researchowl/docs/shortsmith-phases-4-6.md

# Phases 4–6 — more elaborate Shorts

**Roadmap document.** Phases 1–3 are live: spec generation with grounding (phase 2),
render via shortsmith, YouTube upload as private with a human publish click (phase 3).
Today shortsmith renders **silent** vector motion graphics from 8 templates
(`radar_sweep`, `track_map`, `data_card`, `scale_bars`, `orbit_track`, `signal_strips`,
`document_quote`, `counter_close`).

- **Repos touched:** mostly `git.chemavx.xyz/chemavx/shortsmith`. researchowl changes are
  small and called out explicitly per phase — the live-contract design (`GET /templates`
  injected into the prompt at generation time) means new templates and new spec fields
  reach the generator with little or no code here.
- **Contract rule for every phase:** additive and optional. An existing valid spec must
  stay valid; a pod running old researchowl must keep producing renderable specs. No
  breaking field renames, ever.

---

## 0. The gate — data before work

Do not start phase 4 until 3–4 real Shorts are published and have a week of YouTube
Analytics. The decision is not aesthetic, it is a retention curve:

| Signal in Analytics | Diagnosis | Order |
|---|---|---|
| Viewers swipe in the first 1–2 s | Silent open kills the hook | Phase 4 first |
| Retention holds, then decays evenly | Format works, ceiling is production value | Phase 5 first |
| Retention fine, views low | Distribution problem, not video problem | Neither — titles/tags/posting time |

Four videos of data beat any amount of a priori taste. The phases below are ordered by
the expected outcome (audio first), but the gate can reorder them.

---

## Phase 4 — sound

> **Corrected after reading the shortsmith source** (the first draft of this section
> assumed silent renders and proposed royalty-free tracks on disk — both wrong).
> shortsmith has synthesized audio by design: numpy only, no samples, no licensing
> exposure, deterministic to the sample, with the spec's `audio: {preset, silence}`
> block and the validated `sonar` composition guarded by a reference gate. The right
> 4a for that architecture is a wider palette of synthesized presets, not files.

### 4a. The preset palette — **shipped 2026-08-06**

- shortsmith: `pulse` (sub-bass heartbeat tightening past the midpoint — tension, for
  debunks) and `static` (shortwave noise bed, seeded crackles, faint drone — document
  drops) joined `sonar` and `none`. Same arc grammar (nothing starts inside a `silence`
  window, closing swell, same limiter chain); `static`'s noise comes from seeded legacy
  `RandomState` streams, which NEP 19 froze — deterministic on any numpy. `GET /audio`
  publishes the palette with one-line mood notes.
- researchowl: the client fetches the palette (404 → the baseline pair, fallback
  convention), `validate_spec` takes it as the live source of truth for
  `audio.preset`, and the prompt offers it with the mood notes so the model matches
  preset to narrative shape. The edit loop accepts it too: changing `audio.preset` in
  the `/short_spec` file and re-sending is the free way to audition the palette.

### Deferred from 4a: shot-boundary transition accents

A synthesized whoosh at each cut. Deliberately not done yet: `audio.py`'s presets are
fixed compositions that know nothing about the shots, and that one-way line is worth
keeping until the palette itself proves its value. If it happens, it is an opt-in
`Audio.transitions` flag with boundaries passed alongside `silence` — additive, and
`sonar`'s reference gate must not notice.

### 4b. Narration (TTS) + burned-in captions

These two ship together: most Shorts are watched muted, so the captions matter more than
the voice — but both come from the same new field.

- Spec: each shot gains an optional `narration` string. **When present, the shot's
  duration is derived from the synthesized audio length plus padding** (clamped to the
  template's min/max) — voice-first timing, not text-squeezed-into-a-window. `duration`
  stays valid for shots without narration.
- shortsmith: TTS engine local and free — Piper or Kokoro, CPU-realtime, **model version
  pinned in the image** so renders stay reproducible. One voice, always the same: the
  voice is channel identity, not a per-video choice. Captions burned from the narration
  text, word-grouped, styled like the existing typography.
- Fallback (repo convention): if TTS fails, render with music only and report it —
  degrade, don't die. The job must not fail because a phoneme did.
- researchowl — two small, real changes:
  1. **Grounding must cover `narration`.** It is exactly the field an LLM fills with
     confident paraphrase. `grounding.py` extracts from spec props today; add the
     narration strings to the extraction. Same deterministic path, no LLM.
  2. Prompt: narration guidance (spoken register, ≤ ~25 words per shot, hook in the
     first line — the first two seconds decide the swipe).
- Optional later upgrade: ElevenLabs behind an env var (~$0.10–0.30/Short) if the local
  voice grates. Start local; the constraint on volume is review time, not money.

### Phase 4 tests

| Area | Assert |
|---|---|
| Contract | Spec without `audio`/`narration` still validates and renders (today's golden spec passes untouched) |
| Timing | Narrated shot duration == audio length + padding, clamped to template bounds |
| Fallback | TTS failure produces a music-only render plus a warning, not a failed job |
| Grounding (researchowl) | A fabricated figure placed only in `narration` is flagged |
| Determinism | Same spec twice → byte-identical audio track (pinned model) |

---

## Phase 5 — archival assets (the Ken Burns template)

The most differentiating work for this niche: real declassified documents, newspaper
clippings and official photos, panned and zoomed with a highlight box. It is what
separates the channel from generic AI slop — and it extends the grounding philosophy to
imagery, which is why the editorial rule below is load-bearing.

**The rule: only assets that come from the session's own sources.** No stock, no image
search, no "looks right". If the scraper didn't see it, the Short doesn't show it.

- shortsmith:
  - New template `archive_pan`: props = asset reference, start/end crop keyframes,
    optional highlight rectangle, mandatory `credit` line (rendered small, always).
  - New endpoint `POST /assets` — content-addressed upload (sha256 as the id), so specs
    reference immutable hashes and re-renders can't silently swap an image.
- researchowl:
  - Asset extraction during scraping: `og:image`, inline images above a size floor, PDF
    pages rendered to PNG. Stored under `/data/assets/{session_id}/` with **the source
    URL persisted per asset** — provenance is the whole point.
  - Spec generation: the prompt receives the asset list (id, source URL, dimensions,
    nearby text) and may use `archive_pan` shots.
  - The claims report grows an assets section: every asset used, with its origin URL, so
    the human gate reviews imagery the same way it reviews figures.
- Rights posture: US federal government works (Blue Book, NARA scans, official releases)
  are public domain — the bulk of this channel's material. Anything else the human
  reviewer judges at the gate that already exists; the credit line renders regardless.
- This is the largest phase. It is two deliverables in truth (asset pipeline; template)
  and the pipeline is useful alone — extracted assets improve the *blog* posts too.

### Phase 5 tests

| Area | Assert |
|---|---|
| Assets | Content-addressing: same bytes → same id; spec referencing an unknown hash is rejected at validation with the exact path |
| Provenance | Every stored asset carries a source URL; claims report lists all used assets |
| Template | Keyframe interpolation renders deterministically; missing `credit` fails validation |
| Editorial | A spec referencing an asset from another session is rejected |

---

## Phase 6 — `short_es` for Zona de Exclusión

Nearly free once the format is proven (phase 2 doc, §11, still true): shortsmith draws
whatever strings it gets and does not care about language; with phase 4, TTS needs a
Spanish voice (Piper has good ones — pin it like the English one). The work is the
prompt, the narrative shapes, and the stopword list — all researchowl, all small.

Do it only after the EN format has produced retention worth copying. A bad format in two
languages is twice the bad format.

---

## Explicitly out — generative video

No Veo, no Sora, no Runway. Not primarily for cost: a documentary channel whose premise
is *"the numbers come from primary sources"* cannot mix in fabricated "recreations"
without undermining exactly what the grounding pipeline protects. If ever revisited, it
would need an on-screen RECREATION label and a very good reason. There is no current
reason.

---

## Free work — no phase required

Available any time, zero researchowl changes, because the contract is fetched live:

- **New vector templates** in shortsmith: `timeline`, `before_after`, `map_zoom`. Same
  discriminated-union pattern as the existing eight; they appear in the prompt
  automatically on the next generation.
- **Narrative tuning** in the prompt: sharper first-shot hook, the
  hook → evidence → unresolved question → CTA arc. Costs one commit, no deploy risk
  beyond a prompt change.

---

## Implementation order

One change at a time, verified before the next — and phase-gated by the Analytics data
from §0.

1. Publish 3–4 Shorts with the current pipeline. Read the retention curves.
2. ~~Phase 4a~~ — done (the preset palette, see above; the user chose to skip the gate
   for the mechanism and let the published Shorts test the palette itself).
3. Phase 4b (narration + captions), grounding extension in researchowl **first** — build
   the check before the thing it checks, same reasoning as phase 2 §12.
4. Free-work templates whenever convenient; they ride along.
5. Phase 5, asset pipeline before template — the pipeline is useful alone.
6. Phase 6 last, and only if the numbers say the format earned a second language.