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okr-builder

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Create well-structured OKRs (Objectives and Key Results) for product teams, startups, and individuals. Use when asked to write OKRs, set quarterly goals, define key results, or review existing OKRs. Produces a complete OKR set with objectives, measurable key results, baselines, and a scoring guide.

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# OKR Builder Skill

Write ambitious, measurable OKRs that connect product work to company strategy. Avoid vanity metrics, output-focused key results, and objectives that sound like task lists.

## Reads from / Writes to the Brain

If a [`professional-brain`](../professional-brain/SKILL.md) (`brain/`) exists, ground in it instead of re-asking for what you already know:

- **Read first:** `context.md` (metric definitions), `knowledge/strategy.md` (where the product is going), and any open `hypotheses/`. Run `python3 ../professional-brain/scripts/brain_query.py ./brain "<objective theme>"` and carry each fact's provenance tag through — don't set a key result off a `[hunch]` as if it were `[data]`.
- **📥 Propose to the Brain:** after producing, propose logging the chosen objectives + KR targets as a `decisions/` record (the period's bet) and any new metric definitions to `knowledge/`, each provenance-tagged. Show them, get a yes, then write with `../professional-brain/scripts/brain_write.py … --commit` (append-only, dry-run by default).

## Working from a brief

You will often get a short brief without every detail (no baselines, no exact numbers). **Always deliver a complete, specific OKR set anyway** — do not stop to ask questions and do not leave bracketed placeholders like `[target]`. Where a baseline or number is missing, infer a realistic value from the brief and the domain, and mark it *(assumed — confirm)*. A clearly-labelled assumed baseline (e.g. "activation 40% *(assumed)* → 60%") is always better than a blank or an invented-as-fact figure.
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