Business idea development toolkit — scaffold, pushback, evaluate, and forge disruptive ideas
npx claudepluginhub kaminskypavel/ideation-copilotStructured business idea development with /idea:new, /idea:evaluate, /idea:pushback, /idea:update, and /idea:forge. Includes VC, Market, and YC evaluation agents, lean canvas, assumption tracking, and experiment planning.
Turn a raw idea into a validated, investor-ready concept — using AI agents that research, score, and stress-test your thinking.
/plugin marketplace add kaminskypavel/ideation-copilot
/plugin install ideation-copilot@ideation-copilot
You start with an idea. The copilot guides you through a loop of testing, scoring, and improving until the idea is solid — or killed. Each command tells you what to do next.
| Command | Purpose |
|---|---|
/idea:new [name "description"] | Scaffold a new idea with 6 structured docs |
/idea:evaluate [idea-name] [vc|market|yc] | Score with parallel agents (all by default, or pick one) |
/idea:pushback [idea-name] | Conversational stress-test with web research |
/idea:update [idea-name] | Add new info to your docs (interviews, data, team changes) |
/idea:forge [idea-name] | Synthesize everything into a consolidated summary |
/idea:postmortem [idea-name] | Structured debrief when you kill an idea |
/idea:setup | Check & configure optional integrations (Exa, etc.) |
/idea:new pawguard "Smart collar that detects early signs of illness in dogs using biometrics"
Scaffolds 6 structured docs (overview, brainstorm, lean canvas, assumptions, PMF strategy, experiments). You fill in what you know.
/idea:evaluate pawguard
Three AI agents run in parallel, each researching and scoring your idea from a different angle:
| Agent | What it asks | Dimensions |
|---|---|---|
| VC | "Is this investable?" | Team, Timing, TAM, Technology, Moat, Business Model, GTM, Traction |
| Market Analyst | "Is the market real?" | Market Size, Competitive Landscape, Timing & Tailwinds, Customer Access, Regulatory Risk |
| YC Founder-Fit | "Should YOU start this?" | Problem Acuteness, Personal Demand, Successful Proxies, Commitment, Scalability, Idea Space Fertility |
You get a combined score (0-100), deal-breakers flagged, and the single weakest dimension to fix first. Agents use web research — they'll check your TAM claims, find your competitors, and validate your timing.
Run a single agent: /idea:evaluate pawguard vc or market or yc
/idea:pushback pawguard
An adversarial sparring partner breaks your idea into testable claims and challenges each one. You defend, clarify, or concede. It uses named reasoning tools (inversion, base rate analysis, pre-mortem) and web research to back up its challenges.
This isn't a lecture — it's a conversation. You'll discover blind spots you didn't know you had.
/idea:update pawguard
Low scores often mean your docs are incomplete, not that the idea is bad. Add what's missing — your team background, customer interview results, experiment outcomes, market data.
Run evaluate and pushback again. Your scores should improve. Keep iterating until you're confident — or until the evidence tells you to pivot.
When you're ready, forge synthesizes everything — score trajectory over time, what's validated vs still assumed, key pivots, and a pitch-ready summary:
/idea:forge pawguard
Each idea lives in ideas/YYYY-MM-DD-idea-name/:
You write these:
| File | What goes in it |
|---|---|
00-overview.md | Problem, insight, solution, target customer |
01-brainstorm.md | Problem/solution space exploration |
02-lean-canvas.md | Lean Canvas — UVP, channels, revenue, costs |
03-assumptions.md | Riskiest assumptions ranked, with evidence tracking |
04-pmf-strategy.md | PMF ladder, go-to-market, milestones |
05-experiments.md | Experiment backlog, results, pivot/persevere decisions |
The copilot creates these:
Official prompts.chat marketplace - AI prompts, skills, and tools for Claude Code
Open Design — local-first design app exposed to coding agents over MCP. Install once with your agent's plugin command and projects/files/skills are reachable through stdio.
Behavioral guidelines to reduce common LLM coding mistakes, derived from Andrej Karpathy's observations