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gug007gug007/lpm/.claude/skills/lesson/SKILL.md

lesson

Skill150 starsChanged 7 days agoMITView on GitHub

Make a narrated lesson video about lpm from the real desktop app, recorded on a pristine data directory. Title and optional narration text in, MP4 with OpenAI voice out, audio in sync with the clicks. Use when the user asks for a video lesson, tutorial, screencast, or YouTube video about lpm.

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The file

We split this skill into the rules it gives an agent

4 rules. 0 are common, 1 is distinctive, and 3 are very specific to this project.

  • 0

    Common

    Most of the 15 edited versions we read keep these.

  • 1

    Distinctive

    This author's own choices. Worth a look.

  • 3

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    Read the take's log for warning lines and the qa summary: a FAIL (a name, email or error text on screen, an agent that never answered) …

    line 18
  2. Distinctive:

    no YAML, config files or CLI unless the title is about them.

    line 22
  3. Very specific:

    never verify a pipeline change with a real take.

    line 26
  4. Very specific:

    use the OpenAI key in the Keychain (security add-generic-password -s lpm-video -a openai -U -w).

    line 27

“Common” is measured: kept by at least 6 in 10 of the 15 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Generate a video tutorial from a title and optional narration text.

Workflow

  1. node scripts/new.js "<title>" [--projects storefront,client-portal] starts ~/Movies/lpm-lessons/<slug>/ with lesson.json, beats.js and take.sh. Write the narration and the beats: reference/beats-api.md has every call a beat can make, the selectors and the traps.
  2. Tell the user the narration (with the cue words each click lands on), a one-line-per-beat plan, and anything the take touches in their real setup (agent settings, a real git clone, real usage meters), and go straight on to the dry run without waiting for an OK.
  3. When the preflight notes the debug app is older than the Rust sources, run cargo build in desktop/frontend/src-tauri first: a Tauri command the frontend calls but the old binary lacks fails mid-take. <lesson>/take.sh --no-audio is a dry run: it lints the lesson, launches the app and drives the real pointer with estimated timing, and writes frames/NN-<beat>.jpg. Review the frames (a contact sheet helps: ffmpeg -i 'frames/%*.jpg' … tile). Then write the opening and the cover: coldOpen (a one- or two-sentence hook over two short shots of the payoff) and cover (2–4 words and a frame of the payoff) in lesson.json; a dry run's timing is estimated, so check both on the take's qa/sheet.jpg and adjust with a --mux-only re-cut. Without them a lesson opens on its title card and uses that card as its thumbnail.
  4. <lesson>/take.sh is the take: voice first, then the recording, then the MP4, captions, chapters, thumbnail and the checks (qa/report.json, qa/sheet.jpg). Run it in the foreground (a take started in the background fails for lack of a first frame), about 5 minutes. Nobody touches the mouse or keyboard meanwhile.
  5. Read the take's log for warning lines and the qa summary: a FAIL (a name, email or error text on screen, an agent that never answered) means the take is not publishable as it is.
  6. End the reply with the MP4's full absolute path (~/Movies/lpm-lessons/<slug>/<slug>.mp4).

Rules

  • no YAML, config files or CLI unless the title is about them; each video stands alone (no "next lesson" or teaser line; the lint refuses one); lessons open on s.card("<title>") and close on a silent { "id": "outro", "ms": 3200 } line whose beat is s.card("lpm.cx", { hold: true }).
  • narration: name the control, then click it on that word (cue); fold a very short closing sentence ("Click it.") into the one before, since the voice drops those; write lpm lowercase (it is spoken as letters).
  • the recording shows the real machine: the checks read every second of the take for the account name, full name, computer name, email and home path, and for error text. Keep them off screen (the kit's neutralShell replaces the shell prompt).
  • a take drives the real Claude Code / Codex / Cursor on the user's login. take.sh saves their model, effort and status-line settings and puts them back afterwards, even after a crash (restored at the start of the next take). Nothing else in their config is guarded, so a lesson that changes something else must put it back itself.
  • never verify a pipeline change with a real take. Copy the lesson folder (cp -c -R) to a scratch directory and run node scripts/make.js <copy path> --mux-only there; a path is used as it is, so the published lesson is never touched. node --test scripts/test/*.test.js covers the pure parts.
  • use the OpenAI key in the Keychain (security add-generic-password -s lpm-video -a openai -U -w).

Re-cuts (--mux-only): re-render the current take with the current look, sound, zooms and captions; nothing is recorded and nothing is spoken. A reworded line needs --speak: it speaks the changed lines and keeps them only if every new clip still fits before the next line; otherwise all clips are put back as they were and the line needs a new take. --take <n> re-cuts an archived take from _takes/ with the script it was recorded with (it fails when those lines have been spoken again since). The render a re-cut replaces is kept in its take's cuts/. Adjust a zoom without a new take through lesson.json "zooms": { "<line>#<n>": { "scale": 1.6, "at": [0.3, 0.5], "ms": 900 } } or { "drop": true } (the mux log lists the ids).

Flags: node scripts/make.js prints them. The pipeline, the look, the sound, the data directory, the take folders and the checks are described in reference/pipeline.md.

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