Multi Platform Launch

Multi-Platform Launch Skill

Overview

This public intake copy packages packages/skills-catalog/skills/(gtm)/multi-platform-launch from https://github.com/tech-leads-club/agent-skills into the native Omni Skills editorial shape without hiding its origin.

Use it when the operator needs the upstream workflow, support files, and repository context to stay intact while the public validator and private enhancer continue their normal downstream flow.

This intake keeps the copied upstream files intact and uses the external_source block in metadata.json plus ORIGIN.md as the provenance anchor for review.

Multi-Platform Launch Skill You are a product launch strategist who has studied hundreds of successful launches across Product Hunt, Hacker News, BetaList, AppSumo, and 20+ directory/community platforms. You help founders plan, sequence, and execute multi-channel launches that maximize first-week momentum and long-tail discovery.

Imported source sections that did not map cleanly to the public headings are still preserved below or in the support files. Notable imported sections: Before Starting, Platform Landscape (2025-2026), Launch Sequence Framework, Product Hunt Deep Dive, Hacker News (Show HN) Deep Dive, Waitlist Building Playbook.

When to Use This Skill

Use this section as the trigger filter. It should make the activation boundary explicit before the operator loads files, runs commands, or opens a pull request.

• Use when the request clearly matches the imported source intent: When the user wants to launch a product across multiple platforms, plan a Product Hunt launch, build a waitlist, or execute a multi-channel launch strategy. Also use when the user mentions 'product launch,' 'Product....
• Use when the operator should preserve upstream workflow detail instead of rewriting the process from scratch.
• Use when provenance needs to stay visible in the answer, PR, or review packet.
• Use when copied upstream references, examples, or scripts materially improve the answer.
• Use when the workflow should remain reviewable in the public intake repo before the private enhancer takes over.

Operating Table

Workflow

This workflow is intentionally editorial and operational at the same time. It keeps the imported source useful to the operator while still satisfying the public intake standards that feed the downstream enhancer flow.

1. Confirm the user goal, the scope of the imported workflow, and whether this skill is still the right router for the task.
2. Read the overview and provenance files before loading any copied upstream support files.
3. Load only the references, examples, prompts, or scripts that materially change the outcome for the current request.
4. Execute the upstream workflow while keeping provenance and source boundaries explicit in the working notes.
5. Validate the result against the upstream expectations and the evidence you can point to in the copied files.
6. Escalate or hand off to a related skill when the work moves out of this imported workflow's center of gravity.
7. Before merge or closure, record what was used, what changed, and what the reviewer still needs to verify.

Imported Workflow Notes

Imported: Before Starting

Gather these inputs before producing a launch plan:

1. Product type - SaaS, dev tool, AI tool, marketplace, mobile app, physical product
2. Target buyer - developers, SMB owners, enterprise, creators, consumers
3. Current assets - existing audience size, email list, social following, community
4. Launch goal - signups, revenue, press, backlinks, community building, fundraising signal
5. Timeline - how many weeks until desired launch date
6. Budget - $0 bootstrap, <$500 indie, <$5K startup, $5K+ funded
7. Team size - solo founder, 2-3 person team, full team with marketing

If the user has not provided these, ask before building the plan. A launch plan without audience context will produce generic advice.

Examples

Example 1: Ask for the upstream workflow directly

Use @multi-platform-launch to handle <task>. Start from the copied upstream workflow, load only the files that change the outcome, and keep provenance visible in the answer.

Explanation: This is the safest starting point when the operator needs the imported workflow, but not the entire repository.

Example 2: Ask for a provenance-grounded review

Review @multi-platform-launch against metadata.json and ORIGIN.md, then explain which copied upstream files you would load first and why.

Explanation: Use this before review or troubleshooting when you need a precise, auditable explanation of origin and file selection.

Example 3: Narrow the copied support files before execution

Use @multi-platform-launch for <task>. Load only the copied references, examples, or scripts that change the outcome, and name the files explicitly before proceeding.

Explanation: This keeps the skill aligned with progressive disclosure instead of loading the whole copied package by default.

Example 4: Build a reviewer packet

Review @multi-platform-launch using the copied upstream files plus provenance, then summarize any gaps before merge.

Explanation: This is useful when the PR is waiting for human review and you want a repeatable audit packet.

Imported Usage Notes

Imported: Examples

• User says: "Plan our Product Hunt launch" → Result: Agent asks timeline, audience size, and product state; recommends 2 weeks before (landing page, waitlist, PH draft, 20–50 supporters, assets, UTM); launch day (PH 12:01 AM PT, Show HN 8–10 AM, X thread, waitlist email); Week +1 (demo video, retro post, directories, reviews).
• User says: "We're launching on multiple channels" → Result: Agent maps 16+ channels (PH, HN, X, LinkedIn, BetaList, directories, community); prioritizes by audience; sets launch day order and post-launch cadence; warns on common mistakes (Friday launch, no demo, going silent after Day 0).
• User says: "We have no audience for launch" → Result: Agent suggests waitlist build first, 20+ supporters for Day 0, and community/outreach; recommends building in public and content pre-launch; ties to content-to-pipeline and solo-founder-gtm if needed.

Best Practices

Treat the generated public skill as a reviewable packaging layer around the upstream repository. The goal is to keep provenance explicit and load only the copied source material that materially improves execution.

• Keep the imported skill grounded in the upstream repository; do not invent steps that the source material cannot support.
• Prefer the smallest useful set of support files so the workflow stays auditable and fast to review.
• Keep provenance, source commit, and imported file paths visible in notes and PR descriptions.
• Point directly at the copied upstream files that justify the workflow instead of relying on generic review boilerplate.
• Treat generated examples as scaffolding; adapt them to the concrete task before execution.
• Route to a stronger native skill when architecture, debugging, design, or security concerns become dominant.

Troubleshooting

Problem: The operator skipped the imported context and answered too generically

Symptoms: The result ignores the upstream workflow in packages/skills-catalog/skills/(gtm)/multi-platform-launch, fails to mention provenance, or does not use any copied source files at all. Solution: Re-open metadata.json, ORIGIN.md, and the most relevant copied upstream files. Check the external_source block first, then restate the provenance before continuing.

Problem: The imported workflow feels incomplete during review

Symptoms: Reviewers can see the generated SKILL.md, but they cannot quickly tell which references, examples, or scripts matter for the current task. Solution: Point at the exact copied references, examples, scripts, or assets that justify the path you took. If the gap is still real, record it in the PR instead of hiding it.

Problem: The task drifted into a different specialization

Symptoms: The imported skill starts in the right place, but the work turns into debugging, architecture, design, security, or release orchestration that a native skill handles better. Solution: Use the related skills section to hand off deliberately. Keep the imported provenance visible so the next skill inherits the right context instead of starting blind.

Imported Troubleshooting Notes

Imported: Troubleshooting

• Low PH/HN traction → Cause: Wrong day, weak tagline, or no supporters. Fix: Launch Tue–Thu; sharpen tagline and first comment; line up 20+ upvotes/comments in first hour; respond to every comment in 30 min.
• Traffic spike but no signups → Cause: No clear CTA or try-it-now. Fix: Offer instant demo or waitlist; one primary CTA above fold; UTM on all links to attribute.
• Launch day then silence → Cause: No Week +1 plan. Fix: Post daily updates Week +1; YouTube demo, retro post, directory batch, G2/Capterra reviews; compile metrics and 30-day plan.

For checklists, benchmarks, and discovery questions read references/quick-reference.md when you need detailed reference.

Related Skills

• @accessibility - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @ai-cold-outreach - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @ai-pricing - Use when the work is better handled by that native specialization after this imported skill establishes context.
• @ai-sdr - Use when the work is better handled by that native specialization after this imported skill establishes context.

Additional Resources

Use this support matrix and the linked files below as the operator packet for this imported skill. They should reflect real copied source material, not generic scaffolding.

• directories-timing-mistakes.md
• quick-reference.md
• directories-timing-mistakes.md
• quick-reference.md

Imported Reference Notes

Imported: Platform Landscape (2025-2026)

Discovery Platforms

Developer Platforms

Indie/Founder Platforms

AI/Tech Directories

Social Channels

Content Channels

Paid/Review Platforms

Note: G2 is acquiring Capterra/GetApp/Software Advice from Gartner (closing Q1 2026).

Imported: Launch Sequence Framework

Week -4 to -3: FOUNDATION
  |
  |-- Define positioning (use positioning-icp skill)
  |-- Build landing page with waitlist capture
  |-- Set up analytics and attribution tracking
  |-- Create asset library (screenshots, demo video, logo pack)
  |-- Draft Product Hunt page (do not publish)
  |-- Begin building in public content on X/LinkedIn
  |
Week -2: WAITLIST SEEDING
  |
  |-- Submit to BetaList
  |-- Submit to 10-15 free directories (batch submission day)
  |-- Start sharing waitlist link in relevant communities
  |-- Begin warm outreach to 20-50 people for launch day support
  |-- Post "building in public" updates daily on X
  |-- Cross-promote with aligned newsletter creators
  |
Week -1: LAUNCH PREP
  |
  |-- Finalize Product Hunt page (gallery, tagline, maker comment)
  |-- Prepare Hacker News Show HN post (if technical product)
  |-- Brief your launch day support crew with specific instructions
  |-- Schedule social media posts for launch day
  |-- Submit to Microlaunch, Dev Hunt, Indie Hackers
  |-- Pre-write launch thread for X (7-10 posts)
  |-- Record and edit YouTube demo video
  |
Day 0: LAUNCH DAY
  |
  |-- 12:01 AM PT: Product Hunt goes live
  |-- 8:00-10:00 AM PT: Post Show HN on Hacker News
  |-- Morning: Publish X launch thread
  |-- Morning: Post on LinkedIn
  |-- Morning: Share in Discord/Slack communities
  |-- All day: Respond to every PH comment within 30 minutes
  |-- All day: Engage with every HN comment authentically
  |-- Evening: Share progress update on X
  |
Week +1: AMPLIFICATION
  |
  |-- Publish YouTube deep-dive demo
  |-- Write launch retrospective blog post
  |-- Start podcast outreach circuit
  |-- Post on relevant Reddit communities
  |-- Submit to remaining directories
  |-- Collect and share launch metrics publicly
  |
Week +2 to +4: MONETIZATION & PROOF
  |
  |-- Launch AppSumo deal (if applicable)
  |-- Request G2/Capterra reviews from early users
  |-- Repurpose UGC from launch into social content
  |-- Begin SEO content strategy (use ai-seo skill)
  |-- Start building case studies from early adopters

Imported: Product Hunt Deep Dive

How the 2025-2026 Algorithm Works

Product Hunt's team manually curates which products appear on the homepage. They evaluate products on four factors:

1. Useful - solves a real problem
2. Distinctive - clearly different from existing solutions
3. User-Friendly - polished, easy to try
4. Complete - feels like a finished product, not a landing page

The algorithm hides upvote counts for the first 4 hours to distribute exposure more fairly. Early velocity still matters, but gaming is harder than before.

Product Hunt Launch Page Anatomy

Title Strategy: Three Variants

Prepare three title options with distinct angles:

Test these with 5-10 people before launch. Pick the one with the strongest immediate reaction.

Launch Day Execution Roles

For teams of 2+, assign these roles explicitly:

Solo founders: prioritize Thread Owner and Social Driver. Let support queue until evening.

Performance Benchmarks (2025)

Products in the top 6 appear on the first page without scrolling. This is the target.

What to Avoid on Product Hunt

Never ask for upvotes publicly (X, LinkedIn, email blasts) or use exchange groups (IPs get flagged). Avoid Monday/Friday launches (low engagement). Do not launch a landing page with no working product. Always respond to comments (responsiveness affects ranking).

Imported: Hacker News (Show HN) Deep Dive

What Works on HN

Show HN is for things people can run, use, or interact with. Blog posts, signup pages, and newsletters are explicitly off-topic for Show HN.

Ideal post types:

• Live demo with no signup required
• Open-source tool with GitHub repo
• Technical deep-dive with benchmarks
• Novel approach to a known problem

Title formula: Front-load a concrete benefit or intrigue. Use digits, version numbers, or specific results. Avoid superlatives, listicle formats, and clickbait.

Good: "Show HN: Open-source tool that converts Figma designs to React in 30 seconds" Bad: "Show HN: The Best Design-to-Code Tool Ever Made"

Timing

Check hn.algolia.com before posting to verify your slot is not crowded by major stories.

Algorithm Mechanics

HN uses time-decayed scoring. The gravity multiplier increases every ~45 minutes. A post with 10 upvotes in 15 minutes outranks one with 50 upvotes over 6 hours. Early velocity is everything.

Early Upvote Sources (Legitimate)

Use: email list of technical HN-active subscribers, private Slack/Discord with members holding 1+ year HN accounts, direct messages to founder friends on HN.

Never use: public X/LinkedIn upvote asks, "upvote party" groups, new account rings. Shadow-banning is aggressive and permanent.

HN Performance Benchmarks

Expect brutally honest feedback. Respond to criticism thoughtfully and you earn respect.

Imported: Waitlist Building Playbook

Waitlist-driven launches convert at 25-85% vs. 2-4% for cold launches, with near-zero customer acquisition cost. AI-powered waitlist optimization adds ~30% to conversion rates.

Waitlist Architecture

Landing Page (value prop + social proof) -> Email Capture (name, email, use case tag) -> Thank You + Referral Loop (share to move up in line). Each stage feeds a different engine: SEO/social traffic, segmentation for launch day personalization, and viral growth.

Referral-Based Waitlist Growth

The highest-performing waitlists use tiered referral rewards:

Tools: LaunchList (getlaunchlist.com), Waitlister, Viral Loops, ReferralHero

Waitlist Conversion Tactics

Use scarcity framing ("Onboarding 50 users per week"), show position in line, tag users by role/use case at signup for personalized launch emails, send weekly build updates to keep the list warm, and let top referrers in early for pre-launch testimonials.

Waitlist Size Benchmarks

For directory submission, AppSumo, G2/Capterra, UGC scaling, budget framework, timing, success metrics, and common mistakes read references/directories-timing-mistakes.md.