Intake: say what you want, drop the materials (`python -m vstudio.intake`)

这一页还没有中文版，下面是英文原文。

The Reelfold desktop app’s “new batch” form asked for a recipe, a segments file and budgets. Intake replaces it: the creator describes the job in plain language and drops any mix of files and folders (video, audio, photos, pdf / docx / pptx / md / srt). Intake inventories the materials, picks one or more recipes (mixed plans are fine), shows an editable plan, and creates the projects (vstudio.project, references/PROJECTS.md) ready for a pilot run.

materials + "把后面对于自媒体的思考单独剪出来发小红书"
  -> analyze (inventory, cached)  -> plan (model or rules; validated)  -> [revise "每条 60 秒内"]*  -> apply -> projects (+ series)

Code: lib/vstudio/intake/ (inventory, docs, rules, plan, estimate, apply, cli). Plan schema: lib/vstudio/intake/plan.schema.json (python -m vstudio.intake schema --json).

1. Commands (run from `lib/` or with `PYTHONPATH=$VSTUDIO/lib`; every command takes `--json`)

command	what
`analyze --inputs PATH... [--asr off\|sample\|full] [--out analysis.json] [--full]`	the material summary (compact: no paths, short excerpts) + content hashes; `--out` keeps the full analysis
`plan --prompt "..." --inputs PATH... [--client C] [--provider P] [--asr auto] [--auto hook,cover] [--out plan.json]`	the plan (section 3); `--analysis analysis.json` instead of `--inputs`
`revise --plan plan.json --prompt "只要小红书" [--in-place \| --out new.json]`	the plan updated from a follow-up instruction; the history is kept in `revisions`
`apply --plan plan.json [--out DIR] [--dry-run] [--run]`	creates the project folders (a series when the plan has several), returns ids, dirs and the pilot run commands; `--run` starts each pilot; exit 5 on an invalid plan
`schema`	the plan JSON Schema

2. Inventory (`analyze`)

Folders are walked recursively (hidden files, VCS / dependency / state / exports folders and symlinks skipped; 2000 files, depth 8). Inputs are read-only: nothing is written next to them (ASR samples are decoded to a temp folder; the cache lives under $VSTUDIO_CACHE/intake/, keyed by a content hash + the analyzer version, so a second analyze / plan / revise is instant).

kind	facts
video	duration, size, orientation, fps, HDR, audio; speech from a short ASR sample (one 20 s window, three for files over 75 s; `--asr full` = whole track, transcript cached); on 6 frames: faces per frame (`vstudio.face`), talking head, multi-person, screen share (low saturation + flat UI), burned-in captions (bright text with a dark outline concentrated in the lower / middle band, absent elsewhere); filename hints (zoom / meet / recording / 录屏 / final / 成片 / 课 / 培训). The first 40 videos get the heavy checks, the rest probe facts only
audio	duration, speech vs music
image	size, orientation, EXIF capture time, coarse GPS (1 decimal, about 10 km), faces on up to 12 images; a folder of photos / clips becomes one group (count, time span, faces share)
text	pdf (pypdf or PyMuPDF), docx (python-docx), pptx (python-pptx), md / txt / srt / vtt / ass / json: title, up to 12 headings, a 300-char excerpt, language, shape (`script` 剧本 with scenes + dialogue, `slides`, `outline`, `notes`, `article`, `subtitles`), episode count, URLs found (listed, never fetched). A missing extractor is a note, not an error

Every material also gets a role (what the rule planner keys on): call, lecture, screen-recording, talking-head, finished-edit (burned captions or a final export with speech), footage, podcast-audio, voice, music, photo, script, slides, doc, notes, subtitles.

3. Plan

The routed model (task intake of vstudio.llm: llm.tasks.intake or llm.default in the persona / client; e.g. claude-code = the creator’s own Claude Code login; --provider overrides) gets the request, the compact materials, the persona / client defaults, the recipe catalog (every manifest: inputs, item sources, params with enums / defaults, platforms, checkpoints), the SKILL.md phrase table and, when the request selects content from a recording (“剪出来”, “截取”, “有意思的部分”, “单独发”), the time-coded transcript of that recording (--asr auto: transcribed once, cached, for videos up to 45 min). The code then validates and completes the answer:

recipe ids, input keys, input kinds / extensions, param names and values (each param’s JSON Schema from the manifest), item keys: anything unknown or invalid is dropped or reset to the default and listed in warnings; a sub-project with an invented recipe or without its required input is dropped. No recipe is ever invented.
params: recipe default < persona / client (platforms, body speed, cleanup_profile, language) < materials (a finished edit with burned captions -> talkinghead captions: false, gentle cleanup, 1.0x; a screen share -> longform layout_mode: split) < the model < what the request literally says (platforms, counts, “每条 60 秒内”, “1.2 倍”, 英文, 剪干净, 遮脸 / 不遮脸, 卡点). param_sources says where each value came from.
platforms are mapped to the recipe’s ids (小红书 -> xiaohongshu:full for talkinghead, xiaohongshu:vertical for longform slices).
focus rows: ranges are clamped to the media; a focus sub-project without rows gets them from the rule matcher (focus terms + position words 前面 / 中间 / 后面 over the transcript); if no transcript exists yet, apply transcribes and resolves it.
checkpoints from the manifest (needs_you: auto: never ones, budget / consent always, the rest unless listed in --auto), estimates (section 4), a template Chinese summary when the model gave none.

When no model is routed, the call fails (expired login, network) or nothing survives validation, the rule planner builds the plan: the phrase table per clause (另外 / 以及 / ； split the request), material roles (a call -> call-clips, a lecture -> longform-to-short, photos + clips -> photo-story or vlog, a 剧本 -> ai-video, a PDF -> explainer topics from its headings, notes -> preproduction), negations (“不要讲解视频”) and the same parameter extraction. planner.fallback: true + planner.reason say so.

{
  "version": 1, "kind": "vstudio.intake.plan", "id": "20261006-124604-x", "prompt": "...", "client": null,
  "planner": {"provider": "codex", "model": "...", "route": "argument", "fallback": false, "cost_usd": 0, "seconds": 26},
  "analysis": {"digest": "...", "inputs": ["/abs/..."], "totals": {...}, "asr": "sample"},
  "materials": [{"id": "f1", "path": "/abs/x.mp4", "kind": "video", "role": "finished-edit", "duration": 653.2,
                 "burned_captions": true, "transcript": "<cache>/....transcript.json"}],
  "projects": [{
    "id": "p1", "recipe": "talkinghead", "recipe_label": "口播精剪", "name": "...", "why": "...", "materials": ["f1"],
    "inputs": {"video": ["/abs/x.mp4"]},                       // project-level inputs (abs paths or text)
    "items": {"method": "focus",                                // per-file | single | planner | focus | episodes | list
              "count": 3, "focus": "后面关于自媒体的思考；中间的感悟",
              "rows": [{"id": "s01", "inputs": {"video": "/abs/x.mp4"}, "params": {"range": [535.0, 610.0], "title": "..."},
                        "why": "..."}]},
    "params": {"platforms": ["xiaohongshu:full"], "captions": false, "speed": 1.0, "cleanup_profile": "gentle"},
    "param_sources": {"platforms": "prompt", "captions": "material", "speed": "planner"},
    "checkpoints": [{"id": "filler", "kind": "filler-confirm", "label": "确认去 filler", "needs_you": true, "auto": "default"}],
    "estimate": {"machine_min": 3.7, "wall_min": 1.8, "storage_mb": 383, "api_usd": 0.0, "credits": null,
                 "measured": true, "paid_steps": [], "basis": {"source_s": 653, "output_s": 222, "items": 3, "platforms": 1}},
    "outputs": {"platforms": ["xiaohongshu:full"], "videos": 3, "workflow_md": "workflows/talkinghead/WORKFLOW.md"}
  }],
  "series": null,                                               // {id, name} when there are several sub-projects
  "questions": [{"id": "q1", "project": "p1", "text": "...", "options": [...], "default": "..."}],
  "risks": ["..."], "warnings": ["..."],
  "estimate": {"machine_min": ..., "wall_min": ..., "api_usd": ..., "paid_steps": [...]},
  "run": {"pilot": 1, "auto": []},
  "summary_zh": "一段中文说明：做什么、用什么、关键设置、需要你确认什么、时间 / 费用",
  "revisions": [{"prompt": "每条 60 秒内", "at": "...", "planner": {...}, "changes": [...]}]
}

The plan is plain JSON: the app (or the creator) may edit params, rows, ranges, names or drop a sub-project before apply; apply validates it again.

Questions are only for what can’t be defaulted and changes the result (whose face to hide, narration vs music); everything a checkpoint already covers (segment approval, filler cuts, cover, publish review, budget) is not asked up front.

4. Estimates

Speech recipes: the vstudio.batch bench table (this machine’s measured seconds per unit when a batch has run here - measured: true - else the built-in guesses) over the stages probe / extract / asr / (geometry) / cleanup / apply / compose / export x platforms / verify / qc / preview, with source seconds and planned output seconds (rows’ ranges, planner count x the mean of min_s / max_s, or the files’ durations). Render recipes: seconds per output second (explainer 6, promo 4, photo-story 2, vlog 1.5). API cost: 0 when the LLM tasks route to a subscription CLI, a local server or none; else about $0.01 per source minute; OpenAI TTS about $0.015 per narrated minute; AIGC credits are quoted at the ai-video budget checkpoint (never auto). Wall time assumes two lanes.

5. Revise

revise sends the current plan + the instruction to the model (“keep what the instruction does not change”) and validates the result like a new plan. Without a model, rules handle the common follow-ups: 只要 / 只发 <平台> (replace) or a platform alone (add), 每条 N 秒内 (max_s; rows longer than that get a note), N 条, 不要 <配方词> (drop those sub-projects), 1.2 倍 / 原速, 英文, 剪干净 / 轻一点, 遮脸 / 不遮脸, 加 / 不要 hook, 横屏 / 竖屏, 卡点 / 舒缓. An instruction nothing understood leaves the plan unchanged with a warning.

6. Apply

One sub-project -> <out>/<NN>-<name>/ via Project.create (recipe, params, client, auto policy; rows become items with their range / inputs; per-file -> one item per file; episodes -> N episodes; planner recipes without rows keep their segment planner, run at the first run and approved at the segments checkpoint). Several sub-projects share a series (intake-...; the first recipe is the series recipe, each project names its own) so the desk shows them as one job. <out>/intake.json = the plan as applied + the project dirs. Default <out>: $VSTUDIO_HOME/projects/<plan id>. Next step per project: python -m vstudio.project run --dir D --pilot 1 --json-events (exit 4 = the pilot waits for review, 7 = a checkpoint needs the creator).

7. Validated

Golden tests (tests/test_intake.py, mocked model + rule planner, no media work): 12 request x material mixes - lesson -> 20 vertical slices, 口播 -> cleaned 小红书 short, finished horizontal edit with burned captions -> focus ranges in its later part, photos + clips -> 文艺片, PDF -> 5 explainer shorts, 剧本 -> 6 AI episodes, Zoom podcast -> clips with face masks + consent, a mixed course folder -> longform + explainer + 口播稿 (series), DJI clips -> 卡点 vlog for 抖音, a finished export -> polish, screen recordings -> course for B站, notes -> 3 scripts; plus revise (rules and model), apply (projects, series, ranged items), the inventory on synthetic media / documents, the CLI. A real run (a 10.9 min finished horizontal 口播 with burned captions, “把后面对于自媒体的思考，以及中间的一些感悟剪出来，单独发小红书”, provider codex): role finished-edit detected (talking head, burned captions, final export), whole track transcribed for the focus, plan = talkinghead x 3 focus rows (301-363 s and 450-535 s: 副业 / 跨行业感悟; 535-610 s: 自媒体), captions kept (captions: false), gentle cleanup, 1.0x, xiaohongshu:full, about 2 min wall, $0 (subscription CLI), in about 70 s end to end on first analysis.