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Workflow blueprint

The five-stage pipeline that runs every channel. Plus six rules the pipeline enforces automatically - one per failure that cost real time or money to learn.

The pipeline

Five small objects on a workbench, notebook, magnifier, camera, ink-stamp, paper plane, connected by a thin horizontal line. GATE is highlighted in burnt orange as the only warm colour in the frame.

Plan, select, build, gate, post. Every episode on every channel goes through these five stages. The gate is the load-bearing one - that's why it carries the only warm colour in the diagram.

Plan
Brief against the audio, not before
Select
5 queries per need, vs-hero rank
Build
NEVER rules name their beat
Gate
Cosine + ask.py vision
Post
Copy, thumb, pin, queue

The six rules

Each rule closes one class of failure. The cost is the proof - real time, real money, or real defects that would otherwise have shipped.

Six printed cards in a 3-by-2 grid on a warm-grey workbench. Each card shows a number (01 to 06), a small marker icon, the rule name in bold, and the measured cost in burnt orange. The six cards are: 01 Narrate First ~3h, 02 Five Queries $1.50, 03 Name the Beat ~25min, 04 Transport, Not Model 4h, 05 Full Resolution 6 frames, 06 Source Verbatim 2 dropped.
01
Narrate first, brief against the audio

Estimated beats never match the TTS pauses. Plan after the audio, not before.

~3h saved per rebuild
02
Five queries per need

Identical queries put the same 50 jpgs in every folder; the selector dedups globally, so half the needs see nothing.

$1.50 saved per rebuild
03
Every NEVER names its beat

"No Kanao" rejected the very need whose line is "Then he met Kanao." Constraints are per-beat, not global.

~25min saved per re-SELECT
04
UNPARSEABLE means transport, not model

A timeout reads as a model fault unless the selector logs transport separately. The selector never silently swallows stderr.

4h saved per debugging session
05
Pixel review at full 1080×1920

Six frames passed at 640px and failed at full resolution. Confirm any "wrong character" call by opening the source PNG.

6 frames caught
06
Every claim matches the source verbatim

A claim only goes in if the source states it. Two claims dropped from Musashi for being unsourced; one added to Speedmaster because it was sourced.

2 claims cut for honesty

The QA gate that actually runs

The gate runs ResNet50 cosine similarity at three frame positions per scene, then asks a vision model for a modern/anachronism check. The pass/fail criterion is calibrated against the reverse-engineered luxury episode:

Scenet = 0%t = 50%t = 98%
s1_establish0.325 / 0.3170.324 / 0.3160.329 / 0.320
s2_deepen0.335 / 0.3270.353 / 0.3460.369 / 0.362
s3_exit0.231 / 0.2300.228 / 0.2260.211 / 0.213

The numbers are vs-seed / vs-hero. The third scene has the lowest scores and that is correct - a character walking away should diverge from the reference. The gate flags catastrophic divergence, not stylistic movement.

What the workflow proves to a brand

A brand evaluating this work signs off on "the character looks like the character" - not on the mechanism. The blueprint is the proof that the work is auditable end to end: every claim traces to a source, every frame is gated against a reference, every rule has a name and a measured cost, and every failure mode is documented.

See Stage 2 (contact sheets) Forgotten Fronts channel EP_MUSASHI breakdown