Imported from mupozg823/timecode-agent (skill/SKILL.md). Install upstream with npx skills add mupozg823/timecode-agent --skill skill. Copyright stays with the author (MIT).

TIMECODE-AGENT

Hypothesize from the transcript first, verify only the uncertain moments visually, then persist the result as checkpoints. Never capture every frame; state the hypothesis each capture tests. Never promote a device- or dataset-local number into a universal rule.

References, read only when needed

Request to loop routing

Fold the user's words into a loop first. Step numbers come after.

Pipeline: ingest (develop), search (recall), Kubrick (understand), Kuleshov (edit), cut (assemble), archive (preserve).

Kubrick writes the fact ledger (checkpoints.jsonl); Kuleshov writes the edit decision ledger (sequences.jsonl). Kuleshov never modifies the fact ledger. Correct a factual error in the checkpoint, then derive the edit again.

Requirements

• Python 3.12 on macOS or Linux; Windows is experimental — see Runtime setup.
• Full mode needs a local image-input tool and an image-capable model; missing either, run the Degraded mode below.

command -v va
command -v ffmpeg
command -v ffprobe

Only for HTTP(S) URL input:

command -v yt-dlp

Every feature starts on. Read Runtime setup for install, backends, profiles, URL cookies, and cache paths. A backend import failure is not a missing optional feature — repair it, do not degrade.

Loop steps

0. ingest — audio analysis to timestamped transcript plus signal batch

va ingest "<video-path>" --model small --signals
va ingest "<URL>" --signals -o "<absolute-workspace>"

Local default workspace: ./va-out/<stem> under the CWD; URL default: ./va-out/url-<md5-prefix>. Always pass an absolute -o for URLs so the resume path stays knowable. If manifest.json exists, resume with va brief <ws>; va ingest enforces this and also rejects manifest-less directories containing durable evidence. For URLs prefer the fastest source: manual subtitles > uploader auto-captions (original language) > whisper. For timestamp precision, choose --force-whisper on the first ingest or a new workspace (different -o). A new source or transcript generation gets a fresh workspace so old evidence cannot rebind.

0-1. Content mode — fix the signal weighting (dogfooding lesson)

The va brief recommendation is a placement signal. Confirm the mode against transcript, highlights, and scenes. A low transcript_coverage can mean a dead transcript rather than a quiet video — check before asserting, and read Verification toolbox for the response.

For broadcast footage, check overlays, nameplates, and mic flags in the first full-res capture. Ingest and overview exceptions are in Verification toolbox.

1. First-pass inference — transcript only (zero frames)

If the brief has chapters:, use those boundaries as draft spans and re-split only chapters that contradict the transcript. If speakers matter, run va diarize <ws> before any checkpoint; it advances the transcript revision and refuses after evidence exists. Then split at meaning shifts and record.

va checkpoint add <ws> --json-file - <<'EOF'
{"id": "cp-001", "span": [0.0, 42.5], "segments": [0,1,2,3],
 "status": "hypothesized",
 "hypothesis": "MC가 퀴즈 규칙을 설명하는 오프닝. 화자 1명 추정",
 "confidence": 0.55}
EOF

Short entries also go in as flags — va checkpoint add <ws> --id cp-001 --span 0 42.5 --status hypothesized --hypothesis "..." (--span reads mm:ss; --status enforces a value list). Use the heredoc when the body is long or non-ASCII.

• ids run upward from cp-001; spans are in seconds.
• Cover every span so va status <ws> reports no gap.
• Write the hypothesis as what changed since the previous span, not a static list.

2. Pick verification points — where to use your eyes

Look only where one of these holds: confidence < 0.7, ASR conf < 0.6, a speaker or location change, 20s or more of silence or gap, an ambiguous referent, a change in who is on screen, a burst or scene change. Diarization must already be complete; do not mutate the transcript at this stage.

Narrow a long unknown stretch with an overview scan before individual captures.

va filmstrip <ws> --auto
va filmstrip <ws> --auto --start 120 --end 400

Open tiles by absolute path; pick only what deserves full-res. Confirm speaker cues and on-screen text at full-res.

• Endcard duty: after the overview, view the one tail frame that --legible-endcard picked.
• Time-reference span protocol: read the whole transcript of that span, a dense 1-2s overview, and the frames on both sides of the boundary.
• Spatial direction dual-reading rule: weigh both the camera-relative and the subject-relative reading. Without evidence, default to camera (viewer) reference and lower the confidence.
• Fine detail reading: compare 3-5 consecutive frames instead of a single image.
• Capture budget by mode: speech-driven <=15 frames, visual-driven and silent <=25 frames, and <=6 captures per round. Over budget, use va keyframes <ws> --budget N.
• Reach for va ocr on UI text and va faces on the on-screen cast first.

Density (cell spacing no more than 7s, one tile 112s or less), the duration-1 fallback hazard, OCR crops, and the sharpness gate follow Verification toolbox.

2-1. Signal-based verification points (two placement signals)

va highlights <ws> --json      # audio energy burst spans (highlights.json)
va scenes <ws> --json          # scene-change timestamps, no frame extraction (scenes.json)
va audioevents <ws> --json     # learned laughter/applause/cheer/scream candidates — macOS Sound Analysis

Signals fix placement (when) only; selection (what) is the agent's judgment. For silent video prefer scenes; for the editorial meaning of a burst prefer audioevents. When all three are quiet, spend fewer captures. Read Verification toolbox to separate real scene cuts from false positives.

3. capture, vision double-check, checkpoint update

va capture <ws> -t 18.2 -t 95.0 --reason "burst-95s"   # 1-2 frames per span under test
va checkpoint observe <ws> --id <id> --frame <frames/...jpg> --subject "<person>" \
  --state present|absent|uncertain --hypothesis "<what you saw>"

Open absolute frame paths before observe; it derives timestamp and evidence from provenance. Keep unresolved presence uncertain. For other claims, a match is verified, a miss corrected, and a reused id creates a revision. Evidence paths are relative to <ws>; see Verification toolbox.

Write human-facing fields in the user's language. hypothesis, situation, note, and --reason appear verbatim in the vault. Say what you saw, never tool names or internal metrics (full-res, span, score, OCR, burst).

4. Convergence — self-answer then reflect

Answer from the current index first and name evidence gaps that could change the result. Never stop on model confidence. Finish when core claims have resolvable support, no unnamed audit warning or contradiction remains, and another observation is unlikely to change the answer.

va status <ws> --json    # covered_ratio == 1.0, no gap, verified_ratio >= 0.6 as the secondary bar

readiness is a secondary stop signal. Read audit warnings with va audit <roots>: keep going while warnings or evidence gaps remain even at converged. If the budget runs out first, do not force a promotion — state the provisional or unresolved scope.

Edit-scope convergence: only for edit requests with a named span or event. Enter once the whole cut span is covered by terminal plus support (the gate enforces it), even without global convergence. Open-ended highlight discovery needs global convergence over the candidate comparison range first. Details in Output handoff.

5. Output — understanding, editing, processing

For seated-man departure counts, run va ask <ws> "<question>" --format agent-json --lang <user-tag>. Keep codes internal, answer in the user's language, and surface uncertain/next_ms; count:0 never means "none". Read-only, evidence computed once, no LLM, not generic VQA.

• Cite checkpoint and transcript timestamps in answers. The first route per question type follows Wiki schema.
• For highlight and shorts requests, verify the candidates, then offer 2-3 options with different intent. Scoring, boundaries, and reframe are in Output handoff.
• Clip: va clip <ws> --start 1:23 --end 2:05 --accurate. The low-power profile or an explicit clip-encoder setting uses VideoToolbox plus AudioToolbox on macOS. Keep software-precise encoding for balanced and final delivery — deliver without --hw; --hw is for previews and low-power work.
• Cut boundaries: run va boundary-eval <ws> --sequence <id>, then open the frames on both sides of the boundary and re-snap to a word or segment boundary if it is clipped. Cutting to music: va beats extracts the grid, joins land on beats, va beat-eval gates p90 <= 40ms — see Output handoff.
• Handoff: va export <ws> --format xml|otio|fcpxml|srt|edl|md. For CapCut use srt only; manipulating the unofficial draft JSON is forbidden. In --ids cp-004,cp-007 the listed order is the cut order. Revision-bound NLE handoff needs complete decoded-CFR proof — VFR/unknown/irregular sources reject it; use a decoded-CFR delivery source or text handoff.
• Record a reusable edit plan with va sequence add <ws> --json-file - and hand it off with va export <ws> --format otio --sequence seq-001. An edit plan never modifies the fact ledger.

Batch and corpus rules

• Find with va search "<query>" and open the hit workspace with va brief.
• At session end run va index && va wiki; check space with va gc in report mode.
• Corrections, glossary, active wiki, and deletion scope: read Corpus lifecycle.
• At 3 or more videos, parallelize ingest and signals only, per Batch contract. Hypothesis, verification, and judgment stay sequential in the main agent.
• va skillgen --route names which skill each corpus advances and the gate that still blocks it.

Harness branching (per runtime)

The full loop needs both a tool that opens local images as model input and an image-capable model. When unsure, start degraded and promote after the first successful read. Never hardcode a tool name; use the current harness equivalent (Claude Code: Read, Codex: view_image). For any other harness read Harness matrix.

Degraded mode (no vision): use only OCR, faces, audioevents, highlights, scenes, and diarize results, and hold hypothesized plus confidence <= 0.7 except for hard OCR evidence. Separate what someone said from what the video shows; when speech is the only evidence, write it as a speaker's claim.

Cautions

• JSON bodies mix quotes and non-ASCII text — default to --json-file - with a heredoc.
• The capture cache dedups same-timestamp frames; vision cost is separate.
• Label people only from on-screen visual cues (captions, badges, position), and never guess a real name.