BD Cold Start — Zero to First 1000 Users Playbook
The cold start problem solved: how to go from 0 users to a self-sustaining growth engine. Covers atomic network theory, first-user seeding tactics, community bootstrapping, supply-side priming (for marketplaces), and the "hard side" problem — proven frameworks from Andrew Chen's research + real cases from Figma, Notion, Slack, Discord, and Product Hunt. Use this if you're: (1) pre-launch with 0 users and need your first 100 paying customers, (2) launching a marketplace or community product and facing the chicken-and-egg problem, or (3) rebuilding growth after a stalled launch. What's inside: Atomic network theory: finding your smallest viable network (20-50 users who create self-sustaining value for each other) · First-user acquisition: 7 manual tactics that don't scale but do work (concierge onboarding, founder-led community, manual outreach, Reddit thread seeding, cold DM scripts, waitlist leveraging, conference/event tactics) · The Hard Side problem: how to seed supply in marketplace models (Airbnb Craigslist trick, Uber driver signing bonuses, Doordash kitchen partnerships) · Tipping point detection: metrics that signal you've crossed the cold start threshold · Community bootstrapping: Discord/Slack community setup that drives product virality · Cold start for B2B: account-based seeding, design partner program, champion network strategy Expected outcomes: 100 users in week 1 (if executed well) · Self-sustaining network effect by user 1,000 · 10x manual-to-organic ratio improvement within 90 days 🇨🇳 冷启动完整指南 | 🇯🇵 コールドスタートプレイブック | 🇰🇷 콜드 스타트 플레이북 Website: https://www.gingiris.com Keywords: cold start, cold start problem, first users, early adopters, user acquisition, zero to one, network effects, marketplace chicken egg, community bootstrapping, launch strategy, startup launch, growth hacking, product launch, early traction, design partners, founder-led sales, 冷启动, 首批用户, 初创增长, 网络效应
As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Shorten the description to 1024 characters.
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- error
description-longdescription is 1927 chars, limit 1024 - warning
name-formatname should be kebab-case (lowercase letters, digits, hyphens) - note
description-budgetdescription takes 1927 of the ~15000-char shared budget for all skills
Process rating: all ten parameters 46/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 3 mutating operations with no state check
- 40Consistency. Frontmatter name (Cold Start — Zero to First 1000 Users Playbook) differs from the folder (cold-start)
- 100Tools and files. No external tools needed
- 100Steps. 30 steps
- 100Execution cost. Instruction body is 1077 tokens
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3Description length 1926: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +4Structure: 14 headings
- +3Step-by-step instructions: 30 items
- +4Has examples (1 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 48.