quantifies run-to-run noise, and iterates the prompt until it beats — or matches within noise — the baseline. General by design: adapts to any use case, with task-specific playbooks.
Works with: Claude Code (native) · Cursor, Codex CLI (manual)
native: this artifact type is that client's own format
Category: AI & Agents — see all ranked ›
Install (Claude Code):
cp -r optimize ~/.claude/skills/- Adoption: 1 repos
- Upkeep: 95.0
- Freshness: 89.0
- Evidence coverage: 84% of the inputs this score can use — the rest are unknown, and the score is discounted for it
- Health: active
- Contributors: 3
Security audit
Not scanned yet. We audit npm-published capabilities for known advisories, install-time scripts and permission surface; this one has no npm package we can resolve, or has not reached the queue.
Its own instructions
Its SKILL.md shows worked examples and covers setup.
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Measured 2026-08-23 · scorer s5 · how · something wrong here?