agentsclimarketplace

Launchclip cli

Skill rogerchappel/launchclip/skills/launchclip-cli

Create reviewable launch videos and OSS promotion packets from repositories, products, briefs, and media.

Install
npx -y skills add rogerchappel/launchclip --skill launchclip-cli

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Operate, explain, troubleshoot, or automate the LaunchClip CLI for repository, product, topic, voiceover, presenter, downloaded HeyGen-avatar, cinematic one-shot, user-owned project style packs, and promotion-packet video workflows. Use when an agent needs to choose LaunchClip commands, prepare inputs, create or reuse a project-local style, run or resume the model-directed production pipeline, inspect costs and artifacts, validate a workspace, render after approval, or use the legacy dry-run packet lane. Distinguish API-backed CLI production from the separate subscription-agent workflow.

SKILL.md

9.5 KB, ~2.0k tokens by cl100k_base, as published. Nobody here has run it

LaunchClip CLI

Teach or operate the CLI from evidence on the current installation. Read references/cli-reference.md completely before constructing commands or diagnosing a run.

Route the request first

Choose one lane:

  • Subscription/OAuth agent, no model API spend: use the separate launchclip-create-video skill. Do not call launchclip produce.
  • Model-directed production: use produce or its resumable production stages. Planning and visual critique use OpenAI; frame authoring and bounded source-edit repairs may use OpenAI, OpenRouter, or local Ollama. This lane creates an editable HyperFrames project plus draft QA.
  • Local-first OSS promotion packet: use run or the manual initdemoplancaptionsrendervalidatereview stages. Keep provider handoffs in dry-run mode unless explicitly approved.
  • Talking-head motion workflow: use script, align, motion-render, or direct only when the user specifically wants that older presenter-driven lane.

If the desired lane is ambiguous, explain the cost and output difference and ask one concise question. Do not infer that a ChatGPT, Codex, or Claude login authorizes API billing.

For a request such as “cinematic,” “stunning,” “best one-shot,” or “maximize retention,” select the API-backed cinematic profile, not a product-launch specialization:

launchclip produce <source> --profile cinematic --out .launchclip/<slug>

The profile is source-agnostic and works for portrait or landscape output. It raises the craft floor; it cannot guarantee views, distribution, or audience fit outside the evidence available to the run.

Resolve the executable

Prefer the repository-local CLI when operating inside a LaunchClip checkout:

node ./bin/launchclip.js --help

Otherwise use an already installed launchclip. Do not silently install an npm package; ask before using npx to download one. Run --help from the executable you will actually use because flags may differ across versions.

Use the same executable consistently throughout a workspace. In examples, replace launchclip with node ./bin/launchclip.js when working from source.

Confirm inputs and side effects

Before a production run, determine:

  • primary source and source kind
  • output workspace
  • audience, CTA, aspect, duration, language, and creative prompt
  • supporting assets, references, supplied narration/transcript, and presenter footage
  • which provider credentials or local endpoints exist and which paid calls the user approves
  • whether the goal is a fast evaluation draft or a full-quality draft

Ask before:

  • running an arbitrary --demo-cmd
  • transmitting private source material to a provider
  • analyzing a third-party reference video
  • using a voice or likeness without confirmed authorization
  • starting metered production or external generation
  • final rendering, uploading, publishing, or posting

Never print or persist secrets. Report only whether a required environment variable is present.

Resolve user-owned styles

Look for reusable styles in .launchclip/styles/<name>. Verify a named pack with launchclip style show <name> before production, then pass the same name to --style. Use an explicit pack path when it lives elsewhere. An unmatched name remains free-form creative direction; do not silently replace it with a built-in channel preset.

Create a pack only from a completed video project or another pack:

launchclip style create ai-news --from .launchclip/approved-ai-video
launchclip produce ./next-brief.md --kind topic --style ai-news \
  --out .launchclip/next-ai-video

Style management is local and makes no model calls. Do not overwrite a pack unless the user explicitly authorizes --force. Keep .launchclip/styles/** in source control when the visual identity should be shared; leave generated video workspaces ignored.

Promote a downloaded HeyGen avatar

When the user supplies an already-generated, authorized HeyGen video, use the single local-file shortcut:

launchclip produce <source> \
  --heygen-avatar ./heygen-avatar.mp4 \
  --transcript ./heygen-avatar.txt \
  --out .launchclip/<slug>

The primary <source> remains the factual evidence source. The avatar video's audio becomes authoritative narration, and the same video becomes the beat-positioned presenter resource. Do not also pass --voiceover or --presenter; the CLI rejects that ambiguous combination. A transcript is optional only when the user has approved and configured the transcription provider call.

This flag consumes a local output file. It does not generate an avatar, call HeyGen, read HeyGen credentials, or authorize use of a likeness. Confirm that the video is local, authorized, and contains the intended final speech before starting model-directed production.

Run the smallest useful command

Prefer a single produce command for a new model-directed production and stage commands for a resume or repair. Reuse the same workspace so receipt-backed stages can cache valid artifacts.

Use --fast-eval to reduce provider, repair, and snapshot budgets, not to claim a free or cinematic-ready run. Do not combine it with a request for maximum one-shot quality. Use --no-audio to skip generated voice, music, and SFX, not to skip model planning, frame authoring, critique, or repairs. Treat --max-frame-cost-usd as a cumulative guard for direct-frame provider responses only, not as a total pipeline budget.

Use --model-policy cost-aware for standard production: Terra plans, Luna authors, and only failed frames escalate through Terra to Sol. Do not pass a model policy for cinematic production unless the user explicitly requests a different cost/quality tradeoff; --profile cinematic automatically selects the quality policy, and an explicit cost-aware/free/local-first policy weakens that default. Use local-first only when Ollama is running; it prepends the configured local coder model. Use explicit repeated --frame-route or --repair-route values when no unapproved cloud fallback is allowed. Ollama uses its native structured-output API with a deterministic 32K context; OLLAMA_CONTEXT_LENGTH can override that allocation. Repairs are bounded exact source edits, not complete frame rewrites. Repair passes receive four prioritized blocking findings per shot by default; lower --repair-issues-per-shot when the local model needs a smaller fix. Do not raise --max-patch-ratio merely to make a failed repair pass.

Do not pass --allow-frame-fallback, --allow-timing-drift, --skip-quality-gates, or similar escape hatches by default. Use one only when the user understands the quality or fidelity tradeoff and the resulting draft will be labeled accordingly.

Inspect every result

The CLI returns structured JSON. Check at minimum:

  • status, workspace, and next
  • costs.total_usd, costs.complete, per-provider totals, and warnings
  • stage-specific warnings and cached/generated counts
  • verification and critic verdicts
  • production/qa/cinematic-readiness.json and every gate when the profile is cinematic
  • the exact draft, snapshot, QA, or review paths

Do not claim success when the command produced needs-repair, incomplete cost classification, stale verification, a failed/missing cinematic readiness receipt, or an infrastructure failure. Fix toolchain failures before invoking model repair; provider repair cannot fix a missing browser, FFmpeg, or HyperFrames installation.

Respect final approval

produce stops at an analyzed draft. For cinematic work, require fresh native verification, a critic ship verdict, zero authored-frame fallbacks, passing motion/audio gates, and cinematic-readiness.json with ok: true. Only then run production-preview <workspace> to open the editable project in HyperFrames Studio and return its URL. Treat Studio as a review and editing surface: its Export action is an ad hoc HyperFrames render, not LaunchClip approval or the LaunchClip final artifact.

Pause for explicit human approval of the reviewed Studio state. Then run production-render <workspace> --approve --quality high; that command reruns the final checks before rendering. If Studio edits or an automated repair change the visible result, reopen or refresh Studio and obtain fresh approval.

After rendering, confirm the media exists, is non-empty, and has plausible duration and streams. LaunchClip does not grant permission to publish. Treat uploading, Review Feed submission, social queueing, and posting as separate external actions that require their own request.

Hand off operationally

Report:

  • invocation and version/help source used
  • lane selected and why
  • workspace and important artifacts
  • actual cost summary and any unpriced calls
  • verification performed and current verdict
  • approval or repair still required
  • exact safe next command

When blocked, include the command, exact error, failed stage, preserved workspace, and the least expensive resume command.

What ships with it: 2 files

17.5 KB alongside SKILL.md

agents/

references/

Gives 0 of the 12 instructions most automation workflows skills give in ~2.0k tokens

Counted across 745 of the 1,008 authors here whose files we hold, read 2026-08-07

  • Write conventional commit messagesin 36 of 745, across 35 files
  • Delete branches after mergein 30 of 745, across 21 files
  • Make atomic commitsin 25 of 745, across 15 files
  • Write minimal code to pass testsin 22 of 745, across 10 files
  • Re-snapshot after navigation or DOM changesin 21 of 745, across 13 files
  • Use try-catch for error handlingin 20 of 745, across 8 files
  • Run tests before committingin 20 of 745, across 12 files
  • Write tests before implementationin 20 of 745, across 8 files
  • Configure branch protection rulesin 19 of 745, across 5 files
  • Explain the why in commit messagesin 19 of 745, across 9 files
  • Refactor code while tests remain greenin 19 of 745, across 6 files
  • Interact with elements using refsin 19 of 745, across 11 files

Said here and by no other author read

  • read the CLI reference before constructing commands
  • ask one concise question if the lane is ambiguous
  • run help from the executable you will actually use
  • ask before starting metered production or external generation
  • never print or persist secrets
  • verify named style packs before production

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.