asiaostrich/brainstorm

Brainstorm Assistant | 腦力激盪助手

Language: English | 繁體中文

Structured ideation before specification writing. Transform vague ideas into actionable feature proposals through guided brainstorming — grounded in 2024–2026 research on AI-assisted ideation.

在撰寫規格前進行結構化發想。以 2024–2026 年 AI 輔助發想研究為基礎，透過引導式腦力激盪，將模糊構想轉化為可執行的功能提案。

Implements: XSPEC-247 brainstorm v3 — Multi-Persona Ensemble + Multi-Critic Convergence (supersedes the v2 "cognitive science upgrade").

What changed in v3 | v3 的核心改動: Divergence is no longer a single AI voice racing to a count — it is a persona ensemble (each role reasons via chain-of-thought, in isolation) crossed with diversity lenses. Convergence is no longer one AI scorer plus one devil's-advocate — it is a multi-critic panel plus a hard-role rebuttal (Devil's Advocate + Steelman). This directly targets the strongest finding in the literature: multiple personas beat a single pass, and a single LLM critic is weak and prone to sycophancy.

v3 把發散從「單一 AI 衝數量」改為persona 集成（每個角色以思維鏈獨立推理）× 多樣性透鏡；把收斂從「單一 AI 評分 + 單一反駁」改為多評審面板 + 硬角色反駁（Devil's Advocate + Steelman）。直接對應文獻最強結論：多 persona 勝過單一 pass，單一 LLM 評審既弱又易諂媚。

Mode Selection | 使用前先選模式

Apply these objective triggers before starting. Default is full v3 — routing rules are shortcuts to skip phases, not barriers to add.

使用前套用以下客觀觸發條件。預設為完整 v3，路由規則是跳過階段的快捷鍵，而非額外障礙。

| Condition | Recommended Mode | Command | |-----------|-----------------|---------| | Problem description < 20 words or topic feels vague | Full v3 (default) | /brainstorm [topic] | | Strategic question (career, architecture, business model) | Full v3 with rebuttal | /brainstorm [topic] | | Host supports parallel subagents and you want maximum diversity | Full v3 + Enhanced tier | /brainstorm --enhanced [topic] | | Creative-only (naming, tagline, marketing copy) | Lite — skip rebuttal | /brainstorm --no-rebuttal [topic] | | Time-constrained or execution-type (write code, fix copy) | Quick mode | /brainstorm --quick [topic] | | Already have an SDD spec for this topic | Skip pre-flight | /brainstorm --skip-preflight [topic] |

Rule of thumb: If you are unsure which row applies, use full v3. The cognitive overhead of deciding is higher than just running the full flow.

判斷原則： 不確定適用哪一行時，直接用完整 v3。判斷本身的認知成本高於直接跑完整流程。

Workflow | 工作流程

[Mode Selection] ─► PRE-FLIGHT ─► FRAME ─► DIVERGE ───────────► CONVERGE ──────────► OUTPUT
   客觀路由          防止錨定      定義問題   persona 集成+透鏡       多評審面板+硬角色反駁    輸出提案

---

Phase 0: PRE-FLIGHT | 防止 AI 錨定

Why this phase exists: Independent ideas written before the AI generates anything consistently produce more diverse results. In AI-assisted contexts this matters more, not less: research on design fixation shows that AI output — being fluent and high-fidelity — deepens fixation rather than relieving it (Wadinambiarachchi et al., CHI 2024).

本階段存在的原因： 在 AI 生成任何內容之前先寫下自己的想法，能持續產出更多樣的結果。在 AI 情境下這更重要：設計固著研究顯示，流暢、高擬真的 AI 輸出反而加深固著（Wadinambiarachchi 等，CHI 2024）。

Before the AI generates any content, the user completes three items:

在 AI 生成任何內容之前，使用者完成三件事：

| Item | Prompt | 說明 | |------|--------|------| | 1 | One-sentence problem description | 一句話描述問題 | | 2 | Three initial ideas (any format, any quality) | 3 個初始想法（任意形式、不限品質） | | 3 | "Solution types I do NOT want" (N/A allowed) | 「我最不想要的解法類型」（可填 N/A） |

After the user submits, the AI reads all three inputs and proceeds to FRAME. The AI's first DIVERGE output MUST explore directions the user did not mention and MUST NOT duplicate the user's three ideas.

使用者提交後，AI 讀取全部三項輸入再進入 FRAME。AI 的第一批 DIVERGE 輸出必須探索使用者未提及的方向，且不得重複使用者已提交的想法。

Anti-seed guardrail (new in v3): Do NOT accept or generate a "like X but for Y" framing as the seed (e.g. "Slack but for doctors"). Such analogical seeds lock the LLM into one solution space and measurably reduce idea variety. Capture the underlying problem, not a product analogy.

反種子 guardrail（v3 新增）： 不要用「像 X 但給 Y」的框架當種子（如「給醫生用的 Slack」）。這類產品類比種子會把 LLM 鎖進單一解空間、明顯降低想法多樣性。請捕捉底層問題，而非產品類比。

Flag: --skip-preflight bypasses this phase with a one-line warning: ⚠ Skipping Pre-flight may cause AI anchoring

---

Phase 1: FRAME | 定義問題

Define the problem space clearly before generating ideas.

在產生想法之前，先清楚定義問題空間。

| Step | Action | 步驟 | |------|--------|------| | 1 | Clarify the problem with 5 Whys | 用 5 Whys 釐清問題根因 | | 2 | Reframe as "How Might We" (HMW) questions | 重構為 HMW 問題 | | 3 | Identify stakeholders and constraints | 識別利害關係人與限制條件 | | 4 | Gather context from codebase (if applicable) | 從程式碼庫蒐集脈絡（如適用） |

---

Phase 2: DIVERGE | 發散思考（v3：persona 集成 + 多樣性透鏡）

Core mechanism in v3: a persona ensemble, each persona reasoning via chain-of-thought in isolation, crossed with diversity lenses. Meincke, Mollick & Terwiesch (2024) found that chain-of-thought + personas produces the highest idea diversity of any prompting strategy — close to human groups. A raw idea count is a weak proxy; structurally forcing distinct viewpoints is the real lever.

v3 核心機制： persona 集成——每個 persona 以思維鏈在隔離狀態下推理——再乘上多樣性透鏡。Meincke、Mollick、Terwiesch（2024）發現「思維鏈 + persona」的想法多樣性高於所有受測提示策略，接近人類團體。光衝數量是弱代理；結構性逼出不同視角才是真正槓桿。

Step 2a — Persona ensemble | persona 集成

Generate ideas through a default ensemble of personas. Each persona reasons step by step (chain-of-thought) and produces 2–4 ideas from its own lens only. The user may add, drop, or rename personas via --personas.

透過預設 persona 組生成想法。每個 persona 逐步推理（思維鏈），只從自己的視角產出 2–4 個想法。使用者可用 --personas 增減或改名。

| Default persona | Lens it argues from | 視角 | |-----------------|---------------------|------| | Domain expert | What does best-practice in this domain demand? | 領域最佳實務要求什麼？ | | Skeptic / risk | Where does this break? What fails first? | 哪裡會壞？什麼先失敗？ | | Cross-domain analogist | How do biology / other fields solve an analogous problem? | 生物/他領域如何解類似問題？ | | Cost / constraint | What is the cheapest, smallest thing that works? | 最便宜最小可行解是什麼？ | | End-user advocate | What does the actual user feel and need? | 真實使用者的感受與需求？ |

Branch isolation: In baseline mode, generate each persona's ideas without showing it the other personas' output — this prevents intra-session anchoring. Present all personas' ideas together only after every persona has produced its set. (In the Enhanced tier, run personas as parallel isolated agents — see "Enhanced Tier" below.)

分支隔離： baseline 模式下，生成每個 persona 的想法時不讓它看到其他 persona 的輸出，以防止 session 內錨定。等所有 persona 都產完才一起呈現。（Enhanced 層以平行隔離 agent 跑——見下方「Enhanced Tier」。）

Step 2b — Diversity lenses | 多樣性透鏡

Apply at least one lens across the ensemble to push past the "obvious answer zone." Connecting disparate concepts measurably increases originality (Mehrotra, Parab & Gulwani, 2024).

在 persona 組上至少套用一個透鏡，以突破「顯而易見答案區」。連結異域概念能可量測地提升原創性（Mehrotra、Parab、Gulwani，2024）。

| Lens | Prompt pattern | 透鏡 | |------|----------------|------| | Analogical / cross-domain | "Find a system in [biology / logistics / games] that solves an analogous problem. What can we borrow?" | 類比/跨域：借用他領域結構 | | Assumption reversal | "List what everyone assumes must be true, then invert each one." | 假設反轉：列出共識假設並逐一反轉 | | Morphological matrix | "Build a 3-axis matrix (e.g. User × Trigger × Constraint); fill rare combinations." | 形態矩陣：系統性填補罕見組合 |

Force --lens analogical|reversal|morphological to make a specific lens the primary one.

Step 2c — Continue nudge (auxiliary) | 繼續發散提示（輔助）

The "best ideas appear in the second half" pattern (Nijstad) is a human-group finding and is not confirmed for LLMs (which tend to plateau / exhaust). So a fixed idea-count gate is demoted to an auxiliary nudge: if fewer than ~8 distinct ideas exist across the ensemble, prompt "Continue — add a persona or lens you haven't used." Diversity (distinct lenses covered), not raw count, is the gate.

「好點子在後半」（Nijstad）是人類群體現象，未在 LLM 證實（LLM 多為高原/枯竭）。故固定數量門檻降為輔助提示：若全組少於約 8 個相異想法，提示「繼續——加一個還沒用過的 persona 或透鏡」。真正的門檻是多樣性（覆蓋了幾個不同視角），而非數量。

Classic techniques (still available) | 經典技法（仍保留）

| Technique | When to Use | 使用時機 | |-----------|-------------|----------| | HMW Questions | Default starting point | 預設起點 | | SCAMPER | Improving existing features | 改善現有功能 | | Six Thinking Hats | Need multiple perspectives (works well as personas) | 需要多角度（很適合當 persona） |

---

Phase 3: CONVERGE | 收斂（v3：多評審面板 + 硬角色反駁）

Core mechanism in v3: a multi-critic panel replaces the single weighted scorer. A single LLM is a weak, biased evaluator (Li et al., 2025: LLMs are strong at generation/refinement but weak at evaluation — keep the human as final arbiter). Three independent critic lenses score each idea; their scores are aggregated.

v3 核心機制： 多評審面板取代單一加權評分者。單一 LLM 是弱且有偏的評估者（Li 等，2025：LLM 強於生成/精煉、弱於評估——人類保留最終裁決權）。三個獨立評審透鏡各自評分後聚合。

Step 3a: Multi-critic panel | 多評審面板

Run three independent critics, each scoring every idea 1–5 on its own lens. Aggregate (mean) to reduce single-critic bias. Each critic uses the weighted formula below.

跑三個獨立評審，各自以自己的透鏡對每個想法打 1–5 分；取平均聚合以降低單評審偏誤。每位評審皆套用下方加權公式。

| Critic lens | Weighted criteria it owns | 評審透鏡 | |-------------|---------------------------|----------| | Engineering feasibility | Feasibility 50% · Effort 50% | 工程可行性 | | User impact | Impact 70% · Alignment 30% | 使用者影響 | | Strategic alignment | Alignment 60% · Impact 40% | 策略一致性 |

Per-criterion guide (1–5): Feasibility (5=trivial … 1=near-impossible); Impact (5=transformative … 1=negligible); Effort (5=hours … 1=quarters, inverted so lower effort scores higher); Alignment (5=core mission … 1=off-mission).

Optional — RICE / ICE (product features): for prioritising shippable features, score RICE = (Reach × Impact × Confidence) / Effort or the lighter ICE = Impact × Confidence × Ease. Let engineers — not the LLM — estimate Effort; the LLM lacks codebase knowledge. RICE favours incremental wins, so don't use it alone for strategic bets.

可選——RICE / ICE（產品功能）： 排序可出貨功能時用 RICE =（Reach × Impact × Confidence）/ Effort 或較輕的 ICE = Impact × Confidence × Ease。Effort 交由工程師估、不要讓 LLM 估（它無程式庫知識）。RICE 偏好漸進式勝利，別單獨用於策略性押注。

Step 3b: Hard-role Rebuttal Round | 硬角色反駁輪

A soft "please critique this" instruction yields mostly agreement (sycophancy). v3 assigns hard roles: for each of the top 3 ideas, run a Devil's Advocate ("Your job is to argue this idea WILL fail") and a Steelman ("State the strongest charitable version of the counterargument"). Together they stress-test resilience rather than merely poke.

軟性「請批評一下」只會得到附和（諂媚）。v3 指派硬角色：對前三名想法各跑一個 Devil's Advocate（「你的任務是論證此案會失敗」）與一個 Steelman（「說出反方最強而善意的版本」）。兩者一起壓力測試韌性，而非只是戳。

Each counterargument must take the form: "This idea will fail in [specific context] because [specific reason]." Vague concerns ("this might be hard") are rejected.

每個反對理由須為：「在 [具體情境] 下，此想法會失敗，因為 [具體原因]。」模糊顧慮（「這可能有點難」）不接受。

The user must respond to each before advancing:

使用者必須對每個給出回應才能繼續：

| Option | Action | 說明 | |--------|--------|------| | (a) | Accept criticism → provide modified version | 接受批評 → 提供修改版本 | | (b) | Disagree → provide specific reason to retain | 不同意 → 給具體保留理由 | | (c) | Criticism valid → remove from ranking | 批評成立 → 從排名移除 |

Flag: --no-rebuttal skips this step; report section marked "Rebuttal: skipped".

---

Phase 4: OUTPUT | 輸出提案

Produce a Brainstorm Report ready for /requirement or /sdd. Each surviving idea is marked ✓ Passed rebuttal with a one-line summary of the user's response, its originating persona/lens, and its aggregated critic score.

產生可直接對接 /requirement 或 /sdd 的腦力激盪報告。每個存活想法標記 ✓ Passed rebuttal、使用者回應摘要、來源 persona/透鏡、以及聚合評審分數。

Output Format | 輸出格式

# Brainstorm Report: [Topic]

## Problem Statement
[Refined problem + root cause from FRAME]

## HMW Questions
1. How might we ...?

## Ideas Generated
| # | Idea | Persona | Lens | Critic-Feas | Critic-Impact | Critic-Align | Agg. Score |
|---|------|---------|------|-------------|---------------|--------------|-----------|
| 1 | ...  | Skeptic | Reversal | 4.0 | 4.5 | 4.0 | 4.2 |

## Top 3 Recommendations
1. **[Idea]** ✓ Passed rebuttal — [Why] — Persona: [..] — [User rebuttal response]

## Diversity Note
[How many distinct lenses/personas the surviving ideas span — flag if all from one cluster]

## Discarded Ideas (with reasons)
| Idea | Reason |

## Next Steps
- [ ] Proceed to `/requirement` with top idea
- [ ] Proceed to `/sdd` if requirements are clear

Diversity-Collapse Guardrail | 多樣性崩塌防護

Using a single LLM for ideation reduces the diversity of ideas across users, even when each individual feels more creative (Anderson, Shah & Kreminski, 2024; corroborated by the widely-cited Doshi & Hauser, Science Advances 2024). Guard against it:

用單一 LLM 發想會降低跨使用者的想法多樣性，即使個人覺得更有創意（Anderson、Shah、Kreminski，2024；與廣為引用的 Doshi & Hauser，《Science Advances》2024 同向）。防範方式：

• Never seed with a competitor or product analogy ("like X but for Y"). | 絕不用競品/產品類比當種子。
• Vary the lens, not just the wording — reword ≠ diversify. | 變的是透鏡而非措辭——換句話不等於多樣化。
• If the surviving Top 3 all came from one persona/lens, flag it and run one more lens before OUTPUT. | 若前三名全來自同一 persona/透鏡，標示並在輸出前再跑一個透鏡。

Enhanced Tier — Parallel Personas | 強化層——平行 persona

Multi-agent ideation (independent agents conversing/contributing) outperforms a single agent on perceived quality and novelty (Quan et al., 2025, MultiColleagues). Where the host supports parallel subagents (e.g. Claude Code's Agent/Workflow tools), --enhanced runs each persona — and each critic — as a parallel, context-isolated agent, then merges and de-duplicates the results.

多 agent 發想（獨立 agent 互相對話/貢獻）在感知品質與新穎度上勝過單 agent（Quan 等，2025，MultiColleagues）。在支援平行子代理的宿主（如 Claude Code 的 Agent/Workflow 工具），--enhanced 會把每個 persona 與每個評審當作平行、context 隔離的 agent 跑，再合併去重。

Graceful degradation: This tier is optional. On hosts without subagents, --enhanced silently falls back to baseline (single-context simulated personas). The skill remains scope: universal.

優雅降級： 此層為可選。在無子代理的宿主，--enhanced 靜默退回 baseline（單 context 模擬 persona）。本 skill 維持 scope: universal。

Technique Quick Reference | 技法速覽

| Technique | Purpose | 用途 | |-----------|---------|------| | 5 Whys | Root cause analysis | 根因分析 | | HMW | Problem reframing | 問題重構 | | Persona ensemble | Forced viewpoint diversity (v3 core) | 強制視角多樣性（v3 核心） | | Diversity lenses | Push past obvious zone (analogical / reversal / morphological) | 突破顯而易見區 | | Multi-critic panel | Bias-reduced scoring (v3 core) | 降偏誤評分（v3 核心） | | Devil's Advocate + Steelman | Hard-role rebuttal | 硬角色反駁 | | SCAMPER / Six Hats | Classic divergence (usable as personas) | 經典發散（可當 persona） |

Session Self-Evaluation | 工作階段自評

After each session, record three metrics (1–5) to track improvement over time.

每次工作階段結束後記錄三個指標（1–5 分），追蹤長期改善。

| Metric | Question | 指標 | |--------|----------|------| | Adoption Rate | How many of today's ideas will I actually use? | 採用率 | | Diversity | Did surviving ideas span multiple personas/lenses? | 多樣性（跨幾個 persona/透鏡） | | Cognitive Load | How mentally taxing was this? (5 = effortless) | 認知負擔 |

Collect 3 sessions before drawing conclusions. See guide.md for the full A/B experiment protocol.

收集 3 次工作階段資料再下結論。完整 A/B 實驗協議見 guide.md。

Flags | 旗標

| Flag | Effect | 說明 | |------|--------|------| | --personas "a,b,c" | Override the default persona ensemble | 覆寫預設 persona 組 | | --lens analogical\|reversal\|morphological | Force a primary diversity lens | 指定主要多樣性透鏡 | | --enhanced | Parallel persona/critic agents (falls back if unsupported) | 平行 persona/評審 agent（不支援則退回） | | --skip-preflight | Skip Phase 0 with warning | 跳過 Phase 0，顯示錨定警告 | | --no-rebuttal | Skip rebuttal round in CONVERGE | 跳過反駁輪，報告標注 skipped | | --quick | 3-idea fast mode; gates and rebuttal exempt | 快速 3 想法模式；門檻與反駁均豁免 | | --technique scamper | Force SCAMPER as primary technique | 強制使用 SCAMPER |

Usage | 使用方式

• /brainstorm — Start interactive brainstorming session
• /brainstorm "user retention" — Brainstorm around a specific topic
• /brainstorm --enhanced "user retention" — Parallel persona ensemble (if host supports it)
• /brainstorm --personas "designer,economist,skeptic" "pricing" — Custom personas
• /brainstorm --lens analogical "onboarding" — Force the analogical lens
• /brainstorm --quick "reduce checkout friction" — Fast 3-idea mode
• /brainstorm --no-rebuttal "topic" — Skip rebuttal round

Next Steps Guidance | 下一步引導

After /brainstorm completes, the AI assistant should suggest:

腦力激盪完成。建議下一步 / Brainstorming complete. Suggested next steps:

• 執行 /requirement 將最佳構想轉為使用者故事 — Convert top idea to user stories
• 執行 /sdd 直接建立規格（若需求已明確）⭐ Recommended / 推薦 — Create spec directly
• 針對特定構想進行更深入探索 — Explore a specific idea further

Reference | 參考

• Detailed guide: guide.md

AI Agent Behavior | AI 代理行為

完整的 AI 行為定義請參閱對應的命令文件：/brainstorm

For complete AI agent behavior definition, see the corresponding command file: /brainstorm