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Use when the user wants to name or rename something — a product, feature, brand, project, command, or company — or asks to "brainstorm names", "find a better name", "naming ideas", or invokes /brainstorming-names.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | brainstorming-names |
| description | Use when the user wants to name or rename something — a product, feature, brand, project, command, or company — or asks to "brainstorm names", "find a better name", "naming ideas", or invokes /brainstorming-names. |
Tournament workflow: parallel subagents on randomized models generate name candidates each round, a fixed judge scores them against a stable rubric, and top names carry forward. Model randomization replaces the removed temperature knob — each model family has a distinct creative distribution ("house style"), so mixing models samples a wider name space than any single model can.
Core rule: randomize the generators, never the judge.
Default weights: memorability & distinctiveness 40%, fit with purpose 30%, pronounceability/spellability 15%, brand risk (collisions, bad meanings in major languages) 15%. Score 1–10 per criterion.
Who judges: the orchestrator (you) scores inline with this rubric, on your own session model — no judge subagent needed. If a /score skill is available, use it with this rubric instead. Either way, the same judge and same rubric every round — the judge is never randomized, otherwise round-1 and round-5 scores are not comparable and carried winners get unfairly re-ranked.
bash -c (zsh arrays are 1-indexed; this snippet assumes bash):bash -c 'models=(fable opus sonnet haiku); for i in 1 2 3; do echo "${models[RANDOM % ${#models[@]}]}"; done'
Duplicates within a round are fine — the strategy still differs. Drop haiku from the pool if its candidates' average score is the lowest of all models in each of the first two rounds.
Spawn 3 parallel Agent calls, each with model: set from step 1 and ONE strategy from this ordered list: (1) invented/coined words, (2) real-word metaphors, (3) compound/portmanteau, (4) foreign-language roots, (5) mythology/nature/astronomy. Assign 3 distinct strategies per round, cycling through the list in order across rounds (round 1 → strategies 1-3, round 2 → 4, 5, 1, round 3 → 2-4, …). Each subagent prompt MUST contain — subagents see nothing of the conversation:
Score new candidates only. Carried winners keep their original scores — re-scoring causes drift that kills good names.
Carry top 2 forward. Track per name: round, generating model, strategy, score.
Early stop: same #1 for 3 consecutive rounds.
Final table: top 5 names × (per-criterion scores, total, round discovered, generating model, strategy) + one-paragraph recommendation for the single winner. Include which models produced the finalists — useful signal for weighting the pool next time.
| Mistake | Fix |
|---|---|
| Randomizing the judge | Scores stop being comparable; fix judge model + rubric for the whole run |
| Picking "random" models mentally | Biased; use the bash $RANDOM snippet |
| Re-scoring carried winners | Score drift; keep original scores |
| No exclusion list | Later rounds regenerate earlier names, wasting the budget |
| Thin subagent prompts | Subagents lack conversation context; include context block verbatim |
| Vague constraints ("short, memorable") | Unenforceable; convert to numbers before round 1 |
Use when a task has a large solution space and a measurable quality signal and the goal is to generate many candidates and select the best — naming/branding, UX/UI variants, marketing copy, code optimization, prompt tuning. Triggers: "brainstorm and pick the best", "explore and exploit", "simulated annealing", "tournament of options", "generate N options and rank them", iterate-until-score loops, or /explore-exploit.
Use when the user asks to rate, score, judge, critique with a number, or "how good is" an artifact — a live website/UI, a markdown draft (blog post, README, plan, docs), or code/a diff — or invokes /score, or sets up an iterate-until-score-N feedback loop (e.g. with /goal "repeat until 9/10").
Start work on a Linear issue end-to-end — create a worktree, fetch issue context from Linear, implement the fix, then commit, push, and open a draft PR. Use when the user says "start issue", "work on STU-1234", "create a worktree and fix <Linear link>", "start a worktree for this ticket", or gives a Linear URL/issue id and wants the whole branch→PR flow.
Use when the user wants to undo, absorb, collapse, or reverse a git worktree — "undo worktree", "absorb worktree", "move worktree branch back to main repo", "remove worktree and checkout in main", "get rid of this worktree", or finish work in a ~/worktrees.nosync worktree and continue it in the main studio checkout.
This skill should be used when the user asks to "clean up my Mac", "free disk space", "my disk is full", "what's taking up space", "reclaim storage", "offload files", or wants to dedupe/archive backups and media to remote storage. Provides a scan → categorize → offload/clean → reclaim workflow with tested scripts and macOS-specific gotchas.
Act as a wise, effective teacher who makes the user deeply understand the current coding session — the problem, the solution, and the broader context. Use when the user says "teach me", "make sure I understand this", "walk me through what we did", "/teach", or wants to internalize a change, bug fix, or feature before moving on.