BD App Launch — Mobile App Launch Strategy & First 1000 Users
Step-by-step mobile app launch playbook from soft launch to 10K daily active users. Covers pre-launch community building, App Store/Google Play launch optimization, influencer seeding, UGC creator programs, and post-launch growth loops — validated across 20+ successful mobile app launches. Use this if you're: (1) launching a new mobile app and need a structured 90-day go-to-market plan, (2) stuck at under 1,000 downloads/day and need to break through, or (3) building an ASO foundation while simultaneously running creator/UGC programs. What's inside: Pre-launch (T-60 to T-0): landing page with waitlist, beta community seeding (TestFlight/Play Beta), micro-influencer seeding (5K-50K followers), press kit creation, App Store metadata optimization · Launch week: coordinated push across Product Hunt mobile launch, Reddit relevant subreddits, TikTok seeding, press outreach to tech media · Post-launch: UGC creator program setup ($10-200/video tiered pricing), TikTok/Reels/Shorts content matrix, retention loop optimization, ASO iteration based on first-week data · Growth loops: viral sharing features, referral mechanics, social proof accumulation strategy Expected outcomes: 1,000 downloads in first week · 4.5+ star rating maintained · 500+ downloads/day within 90 days via organic channels 🇨🇳 App发布完整指南 | 🇯🇵 アプリローンチガイド | 🇰🇷 앱 런칭 플레이북 Website: https://www.gingiris.com Keywords: app launch, mobile app launch, app marketing, launch strategy, app store launch, Google Play launch, first 1000 users, app growth, mobile app marketing, UGC creators, TikTok marketing, influencer marketing, App Store optimization, app cold start, beta launch, TestFlight, app promotion, app installs, user acquisition, 应用发布, App上线, 冷启动
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 1738 chars, limit 1024 - warning
name-formatname should be kebab-case (lowercase letters, digits, hyphens) - note
description-budgetdescription takes 1738 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 (App Launch — Mobile App Launch Strategy & First 1000 Users) differs from the folder (app-launch)
- 100Tools and files. No external tools needed
- 100Steps. 17 steps
- 100Execution cost. Instruction body is 923 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 1737: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +4Structure: 17 headings
- +3Step-by-step instructions: 17 items
- +4Has examples (3 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 48.