| 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 <!-- ko-example -->"what should I work
on next", "side project ideas", "새 프로젝트 아이디어", "career
pivot ideas"<!-- /ko-example -->.
|
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).
