--- name: lockedin-render-ideas description: | Proposes 3 to 5 next-project or career-move ideas grounded in the user's experience. One-paragraph pitch each, with cited entities. Two-turn writer/reviewer with a 5-dimension rubric. Activate when the user says "what should I work on next", "side project ideas", "새 프로젝트 아이디어", "career pivot ideas". --- # render-ideas Status: **v1.2 (calibrated)**. Research-based calibration complete. RUBRIC.md ships with five cross-source-validated dimensions. A banned_phrases.json (27 entries, each backed by 2+ sources) and research-notes.md (7 cited sources) ship alongside the prompts. Pass/fail fixture corpus under `tests/fixtures/ideas/`. ## Use this when - The user asks "what should I work on next" or "ideas for side projects" or names a constraint and asks for proposals. - The user wants to surface novel combinations of their existing skills and domains. - The user asks for career-move pitches grounded in their actual experience. ## Do NOT use when - The user wants a resume or cover letter → `render-resume-en` / `render-jaso`. - The user wants an interview answer → `render-interview`. - The vault is sparse. Seed first via `/lockedin init` or ingestion. ## Two-turn pattern Writer turn surfaces 3 to 5 ideas. Reviewer turn re-loads `RUBRIC.md` fresh and scores the set on five dimensions. ## Output shape 3 to 5 ideas, each one paragraph long, with a one-sentence pitch followed by two to three sentences of rationale that cite vault entities. Slug citations (`[[type/slug]]`) are resolved to natural language by the orchestrator before the user sees the artifact. ## Files in this directory ``` SKILL.md (this file) research-notes.md 7 cited sources, cross-source analysis summary banned_phrases.json 27 entries, severity-tagged, each backed by 2+ URLs prompt-writer.md writer-turn instruction prompt-reviewer.md reviewer-turn instruction (re-loads RUBRIC.md fresh) RUBRIC.md 5-dimension scoring contract + score bands ``` ## Calibration status v1.2 calibrated. The rubric dimensions (feasibility, novelty, evidence_ground, scope_match, motivation_alignment) are grounded in cross-source public research from Atlassian, Inc., FasterCapital, Proposify, Built In, fundsforNGOs, and arXiv. The banned_phrases.json contains 27 entries across five categories (buzzword_opener, vague_enthusiasm, hedging_language, unsubstantiated_claim, comparison_shortcut), each backed by 2+ independent sources. Pass and fail fixture corpus is at `tests/fixtures/ideas/{pass,fail}/` (3 pass, 3 fail).