version: "1.0.3" name: analyze-video description: Internal sub-procedure (not a standalone entry point) — the shared footage-analysis pipeline that produces audio transcripts, contact sheets, and Sonnet-written summaries. Invoked end-to-end by the process-library and reprocess-with-contact-sheets skills; don't trigger it on its own.

Skill: Analyze Video (parent brief)

This is the main thread's playbook for the Analyze Video workflow step. Run it after library setup, before any cut work. It covers the three artifacts produced per clip: audio transcript, contact_sheet, and markdown summary. The roughcut agent reads dialogue on demand by running script_extractor.rb over the transcript JSON — no separate script artifact.

SKILL.md is the parent's dispatch brief. The sub-agent working prompt lives in agent_prompt.md — inline its contents when launching a Task agent. Don't pass SKILL.md.

Terminology

• User-facing: call it "footage analysis" or "analyzing footage."
• Internal/file names: "transcription" (library.yaml field transcript, etc.).

Prerequisites

• Library setup is complete (library.yaml exists, schema is current — run migrations from AGENTS.md if not).
• Read libraries/settings.yaml directly for whisper_model. For library fields, read the snapshot via ruby lib/buttercut/library.rb <name> summary and pull the values you need from the JSON — don't parse library.yaml inline.

At this point, create a todo list, visible to the user, with high-level, non-technical steps so they can follow the overall plan for processing the library. Include caffeination steps only if they've opted in. e.g.:

- [ ] Keep Mac Awake with Caffeinate 
- [ ] Create transcripts (0/30)
- [ ] Analyze footage (0/30)
- [ ] Turn off Mac Caffeinate 
- [ ] Review the footage together

These public todos map onto the steps below: "Keep Mac Awake with Caffeinate" is Step 1 and "Turn off Mac Caffeinate" is Step 6; "Create transcripts" covers the mechanical run in Step 2 plus the optional refinement pass in Step 3 (advance its count from Step 2's [transcript …] progress lines; hold it in-progress through Step 3 when refinement is on); "Analyze footage" tracks the summaries in Step 4 (advance its count as clips are summarized, not after contact sheets); "Review the footage together" is Step 5.

In the public chat, refer to these non-technical steps. Keep the technical work (WhisperX, contact-sheet generation, Sonnet summaries) behind the scenes.

Step 1 — Prevent sleep during processing

Before starting analysis, ask the user (via AskUserQuestion): "Processing can take a while — want me to keep your computer awake until it's done?" Options: "Yes (Recommended)" and "No".

If yes, start caffeinate in the background:

caffeinate -i -w $$ &
CAFFEINATE_PID=$!

This prevents idle sleep for the lifetime of the shell. Store the PID — you'll kill it in Step 6 once analysis is finished. (Backup is handled by the calling skill — process-library — after this skill returns.)

Step 2 — Process footage (transcripts, then contact sheets)

(If you reached this skill directly rather than via process-library, first tell the user: "Found [N] videos ([total size]). Starting footage analysis...")

This is two mechanical commands you run one after the other — transcripts first, contact sheets second. Never run them at once: WhisperX is RAM-hungry, and overlapping the two is what we're avoiding. Don't hand-roll either with sub-agents; each runs identically every time, ~2 clips at a time, recording each clip into library.yaml the moment it finishes. Both are idempotent — they only touch clips still missing that artifact — so a re-run finishes only what's left. They will run in a minature Sidekiq-like job service. You'll run the command and then watch the log file that will be returned to watch progress.

Step 2a — Transcripts. If using Claude Code, run with run_in_background: true on the Bash tool. Otherwise use correct tool/argument to run in background.

ruby lib/buttercut/process_footage.rb transcripts <library-name>

Immediately after launching, set up a Monitor or equivalent on the log file to receive a notification for each completed or failed clip:

tail -f tmp/logs/processing/<library-name>/$(date +%Y-%m-%d).log | grep --line-buffered -E "TranscribeJob  (done|error)|transcript: [0-9]+/[0-9]+|Error|error:|failed|Traceback"

ruby lib/buttercut/library.rb <name> pending transcript   # JSON list; done = N − its length

Update "Create transcripts 5/20" ie, number_of_clips_complete/total_number_of_clips as clips finish so the user can see progress. When the background task completes it exits non-zero if any clip failed. On failure, re-run once. If it fails again, investigate and then either create a fix so we handle it elegantlyy or inform the user about the problem with the clip and potentially pull it from the library.

Wait for 2a to finish before starting 2b.

Step 2b — Contact sheets. Once transcripts are done, build the sheets (a fast ffmpeg pass — foreground is fine):

ruby lib/buttercut/process_footage.rb contact-sheets <library-name>

Contact sheets stay behind the scenes — don't surface them as a public count. Mark "Create transcripts" done once 2a has finished and any refinement (Step 3) is complete.

Tuning (optional): both steps read parallel_jobs from libraries/settings.yaml (default 2); override for one run with --jobs N. Each writes a timestamped log under tmp/logs/processing/<library>/ for after-the-fact review.

Reprocessing. Each step skips clips that already have its artifact. To redo one — say a transcript that misheard a name — re-run that step with --force --clips NAME.mov. --force alone rebuilds every clip.

Step 3 — Refine transcripts (judgment — only if transcript_refinement: true)

Refinement is the one part of transcription that's a judgment call — fixing misheard proper nouns from library context — so it stays a model step, run after the mechanical pass. Skip this entire step if the library's `transcript_refinement` is `false`.

When it's true, dispatch refinement sub-agents (2-4 in parallel, rolling) over the transcripts Step 2a just wrote. Inline skills/transcribe-audio/refine_instructions.md as each sub-agent's prompt and pass, inline:

• transcript_path — absolute path to the clip's transcript JSON under libraries/<library>/transcripts/
• user_context and footage_summary — current values from ruby lib/buttercut/library.rb <name> summary (empty strings are fine; refinement still catches nonsense-token and self-witness fixes)

Sub-agents edit the transcript JSON in place and return a short list of corrections. They do NOT touch library.yaml — Step 2a already set the transcript field. Mark the public "Create transcripts" todo done once refinement completes.

Step 4 — Summaries (Sonnet sub-agents, batched, rolling)

Dispatch analyze-video sub-agents on the Sonnet model. Sonnet reads the contact sheet with noticeably more visual specificity than Haiku (catches clothing, architecture, camera framing) — worth it since the summaries feed every later cut decision.

Batch 10 clips per sub-agent, up to 10 sub-agents in parallel, with rolling dispatch. Each sub-agent processes its 10 clips sequentially; batching amortizes the ~5–10s per-agent dispatch overhead. For a 93-clip library that's ~10 sub-agents total instead of 93. Start the next sub-agent as soon as one returns — don't wait for the whole wave of 10 to finish, or you give up ~30% of wall-clock to whichever agent in the wave is slowest.

For each sub-agent, pass a list of 10 clip records inline. Each clip record needs:

• video_filename — basename of the video (used in the summary header and reply line)
• duration — duration string from library.yaml (e.g. 00:01:19); the agent renders it in the summary header
• contact_sheet_path — absolute path to the _full.jpg (from step 2)
• transcript_path — absolute path to the audio transcript JSON (from step 2); the sub-agent extracts dialogue on demand via script_extractor.rb
• summary_output_path — absolute path where the agent should write the summary markdown. Don't hand-build this filename; ask the library for the canonical path: ruby lib/buttercut/library.rb <name> field_path summary <clip> (handles the summaries/summary_<clip>.md convention for you, with or without the file extension)

As each sub-agent returns its batch, update library.yaml with summary for every clip in that batch:

ruby lib/buttercut/library.rb <name> complete summary <filename> [<filename>...]

The contact_sheet field was already populated in step 2, so the sub-agent return only contributes summaries.

If a sub-agent returns summaries inline instead of writing them to disk (sometimes Sonnet hallucinates "the Write tool is blocked" and dumps the markdown into its reply), don't retry blindly — just extract each summary from the agent's response and Write it to the matching summary_output_path from the parent thread. Then run the complete summary command as usual. Faster than redispatching, and the content is already there.

(Per-segment contact sheets generated for long clips live alongside the _full sheet on disk and are discoverable by convention — they aren't listed in library.yaml.)

Don't move forward until summaries are complete. Advance the "Analyze footage" count as clips are summarized; mark it done when finished.

Images. Still images skip Steps 2–3 entirely (no audio to transcribe, no contact sheet to build) — a summary is the only artifact they need, and they show up in ruby lib/buttercut/library.rb <name> pending summary right alongside videos. The image is the thing to look at, so summarize it directly: read the image file (it's small) from the main thread or a Sonnet sub-agent and write a 3–4 sentence description — subjects, setting, composition, any on-image text — to the canonical path from ruby lib/buttercut/library.rb <name> field_path summary <image-filename>. Note image clip keys include the extension (title-card.png → summaries/summary_title-card_png.md), which field_path handles for you. Then record it the same way: ruby lib/buttercut/library.rb <name> complete summary <image-filename>.

Step 5 — Confirm footage understanding with the user

(This is the "Review the footage together" todo.) Once every summary is written, talk through what the footage actually shows — confirm character names, locations, the narrative through-line, any stray or off-thesis clips, and the user's creative intent for this library. Use plain conversation; only reach for AskUserQuestion when offering a discrete choice. As you learn things, update:

• footage_summary and user_context via ruby lib/buttercut/library.rb <name> update_metadata footage_summary "..." (and the same with user_context)
• individual summary_*.md files when a summary mislabels someone or misses a key detail (e.g., "a man in a tan jacket" → the user's name)

This is the one place to do this thorough pass. Every later roughcut planning run inherits the resulting context rather than re-interrogating the library.

Step 6 — Stop caffeinate

If you started caffeinate in Step 1, kill it now:

kill $CAFFEINATE_PID 2>/dev/null

Parallel sub-agent pattern (reference)

Used in steps 3 and 4.

Parent agent responsibilities:

• Read library.yaml and settings.yaml once to gather all values needed by sub-agents.
• Launch Task agents passing all needed values inline in the prompt.
• Update library.yaml sequentially as agents complete (via the Library API — see AGENTS.md).
• Handle errors and retries.

Child agent responsibilities:

• Process its assigned clip(s) using only the inputs passed inline by the parent.
• Refine a transcript JSON in place (refinement) or read the pre-generated contact sheet, extract dialogue from the transcript via script_extractor.rb, and write the summary markdown in one Write call (analyze-video). (WhisperX is no longer a sub-agent — process_footage.rb transcripts runs it in Step 2a.)
• Return a short structured response with file paths.

Each skill's agent_prompt.md documents its own IO contract — including whether the sub-agent reads or writes library.yaml. (Spoiler: it never writes library.yaml. Only the parent writes, via the Library API.)

If the user requests a rough cut before analysis completes

Warn: "I can create a rough cut now, but I'll do a better job after analyzing all the footage. Continue anyway?" If the user confirms, proceed. Otherwise, wait for analysis to complete.