From great_cto
Structured idea generation with multi-LLM debate panel to pressure-test and converge on product or architectural decisions. For product-owner ideation and architect design-space exploration.
How this skill is triggered — by the user, by Claude, or both
Slash command
/great_cto:brainstormingWhen to use
Apply when: - product-owner is turning a raw idea/problem into a validated brief - the decision is "what/whether to build", not "how to build it" - an idea needs adversarial pressure-testing before committing engineering time - architect wants to explore a wide design space before picking an approach
This skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Idea work has three movements: **diverge → debate → converge**. Most teams skip
Idea work has three movements: diverge → debate → converge. Most teams skip the middle one and converge on the first plausible idea. The debate panel is the point of this skill.
From a framed problem (who · cost-of-pain · why-now · success metric), produce 3–5 distinct approaches — different bets, not cosmetic variants. Force diversity along at least one axis each:
For each: the core bet · the smallest version that tests it · the main risk. Reject near-duplicates — if two options share the same bet, drop one and push for a more contrarian alternative.
Four personas, four different models, two rounds. Model diversity matters: different model families fail differently, so they catch different holes.
| Persona | Stance — argues… | Model | Invocation |
|---|---|---|---|
| Visionary | the strongest case FOR — the 10x outcome if it works | claude-opus-4-8 | Task, model: opus |
| Skeptic | the strongest case AGAINST — why it fails / who tried & died | claude-sonnet-4-6 | Task, model: sonnet |
| User-Advocate | the user's honest reaction — would I pay / switch / care? AND, for any product that messages or collects data from end-recipients, the recipient's consent/opt-in friction (TCPA / opt-out / spam fatigue / who refuses) | claude-haiku-4-5 | Task, model: haiku |
| Pragmatist | cost, time-to-ship, build-vs-buy, unit economics | Kimi K2 | mcp__great_cto_llm_router__ask_kimi |
Spawn the three Task personas in one message (parallel) + call the Kimi
router for the Pragmatist. Each gets the framed problem + the diverge options and
only its own stance. Prompt template:
You are the {persona} on a product debate panel. Stance: {stance}. Problem: {framing}. Options on the table: {options}. Make the strongest possible {for/against} case. Be specific and concrete — name the mechanism, the comparable, the number. End with: verdict (BUILD / DON'T / PIVOT-to-which-option) + your single biggest worry.
Feed each persona the other three's Round-1 positions. Ask:
Here are the other panelists' positions: {r1_others}. Rebut the one you most disagree with. Then give your updated verdict and the one condition that would change your mind.
If all four agree in Round 1, the framing was too soft — re-run with a sharper, explicitly contrarian Skeptic ("assume this is a bad idea; prove it"). Genuine consensus only counts when the Skeptic was given every chance to kill it.
The product-owner (Opus) is the chair, not a vote-counter. Read all eight statements and produce:
Feed this straight into the product brief's Debate digest section.
The panel is ~$0.30–0.60 per idea (one Opus + one Sonnet + one Haiku + one Kimi call × 2 rounds). That is the cheapest insurance in the pipeline: it runs before any engineering time is spent, at the stage where "no" is free and "yes" is expensive.
npx claudepluginhub avelikiy/great_ctoConducts multi-persona debates for founder decisions with 4 grounded personas (Operator, Buyer, Investor, Contrarian) across structured rounds. Outputs transcript, recommendation, and decision log.
Implements debate protocols, cross-examination patterns, and synthesis techniques for multi-agent teams in idea validation, PRD reviews, and competitive analysis.
Facilitates structured brainstorming for problem exploration, design decisions, approach comparisons, and trade-off evaluations using 8 LLM bias-counteracting methods with parallel subagent deep dives.