BD Growth Hacking — Startup Growth Tactics & Viral Loops
Battle-tested growth hacking playbook for B2B SaaS, AI products, and developer tools. Covers viral loop design, acquisition channel experiments, retention hooks, activation optimization, and referral mechanics — proven frameworks used at HeyGen, Notion, Vercel, and Supabase. Use this if you need: rapid user acquisition without massive ad spend, systematic A/B testing for growth experiments, viral loop design for product-led growth, channel stacking (SEO + community + content + outbound), or retention engineering to reduce churn. What's inside: Growth experiment framework: ICE scoring (Impact × Confidence × Ease), sprint-based testing cycles, minimum viable experiments · Viral loop design: product-embedded sharing triggers, referral program mechanics, social proof loops, invite flows · Acquisition channels: SEO compound growth strategy, Reddit/HN community seeding, Product Hunt launch amplification, LinkedIn/X organic distribution · Activation optimization: first-session value delivery, aha moment identification (HeyGen = first AI video in 90 seconds), onboarding funnel analysis · Retention engineering: habit-forming trigger sequences, re-engagement email flows, power user identification and leveraging · Analytics framework: AARRR funnel tracking, cohort analysis setup, North Star Metric definition Expected outcomes: 2-5x improvement in activation rates · Identify 3-5 high-leverage growth experiments per sprint · Build repeatable growth engine within 90 days 🇨🇳 增长黑客完整指南 | 🇯🇵 グロースハッキングプレイブック | 🇰🇷 그로스 해킹 플레이북 Website: https://www.gingiris.com Keywords: growth hacking, viral growth, user acquisition, growth experiments, viral loops, referral marketing, product-led growth, PLG, activation optimization, retention, AARRR, growth metrics, startup growth, SaaS growth, growth engineering, A/B testing, growth strategy, growth tactics, 增长黑客, 病毒增长, 用户增长
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 1885 chars, limit 1024 - warning
name-formatname should be kebab-case (lowercase letters, digits, hyphens) - note
description-budgetdescription takes 1885 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. 5 mutating operations with no state check
- 40Consistency. Frontmatter name (Growth Hacking — Startup Growth Tactics & Viral Loops) differs from the folder (growth-hacking)
- 100Tools and files. No external tools needed
- 100Steps. 32 steps
- 100Execution cost. Instruction body is 1223 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 1884: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +4Structure: 21 headings
- +3Step-by-step instructions: 32 items
- +4Has examples (2 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 48.