Launch logo

Launch

Organization
Rune-kit
launch

Deploy + marketing orchestrator. Use when user says 'launch', 'ship to production', 'deploy and announce', or 'go live'. Runs the full pipeline — pre-flight tests, deployment, live verification, marketing asset creation, and announcement.

Overview

PublisherRune-kit
Repositoryrune
Skill namelaunch
Stars
88
Forks
26
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Rune-kit on GitHub. Read the source before you install it.

Installation

Install the Launch AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Rune-kit/rune/tree/master/skills/launch
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Rune-kit/rune.git /tmp/rune
mkdir -p .claude/skills
cp -r /tmp/rune/skills/launch .claude/skills/launch
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Launch in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Launch on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Launch is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

launch

Purpose

Orchestrate the full deployment and marketing pipeline. Launch coordinates testing, deployment, live site verification, marketing asset creation, and public announcement. One command to go from "code ready" to "product live and marketed."

Triggers

  • /rune launch — manual invocation
  • Called by team when delegating launch tasks

Calls (outbound)

  • test (L2): pre-deployment full test suite
  • audit (L2): pre-launch health check — full 7-phase quality gate
  • deploy (L2): push to target platform
  • incident (L2): if post-launch health check fails → triage and contain
  • retro (L2): post-launch retrospective — what went well, what didn't
  • browser-pilot (L3): verify live site screenshots and performance
  • marketing (L2): create launch assets (landing copy, social, SEO)
  • watchdog (L3): setup post-deploy monitoring
  • video-creator (L3): create launch/demo video content
  • L4 extension packs: domain-specific launch patterns when context matches (e.g., @rune/devops for infrastructure, @rune/ecommerce for storefront)

Called By (inbound)

  • User: /rune launch direct invocation
  • team (L1): when team delegates launch phase

Execution

Step 0 — Artifact Readiness Check

Before starting the pipeline, verify that prerequisite artifacts exist. Scan using Glob — do NOT hardcode paths, use discovery patterns.

REQUIRED ARTIFACTS:
  Source code:        Glob **/*.{ts,tsx,js,jsx,py,rs,go} — at least 1 match
  Build config:       Glob {package.json,Cargo.toml,pyproject.toml,go.mod} — at least 1 match
  Tests:              Glob **/*.{test,spec}.* OR **/test_*.* — at least 1 match

RECOMMENDED ARTIFACTS (warn if missing, don't block):
  Design system:      Glob .rune/design-system.md — if frontend project
  Deploy config:      Glob {vercel.json,netlify.toml,Dockerfile,fly.toml,.github/workflows/*} — any 1
  README:             Glob README.md
  Environment:        Glob .env.example OR .env.production — warn about secrets if .env found

BLOCKING CONDITIONS:
  ❌ No source code found → STOP: "Nothing to deploy"
  ❌ No build config found → STOP: "No project config detected — cannot determine build/deploy"
  ❌ No tests found → WARN: "No tests detected — pre-flight will run build-only verification"

Report artifact status before proceeding:

## Artifact Check
- Source: ✅ [N] files ([language])
- Build config: ✅ [file]
- Tests: ✅ [N] test files | ⚠️ No tests found
- Deploy config: ✅ [platform] | ⚠️ Not found (will detect in Phase 2)
- Design system: ✅ .rune/design-system.md | ⚠️ Not found (run /rune design first for UI projects)

Step 1 — Initialize TodoWrite

TodoWrite([
  { content: "PRE-FLIGHT: Run full test suite and verification", status: "pending", activeForm: "Running pre-flight checks" },
  { content: "DEPLOY: Detect platform and push to production", status: "pending", activeForm: "Deploying to production" },
  { content: "VERIFY LIVE: Check live URL and setup monitoring", status: "pending", activeForm: "Verifying live deployment" },
  { content: "MARKET: Generate landing copy and social assets", status: "pending", activeForm: "Generating marketing assets" },
  { content: "ANNOUNCE: Present all marketing assets to user", status: "pending", activeForm: "Preparing announcement" }
])

Phase 1 — PRE-FLIGHT

Mark todo[0] in_progress.

REQUIRED SUB-SKILL: rune:verification
→ Invoke `verification` with scope: "full".
→ verification runs: type check, lint, unit tests, integration tests, build.
→ Capture: passed count, failed count, coverage %, build output.

If any check fails: → STOP immediately → Report: "PRE-FLIGHT FAILED — deploy blocked" → List all failures with file + line references → Do NOT proceed to Phase 2

Mark todo[0] completed only when ALL checks pass.


Phase 2 — DEPLOY

Mark todo[1] in_progress.

2a. Detect deployment platform.

Bash: ls package.json
Read: package.json  (check "scripts" for deploy, build, start commands)

Platform detection (in order):
  1. Check package.json scripts for "vercel" → platform = Vercel
  2. Check package.json scripts for "netlify" → platform = Netlify
  3. Check for vercel.json or .vercel/ dir → platform = Vercel
  4. Check for netlify.toml → platform = Netlify
  5. Check for Dockerfile or fly.toml → platform = custom/fly.io
  6. Fallback: ask user for deploy command before continuing

2b. Execute deploy command.

Vercel:
  Bash: npx vercel --prod
  Capture: deployment URL from stdout

Netlify:
  Bash: npx netlify deploy --prod --dir=[build_output_dir]
  Capture: deployment URL from stdout

Custom (package.json script):
  Bash: npm run deploy
  Capture: deployment URL or status from stdout

Fly.io:
  Bash: flyctl deploy
  Capture: deployment URL from stdout
Error recovery:
  If deploy command exits non-zero:
    → Capture full stderr
    → Report: "DEPLOY FAILED: [error summary]"
    → Do NOT proceed to Phase 3
    → Present raw error to user for diagnosis

Mark todo[1] completed when deploy returns a live URL.


Phase 3 — VERIFY LIVE

Mark todo[2] in_progress.

3a. Verify live site.

REQUIRED SUB-SKILL: rune:browser-pilot
→ Invoke `browser-pilot` with the deployed URL.
→ browser-pilot checks: page loads (HTTP 200), no console errors, critical UI elements visible.
→ Capture: screenshot, status code, load time, any JS errors.
Error recovery:
  If browser-pilot returns non-200 or JS errors:
    → Report: "LIVE VERIFY FAILED: [details]"
    → Do NOT proceed to Phase 4
    → Present screenshot + error log to user

3a.5. Apply post-deploy health thresholds.

Use the metric-based rollback decision matrix defined in rune:deploy Step 4.5. Compare error rate, latency (p95), and availability against pre-deploy baseline for 15 minutes:

  • ALL metrics ADVANCE → proceed to Phase 4
  • ANY metric HOLD → extend monitoring to 30 minutes, alert user before proceeding
  • ANY metric ROLLBACK → stop pipeline, invoke rune:incident, do NOT proceed to marketing

This step bridges deploy's numeric thresholds into the launch pipeline — launch must not proceed to marketing while the deployment is unhealthy.

3b. Setup monitoring.

REQUIRED SUB-SKILL: rune:watchdog
→ Invoke `watchdog` with: url=[deployed URL], interval=5min, alert_on=[5xx, timeout].
→ watchdog configures health check endpoint monitoring.
→ Capture: monitoring confirmation + health endpoint path.

Mark todo[2] completed when live verification passes and monitoring is active.


Phase 4 — MARKET

Mark todo[3] in_progress.

4a. Generate marketing assets.

REQUIRED SUB-SKILL: rune:marketing
→ Invoke `marketing` with: project context, deployed URL, key features.
→ marketing generates:
    - Landing page hero copy (headline, subheadline, CTA)
    - Twitter/X announcement thread (3-5 tweets)
    - LinkedIn post
    - Product Hunt tagline + description
    - SEO meta tags (title, description, og:image alt)
→ Capture: all generated copy as structured output.

4b. Optional — launch video.

If user requested video content:
  REQUIRED SUB-SKILL: rune:video-creator
  → Invoke `video-creator` with: deployed URL, feature list, target platform.
  → Capture: video script + asset manifest.

Mark todo[3] completed when all requested assets are generated.


Phase 5 — ANNOUNCE

Mark todo[4] in_progress.

Present all assets to user in structured format. Do not auto-publish — user approves before posting.

Present:
  - Deployed URL (clickable)
  - Monitoring status
  - All marketing copy blocks (ready to copy-paste)
  - Video script (if generated)
  - Next steps checklist

Mark todo[4] completed.


Constraints

  1. MUST pass ALL tests before any deploy attempt — zero exceptions
  2. MUST pass sentinel security scan before deploy — no CRITICAL findings allowed
  3. MUST have rollback plan documented before deploying to production
  4. MUST NOT deploy and run marketing simultaneously — deploy first, verify, then market
  5. MUST verify deploy is live and healthy before triggering marketing skills

Mesh Gates

GateRequiresIf Missing
Test Gateverification output showing all greenRun rune:verification first
Security Gatesentinel output with no CRITICAL findingsRun rune:sentinel first
Deploy GateSuccessful deploy confirmation before marketingDeploy first

Output Format

## Launch Report
- **Status**: live | failed | partial
- **URL**: [deployed URL]
- **Tests**: [passed]/[total]

### Deployment
- Platform: [Vercel | Netlify | custom]
- Build: [success | failed]
- URL: [live URL]

### Monitoring
- Health endpoint: [path]
- Check interval: 5min
- Watchdog: active | failed

### Marketing Assets
- Hero copy: [ready | skipped]
- Twitter thread: [ready | skipped]
- LinkedIn post: [ready | skipped]
- Product Hunt: [ready | skipped]
- SEO meta: [ready | skipped]
- Launch video: [ready | skipped]

Returns

ArtifactFormatLocation
Deploy status + live URLInline (Launch Report)Emitted at session end
Marketing assets (copy, social, SEO)Markdown (inline)Generated by rune:marketing, presented in Phase 5
Release checklistMarkdown (inline)Shown in Announce phase
Monitoring confirmationInlineWatchdog setup output
Launch ReportMarkdown (inline)Emitted at end of session

Sharp Edges

Known failure modes for this skill. Check these before declaring done.

Failure ModeSeverityMitigation
Attempting deploy with failing tests or TypeScript errorsCRITICALHARD-GATE blocks this — pre-flight must be 100% green
Running marketing before deploy verified liveHIGHConstraint 4: deploy → verify HTTP 200 → THEN market. Never simultaneous
No rollback plan before production deployMEDIUMConstraint 3: document rollback strategy before running deploy command
Platform auto-detected incorrectly (wrong deploy command)MEDIUMVerify platform config files before running — ask if ambiguous
Marketing assets generated from assumptions rather than scout outputMEDIUMStep 1 requires scout to run — copy based on actual features, not assumptions

Done When

  • Pre-flight PASS: all tests, types, lint, build, and sentinel green
  • Deploy command succeeded with live URL captured
  • Live site returns HTTP 200 (curl or browser-pilot confirmed)
  • watchdog monitoring active on deployed URL
  • All requested marketing assets generated (or skipped with reason)
  • User presented with all assets before any publishing
  • Launch Report emitted with URL, monitoring status, and asset list

Cost Profile

~$0.08-0.15 per launch. Sonnet for coordination, delegates to haiku for scanning.

Scope guardrail: Do not publish marketing assets or trigger external announcements unless explicitly delegated by the parent agent.

Frequently asked questions

What does the Launch AI skill do?

Deploy + marketing orchestrator. Use when user says 'launch', 'ship to production', 'deploy and announce', or 'go live'. Runs the full pipeline — pre-flight tests, deployment, live verification, marketing asset creation, and announcement.

Why use Launch on TypingMind?

Because you install it once and use it with any model. Launch is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Launch in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Rune-kit/rune/tree/master/skills/launch. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Launch?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Launch?

As many as you like. As long as a model supports skills, you can use Launch with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Launch AI skill free?

Yes. It is published on GitHub by Rune-kit under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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