Reproducible Launch Kit

    1

    Build launch assets from the repo through a reproducible pipeline: sourced evidence, generated storyboard, deterministic capture.

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    Reproducible Launch Kit

    Example session with this skill installed

    Generate a launch kit for version 2.4.0 based on the current repo. I have a 30-second budget. The last tag was v2.3.0. Use the bench/cold-start.json for metrics.

    • Read your context and instructions
    • Compiled the reproducible launch kit

    I have initialized the launch pipeline for v2.4.0. I extracted 12 new features from commits and a 14% improvement in cold-start latency from your benchmarks. The storyboard is constrained to 29.5 seconds across 5 scenes.

    Launch Kit Manifest

    ArtefactStatusPath
    Evidence LedgerGenerated (18 facts)artifacts/evidence.json
    StoryboardTimed (29.5s total)artifacts/storyboard.json
    Raw ClipsRecorded (Playwright)clips/*.webm
    Final VideoAssembled (1080p)dist/launch_v2.4.0.mp4
    Channel CopyReady (X, LinkedIn)dist/copy_drafts.txt

    Script Summary

    1. Scene 1 (0-5s): Cold start performance. Caption: "14% faster startup (240ms)."
    2. Scene 2 (5-15s): New Workspace Dashboard UI. Caption: "Unified view for all projects."
    3. Scene 3 (15-25s): Batch Processing API. Caption: "Process 1k records in < 2s."
    4. Scene 4 (25-29.5s): Outro & Version Tag. Caption: "v2.4.0 available now."

    Next steps

    • Review dist/copy_drafts.txt for the "what it does not do" disclaimer.
    • Run python scripts/claim_check.py evidence.json draft.md to validate your external blog post.
    • Inspect dist/launch_v2.4.0.mp4 to ensure the deterministic UI capture meets visual standards.

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    About this skill

    Regenerate the whole launch kit from the repo after any UI change.

    Turn a shipped project into launch assets - demo video, changelog and channel copy - through a reproducible pipeline where every scene is scripted, every capture is deterministic and every number traces back to evidence. Use when announcing a release, recording a product demo, preparing a launch, or when a demo video has to be re-recorded after a UI change.

    The pipeline is five stages with typed hand-offs: collect evidence from git, CI reports and benchmarks; gate the draft so no unsourced number survives; generate a timed storyboard under a duration budget; capture each scene with a frozen clock, a seeded random source and a pinned fixture; assemble with subtitles from the same storyboard. Any stage can be re-run alone, and two runs on the same commit produce the same assets.

    Includes an EXPERT.md reference (exact parameters, thresholds, failure diagnosis). Tested scripts: scripts/assemble.sh, scripts/claim_check.py, scripts/collect_evidence.py, scripts/record_demo.mjs, scripts/storyboard.py.

    It will not invent a benchmark, fake UI state, or write a claim you have not measured: thin evidence produces a short honest kit, not a padded one.

    How to use

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    Requires

    Python
    Git

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