Plan a new product launch around finding early customers, guiding their first use, and running short experiments. The skill also helps diagnose stalled adoption, document repeatable outreach methods, and assess whether customer demand supports further expansion.
Launch new products from idea to first customers. Use when launching products, finding early adopters, building launch week playbooks, diagnosing why adoption stalls, or learning that press coverage does not equal growth. Includes the three-layer diagnosis, the 2-week experiment cycle, and the launch that got 50K impressions and 12 signups.
Review the instructions the agent will receive before you install.
Launch new products from idea to first customers. The goal isn't headlines — it's finding 10 customers who can't live without you.
Triggers:
Context:
The Pattern:
Coordinated a feature launch with full press tour. TechCrunch, VentureBeat, product blogs. Big announcement day.
Result:
Why It Failed:
Optimized for media buzz, not user value. The feature wasn't ready for self-serve. It needed education, context, hand-holding. Press gives you eyeballs. But eyeballs without activation = vanity.
What Works Better:
Email 50 target customers directly. "We built [feature] because teams like yours struggle with [problem]. Want early access?" Walk them through setup personally. Get feedback, iterate.
Result: 50 emails → 15 replies (30% reply rate) → 8 trials → 4 conversions (50% trial-to-paid).
The Lesson:
Early customers come from direct outreach, not press coverage. Press matters later (Series A announcement, major milestone). For 0-to-1, it's distraction.
The Pattern:
You launched. You have some awareness. But conversion is weak. The problem lives in one of three layers, and each requires a different intervention.
Layer 1: Positioning Problem
Symptoms:
Diagnosis: You're "fighting an asymmetric war on the wrong front" — competing on features against better-funded companies. Map where competitors claim unique value. Find the position they can't easily copy.
Fix: Stake a claim you can own structurally (not just through product features). Test with outbound messaging before committing product resources.
Layer 2: Experience Problem
Symptoms:
Diagnosis: Flexibility without opinionated defaults is a liability, not a feature. Users face the "paradox of choice" — too many options, not enough guidance to the aha moment.
Fix: Identify 2-3 "undeniable use cases" that deliver immediate value. Restrict onboarding to those specific use cases. Gate advanced features behind a mastery path. Rewrite help content around jobs-to-be-done, not feature lists.
Layer 3: Alignment Problem
Symptoms:
Diagnosis: "Exploratory mode" — where every initiative has equal priority — becomes destructive when resources are constrained.
Fix: Define a single shared north star. Use it as tiebreaker for every decision: "Does this help us win a customer?" Cut activities that don't ladder up. Make progress visible weekly, not quarterly.
How to Use This:
When a launch stalls, diagnose which layer is broken before throwing resources at it. Fixing experience when the problem is positioning wastes engineering time. Fixing positioning when the problem is internal alignment wastes marketing spend.
Principle: First 10 customers are not for revenue. They're for learning.
What You're Learning:
How to Find Them:
Channel 1: Personal Network (first 2-3)
Channel 2: Direct Outreach (customers 3-20)
Channel 3: Ceiling Moment Targeting (highest-intent)
Channel 4: Community (developer products)
The Pattern:
Speed in early stages matters more than perfection. The constraint isn't whether you're right — it's how quickly you can test assumptions and iterate.
How to Execute:
The Playbook Rule:
Every successful experiment must become a playbook before scaling. Structure: Goal → Steps → Expected output → Metrics → Risks. If someone unfamiliar can't execute the playbook, it's not documented well enough.
Why This Matters:
One-off wins don't compound. Systematized experiments do. The goal isn't a single launch — it's building a repeatable machine for testing assumptions at speed.
Common Mistake:
Over-planning before testing. Waiting for "perfect" conditions before launching. Staying with failing experiments too long because you've invested emotional energy. Make decisions with 70% information.
The Pattern:
Rather than entering new markets through direct sales alone, use partnerships with established players to accelerate.
How to Execute:
The Supernode Pattern:
Position yourself as the integration hub that other tools naturally connect through. You own critical data or workflows that other platforms need. This compounds — each new partner makes you more valuable to the next.
Category Sequencing:
Don't pursue partnerships everywhere. Dominate 2-3 categories per quarter:
Common Mistake:
Launching partnerships without clear integration pathways. Expecting partners to drive awareness without support. Treating partnerships as a sales channel rather than platform expansion.
Product-market fit is when customers pull you forward, not when you push them.
Retention:
Organic Growth:
Sales Velocity:
Qualitative:
If you don't have these, you don't have PMF yet. Don't scale marketing/sales.
Do prospects understand what you are?
├─ No → Layer 1: Positioning problem
│ Fix: Test new messaging before changing product
└─ Yes → Continue...
│
Do users activate after signing up?
├─ No → Layer 2: Experience problem
│ Fix: Restrict onboarding to 2-3 use cases, guide to aha moment
└─ Yes → Continue...
│
Is the team aligned on what matters?
├─ No → Layer 3: Alignment problem
│ Fix: Single north star, weekly visibility, cut non-essential
└─ Yes → Keep iterating, you're on the right track
Self-serve ready? (Users get value in <10 min)
├─ No → Direct outreach only (press won't convert)
└─ Yes → Do you have >$1M funding to announce?
├─ Yes → Both (press for awareness, outreach for conversion)
└─ No → Direct outreach first, press later
1. Optimizing for headlines instead of activation 50K impressions and 12 signups. Press ≠ growth.
2. No target customer list before launch Spray-and-pray doesn't work at 0-to-1. Build the list of 100 accounts first.
3. Flexibility without defaults Giving users every option paralyzes them. Pick 2-3 undeniable use cases and guide hard.
4. Giving product away for free Free users give polite feedback. Paying users give honest feedback.
5. Scaling before learning First 10 customers are for learning, not revenue. Document everything.
6. Over-planning, under-testing 2-week experiments with clear kill criteria. Move fast, document learnings.
7. Diagnosing the wrong layer Positioning fix when the problem is experience = wasted marketing. Experience fix when the problem is positioning = wasted engineering.
Three-layer diagnosis: Layer 1: Positioning (messaging sounds like competitors) → Test new messaging Layer 2: Experience (awareness but no activation) → Guide to aha moment Layer 3: Alignment (team scattered) → Single north star, weekly visibility
First 10 customers: Personal network (2-3) → Direct outreach (3-20) → Ceiling moment targeting (highest intent) → Community (developer products)
2-week experiment cycle: Hypothesis → Success criteria → Test (2 weeks max) → Kill or 3x → Document playbook
PMF signals: 40%+ Week 1→4 retention + word-of-mouth + shortening sales cycles + >40% very disappointed
Partner-led entry: Customer problem first → Narrow pilot → Reference customers together → Leverage their GTM
Based on launching features that optimized for press and got 12 signups from 50K impressions, diagnosing launch stalls across three companies using the three-layer model, and building the 2-week experiment cycle that turned ad hoc testing into a repeatable machine. Also draws on partner-led market entry across multiple geographies and segments. Not theory — lessons from mistaking vanity metrics for growth and learning to diagnose the actual problem.
No reviews yet. Tell others what you thought.
No login needed. Share how it worked for you, ask a question, or note where it did not run.
No comments yet. Be the first to write one.
Map an existing codebase into seven documents covering its technology, structure, architecture, conventions, integrations, tests, and concerns. Link findings to file evidence, mark unknowns, and highlight differences between stated plans and the actual implementation.
Assess a repository’s readiness for AI agents with AgentRC and generate a standalone HTML dashboard. Review scores across nine areas, see prioritized improvements, and optionally apply scoring policies tailored to an organization.
Generate coding agent instruction files from a project's actual code using AgentRC. Capture detected languages, frameworks, and conventions in one shared file or split rules by topic and project area, with a preview before writing.
Customize AgentRC readiness assessments to match team or organization standards. Choose or create policies that adjust check importance, disable irrelevant checks, set pass thresholds, and define requirements for automated checks in CI workflows.
Analyze supplied ad performance data to identify wasted spend, strong campaigns, and next tests. Compare channels, assess whether test differences are meaningful, and produce budget reallocation recommendations with a prioritized action plan.
Turn code files into learning resources by adding comments that explain syntax, programming concepts, and design choices. Adjust explanation depth and repetition to the reader’s knowledge while preserving file structure, encoding, and syntax.