Launchclip cli
Create reviewable launch videos and OSS promotion packets from repositories, products, briefs, and media.
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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
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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-videoskill. Do not calllaunchclip produce. - Model-directed production: use
produceor 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
runor the manualinit→demo→plan→captions→render→validate→reviewstages. Keep provider handoffs in dry-run mode unless explicitly approved. - Talking-head motion workflow: use
script,align,motion-render, ordirectonly 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, andnextcosts.total_usd,costs.complete, per-provider totals, and warnings- stage-specific warnings and cached/generated counts
- verification and critic verdicts
production/qa/cinematic-readiness.jsonand 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/
- openai.yaml199 B
references/
- cli-reference.md17.3 KB
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.