1. Phase 1 (Intake) — shortened to 1 round of questions
2. Phase 2 (Brainstorm) — 3 variations instead of 5-8
3. Phase 3 (Research) — Wave 1 + Wave 2 only (skip customer voice and distribution deep-dives)
4. Phase 4 (Strategy) — Lean Canvas only
5. Skip Phase 5 (Brand) and Phase 6 (Product)
6. Phase 7 (Financial) — Revenue model only, no full projections
7. Phase 8 (Validation) — Scorecard + top 3 experiments only

Reference: Read

references/output-guidelines.md

once at the start. It defines the standard file header/footer (title, date, phase, confidence, flags), cross-phase referencing format, quality examples of good vs. bad output, and how to handle mid-process pivots.

• What problem are you solving? Who has this problem?
• What's your proposed solution? How does it work?
• What triggered this idea? (personal pain, market observation, technical insight)
• Do you have any existing work? (prototypes, research, landing pages, waitlists)

• What's your background? Relevant domain expertise?
• Are you solo or do you have co-founders? What are their strengths?
• How much time can you dedicate? Full-time or side project?
• What's your budget/runway situation?

• Who is your ideal customer? Be as specific as possible.
• How do they currently solve this problem? (existing alternatives, workarounds)
• Do you know of direct competitors? Who are they?
• What geography/market are you targeting first?

• How do you plan to make money? (subscription, one-time, marketplace, freemium)
• Any idea of pricing?
• What does success look like in 6 months? 12 months? 3 years?
• What are your biggest unknowns or worries?

• Any technical constraints? (must be mobile-first, needs to integrate with X)
• Any strong opinions on brand/positioning? (premium vs accessible, playful vs serious)
• Regulatory considerations?

• "Why are you the right person to build this? What unfair advantage do you have?"
• "If Google/a well-funded competitor launched this tomorrow, what would you do?"
• "What's the strongest argument against this idea?"
• "Have you talked to potential customers? What did they actually say (not what you hoped they'd say)?"
• "What would make you walk away from this idea?"

• Ask 3-5 questions at a time, not all at once
• Acknowledge and build on answers — show you're listening
• Probe vague answers: "You said 'small businesses' — can you narrow that down? Like, freelancers? 10-person agencies? Local retail?"
• After 2-3 rounds, summarize what you've understood and ask the user to confirm or correct

1. Diverge — Generate 5-8 variations of the core idea. Push boundaries:
   • What if the target market was completely different?
   • What if the business model was inverted?
   • What if you solved a smaller/larger version of the problem?
   • What adjacent problems could you solve instead?
   • What would the "10x version" look like vs. the "simplest possible version"?
2. Analyze — For each variation, note:
   • What's exciting about it
   • What's risky or hard
   • How it changes the competitive landscape
3. Converge — Present the variations to the user. Help them identify which elements resonate. The goal isn't to pick one variation — it's to enrich the original idea with insights from the exploration.
4. Refine — Based on the user's reactions, crystallize the refined idea. Update the brief if the idea evolved significantly.

References — Read the relevant file for each wave:

• references/research-principles.md

  — Cross-cutting rules (source quality, cross-referencing, quantification, handling search failures). Read this FIRST.

• references/research-wave-1-market.md

  — Agent templates for Wave 1 (market sizing, trends, regulatory)

• references/research-wave-2-competitors.md

  — Agent templates for Wave 2 (direct, indirect, GTM analysis)

• references/research-wave-3-customers.md

  — Agent templates for Wave 3 (customer voice, demand, audience)

• references/research-wave-4-distribution.md

  — Agent templates for Wave 4 (channels, geographic entry)

• references/research-synthesis.md

  — How to synthesize raw findings into final deliverables

Read only the principles file + the wave file you're currently executing. Don't load all wave files at once.

• Each agent performs 5-8 web searches minimum, drilling deeper with each round
• Cross-reference every key finding across 2-3 independent sources
• Rate source quality (Tier 1: analyst reports, Tier 2: tech press, Tier 3: blogs/social)
• Quantify everything — "$4.2B at 12.3% CAGR" not "the market is growing"
• Date all data and flag anything older than 18 months
• Note contradictions between sources rather than picking one

• Agent 1A: Market Sizing & Economics — TAM/SAM/SOM, unit economics benchmarks, market headwinds
• Agent 1B: Industry Trends & Timing — tech trends, investment/M&A activity, behavioral shifts, expert predictions
• Agent 1C: Regulatory & Compliance — current regulations, data privacy, upcoming changes, compliance costs (Skip 1C if the startup has no regulatory exposure)

• Agent 2A: Direct Competitor Deep-Dives — full profiles on 5-8 competitors (product, pricing, funding, traction, strengths, weaknesses)
• Agent 2B: Indirect Competitors & Substitutes — alternative approaches, platform risk, switching costs
• Agent 2C: Competitor Go-to-Market — how competitors acquire customers, channel analysis, content strategy

• Agent 3A: Customer Voice & Pain Points — Reddit, forums, reviews mining with verbatim quotes, pain hierarchy, language map
• Agent 3B: Demand Signals & Market Validation — search trends, pricing intelligence, Product Hunt signals, WTP evidence
• Agent 3C: Target Audience Profiling — personas, buying behavior, decision-making process, where to reach them

• Agent 4A: Distribution Channel Deep-Dive — channel ranking by ROI, SEO opportunity, partnership opportunities, first 90 days plan
• Agent 4B: Geographic & Market Entry — beachhead market recommendation, entry barriers, expansion path

{project-name}/01-discovery/raw/

• Connect dots across research areas (competitive gaps → customer pains → positioning opportunities)
• Highlight contradictions and explain which data to trust
• Rate confidence for each major claim (High / Medium / Low)
• Extract explicit strategic implications, not just facts

• {project-name}/01-discovery/market-analysis.md

  — Market size (TAM/SAM/SOM), growth, maturity, regulatory summary, timing assessment

• {project-name}/01-discovery/competitor-landscape.md

  — Competitor profiles, structured comparison matrix (table with columns: Name, Product, Pricing, Target, Funding, Traction, Key Strength, Key Weakness), positioning map, platform risk, vulnerability analysis

• {project-name}/01-discovery/target-audience.md

  — Persona(s), pain hierarchy, jobs-to-be-done, language map, buying behavior, channels

• {project-name}/01-discovery/industry-trends.md

  — Tech trends, investment signals, behavioral shifts, regulatory trajectory, strategic implications

• {project-name}/01-discovery/confidence-dashboard.md

  — Summary of data quality across all research. For each major claim, list: the claim, source tier (1/2/3), number of corroborating sources, confidence level (High/Medium/Low), and data age. This tells the founder where they're standing on solid ground vs. thin ice.

• Green light — Data supports proceeding. Note the strongest signals.
• Yellow light — Mixed signals. Specify what's concerning and what would need to be true for success.
• Red light — Data argues against this approach. Suggest pivots if the research revealed adjacent opportunities.

{project-name}/01-discovery/research-gate.md

Reference: Read

references/frameworks.md

for canonical definitions of Lean Canvas, April Dunford Positioning, Value Proposition Canvas, and RICE/MoSCoW prioritization. Use these to ensure consistent, accurate application of each framework.

02-strategy/lean-canvas.md

• Problem (top 3)
• Customer Segments
• Unique Value Proposition
• Solution
• Channels
• Revenue Streams
• Cost Structure
• Key Metrics
• Unfair Advantage

02-strategy/value-proposition.md

• The value proposition canvas (jobs-to-be-done, pains, gains)
• How the product addresses each pain and creates each gain
• The one-sentence value prop
• Proof points and credibility signals

02-strategy/business-model.md

• Revenue model (how money comes in, pricing tiers)
• Unit economics (CAC, LTV, margins — even rough estimates)
• Scalability considerations
• Dependencies and key partnerships

02-strategy/positioning.md

• Competitive alternatives (what happens if you don't exist)
• Unique attributes (what you have that alternatives don't)
• Value (what those attributes enable for customers)
• Target market (who cares the most)
• Market category (the context that makes the value obvious)

02-strategy/go-to-market.md

• Launch strategy (where, how, to whom first)
• First 100 customers plan
• Growth channels ranked by expected impact and cost
• Partnerships and ecosystem plays
• Timeline with milestones

Checkpoint: Before starting, briefly present the strategy summary to the founder: positioning, target market, business model. Ask: "Does this reflect your vision? Anything to adjust before we build the brand on top of it?"

03-brand/mission-vision-values.md

• Mission — Why the company exists (present-tense, action-oriented)
• Vision — The world you're building toward (aspirational but credible)
• Values — 3-5 core values with brief explanations of what they mean in practice (not generic platitudes — values should help make decisions)

03-brand/tone-of-voice.md

• Brand personality traits (3-4 adjectives with definitions)
• Voice principles with "we are / we are not" examples
• Writing samples: how the brand sounds in different contexts (homepage headline, error message, social media post, customer email)
• Vocabulary guide: preferred terms vs. avoided terms

03-brand/brand-personality.md

• Brand archetype analysis (which archetype fits and why)
• Emotional attributes
• Visual direction suggestions (not full design, but adjectives and references)
• How the brand should feel compared to competitors

01-discovery/competitor-landscape.md

01-discovery/target-audience.md

Reference: Use RICE or MoSCoW from

references/frameworks.md

for feature prioritization.

04-product/mvp-definition.md

• Core hypothesis the MVP tests
• Must-have features (the minimum set to test the hypothesis)
• Nice-to-have features (for v1.1, not v1.0)
• Explicitly out of scope (prevent scope creep)
• Success criteria: what results would validate the MVP

04-product/feature-prioritization.md

• Feature list with RICE or MoSCoW prioritization
• Dependencies between features
• Effort estimates (T-shirt sizing: S/M/L/XL)
• Recommended build order

04-product/user-journey.md

• End-to-end user journey map (from discovery to regular usage)
• Key touchpoints and emotions at each stage
• Drop-off risks and mitigation ideas
• The "aha moment" — when does the user first experience value?

Checkpoint: Before projections, confirm key assumptions with the founder: pricing, target customer volume, team size, timeline. These directly drive the numbers — getting them wrong here means the projections are fiction.

01-discovery/market-analysis.md

01-discovery/competitor-landscape.md

Reference: Read

references/industry-benchmarks.md

for standard metrics by business model type (SaaS, marketplace, e-commerce, etc.). Compare the founder's projections against these benchmarks and flag any that fall outside normal ranges — both too pessimistic and too optimistic.

05-financial/revenue-model.md

• Pricing strategy with rationale
• Revenue projections (Month 1-12, Year 1-3) with assumptions stated
• Sensitivity analysis: what happens if key assumptions change by ±30%

05-financial/cost-structure.md

• Fixed costs (infrastructure, tools, salaries)
• Variable costs (per-user, per-transaction)
• One-time costs (development, legal, launch)
• Break-even analysis

05-financial/projections.md

• 3 scenarios: conservative, base, optimistic
• Key assumptions for each scenario
• Cash flow timeline
• Funding needs and runway calculation

06-validation/validation-playbook.md

• Ordered list of experiments to run, from cheapest/fastest to most expensive
• For each experiment:
  • What assumption it tests
  • How to run it (step-by-step)
  • What to measure
  • What result would validate vs. invalidate the assumption
  • Estimated time and cost
• Examples: landing page test, fake door test, concierge MVP, customer interviews, ad campaign test, wizard of oz, pre-sales

06-validation/risk-analysis.md

• Risk matrix (likelihood × impact) for:
  • Market risks (no demand, timing, regulatory)
  • Product risks (can't build it, won't work)
  • Business risks (can't monetize, can't scale)
  • Team risks (missing skills, founder conflicts)
  • Financial risks (runway, CAC too high)
• For each high-priority risk: mitigation strategy and early warning signals

06-validation/assumptions-tracker.md

• Every critical assumption the business plan rests on
• Current confidence level (high/medium/low)
• How to test each assumption
• Status: untested / testing / validated / invalidated

06-validation/experiment-design.md

• Detailed design for the top 3 recommended experiments
• Hypothesis, method, metrics, success criteria, timeline
• Templates the founder can use immediately (interview scripts, survey questions, landing page copy outline)

README.md

• One-paragraph overview of the startup
• Key findings from research
• Strategic positioning summary
• Top 3 risks and how to mitigate them
• Confidence dashboard summary (what we know vs. what we're guessing)
• Links to all generated documents

action-plan-30-days.md

• Week 1: Customer discovery (who to contact, what to ask, how many conversations)
• Week 2: Validation experiments (which ones to run first, with specific steps)
• Week 3: MVP scoping (based on validation results, what to build/fake/skip)
• Week 4: Go/no-go decision and next phase planning
• Each week should have 3-5 specific, actionable tasks — not "do customer research" but "send 20 cold LinkedIn messages to [persona] using this template"

Anti-pattern check: Before finalizing, scan the entire output for common founder anti-patterns and flag any you detect: "solution looking for a problem," "boiling the ocean" (too many features/markets at once), "premature scaling," "vanity metrics," "building in stealth too long," "ignoring unit economics." Include a brief Anti-Patterns Detected section in the README if any are present.

Reference: Read

references/honesty-protocol.md

at the start of every session for the full protocol. The key rules are summarized here.

1. Tell the truth. If the market is too small or the idea has a fatal flaw, say so directly. Flag when founder assumptions contradict research data.
2. Label claims. Use [Data], [Estimate], [Assumption], [Opinion] tags. Never present estimates as facts.
3. Surface flags in every phase. Include a Red Flags / Yellow Flags section at the end of every phase output.
4. Clear verdict. Scorecard must recommend: 8-10 proceed, 6-7 conditional, 4-5 concerns, 1-3 stop/pivot.
5. Ground in evidence. No data? Say so. Don't fabricate.
6. Make it actionable. Every document tells the founder what to do next.
7. Track everything. Update PROGRESS.md after each phase.