Taste Distillation

SkillMedia

taste-distillation is a skill that measures a set of reference videos into a reusable style pack. It captures the look of reference footage as a 3D LUT for colour grade, a shot-length distribution for cut rhythm, hero stills, screen-blend overlay plates, and a text spec for a generative model. The agent can then render content neutrally and apply the pack deterministically instead of relying on colour words in prompts.

Use Taste Distillation in Claude, ChatGPT or Ahel Desktop

Free. Sign in, add Taste Distillation and connect your AI. About a minute.

Also: Claude Code · Cursor · Codex

Then ask your AI: use the Taste Distillation skill

Details

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Have a set of reference videos whose look you want to capture.

Taste DistillationStart free

What your AI can do with it

  • Measure chroma per luminance zone across reference videos
  • Build a 3D LUT of the colour grade
  • Derive a shot-length distribution for cut rhythm
  • Extract hero stills and screen-blend overlay plates
  • Write a text spec for a generative model
  • Mask static interface furniture and detect cuts with an adaptive threshold

Getting started

  1. Have a set of reference videos whose look you want to capture.
  2. Add the taste-distillation skill to your agent.
  3. Point the agent at the reference videos and ask it to measure them into a style pack.
  4. Use the resulting pack to apply the colour grade, pacing, stills, overlays and text spec to new content.

What this skill tells your AI

The instructions your AI receives, as published by affaan-m/ecc in skills/taste-distillation/SKILL.md and read by ahel’s review.

This standalone skill ships its implementation in scripts/; use taste-application for the subsequent generated or local-take edit. Keep each named genre in its own pack. Measurements from Flash Ethereal must not be silently reused for Fluid Sketch or 3D Cyber Glitch. A measured zero is valid data; distinguish it from an absent field.

Local dependencies are in scripts/requirements.txt. Separately authorized provider work also needs scripts/requirements-live.txt, credentials and explicit TASTE_FORGE_ALLOW_LIVE=1. --dry-run does not read credentials or submit jobs. Never infer that a workflow was saved from a local endpoint name; use the actual provider-side workflow or request evidence.

Turn reference videos into a style pack: a folder of measurements and assets that later stages consume deterministically.

When to Activate

  • "capture the look of these clips" / "distill the vibe" / "make this repeatable"
  • User has reference footage and wants a LUT, a grade, or matching pacing
  • Building a library of looks partitioned by genre
  • Any request where the answer would otherwise be "describe the style in a prompt"

The Core Finding

Prompting cannot deliver a grade. Measurement can.

Measured on real footage: three paid generations with escalating colour direction moved midtone a* from +1.9 → +2.8 → +0.3 against a +24.9 target, and contrast never left ~19 against a 34.7 target. Applying a measured pack to the same footage hit chroma MAE 1.88 and contrast 33.7 in one deterministic pass, for free.

So the split is: the model supplies content, motion and lighting structure; the pack supplies the look. Colour words in a generation prompt are worse than useless — they cost money and push the render away from the neutral base the LUT wants. Say so explicitly in the prompt: "Colour: none. Render neutral. Grading is applied afterwards."

What a Pack Contains

stylepacks/<genre>/
  grade.json      measured colour statistics (see below)
  cadence.json    every detected shot boundary + the derived distribution
  look.cube       33^3 LUT, drag straight into Resolve as a node LUT
  spec.json       VLM description, grounded in the measurements
  grounding.txt   the measured facts fed to the VLM
  stills/         full-res frames from the longest shots (conditioning images)
  plates/         screen-blend overlay elements lifted onto black
  props/          minted GLB meshes
  pack.json       manifest

Running It

python mint.py --genre <name> --refs a.mov b.mov c.mov     # offline, no API key
python distill.py --genre <name>                            # one VLM call

mint.py is pure numeric analysis — no network, no key, deterministic, so a pack can be regenerated rather than backed up.

The Measurements That Matter

Chroma by luminance zone, not globally

Colour identity usually lives in one luminance band. A global a*/b* offset mathematically cannot represent split-toning. Measure chroma inside zones (L* edges [0,15,35,55,75,100]).

A real signature: violet at L*25 (a* +24.9, b* −17.5), near-neutral at both ends. Reporting only the darkest and lightest zones calls that "uniform cast" — always print the whole curve.

Median + MAD, never mean + std

Chroma in real reference sets is strongly right-skewed. On one measured reel the mean midtone chroma was 36.9 against a median of 17.5, so a mean-based LUT pushed colour ~3x harder than the material warranted.

Contrast is std(L*), not white minus black

The white−black range is ~100 on almost any real footage and discriminates nothing.

Background share is a first-class statistic

Record the share of pixels below L*10. No moment of the distribution can see it: a clip can hold the right mean, std and chroma while its blacks have been lifted into grey. This is exactly how a grade once scored MAE 1.88 / contrast 33.7 while the actual frame was a muddy purple mess.

Mask the interface before measuring

Screen-recorded references carry static furniture — letterbox bars, a status bar, a like icon, caption text. All of it lands in the statistics as if it were the look: black bars inflate shadow weight, a red heart skews a* toward magenta. Temporal variance separates them cleanly — the footage moves, the interface does not — so no hand-tuned crop is needed. On real material this keeps ~65% of pixels.

Cadence needs an adaptive threshold

The right content-detector threshold is material-dependent: a high-contrast action reference cuts hard enough for 30, a moody one hides its cuts under it. Sweep descending thresholds and take the highest one that still recovers ≥90% of the shots the most sensitive setting finds — that biases toward real cuts over noise. Reject thresholds implying an absurd cut rate (>100/min); continuous camera moves trip the detector every frame.

Run the whole sweep in one decode pass with a shared StatsManager. The naive version re-decodes per threshold, which on 60fps source is the difference between seconds and minutes.

Overlay Plates: Assets, Not Screenshots

A still is a whole frame — compositing one just puts a second picture on top. A plate is the reference's graphic vocabulary (flares, streaks, glitch fragments) lifted onto black so it screen-blends with no keying.

Two traps, both hit on real material:

  1. Absolute thresholds fail. On a bright reference an L>55 AND chroma>12 selection takes ~90% of frame, and the "plate" is the picture — including a recognisable face. Select by percentile (~top 3%) and reject any plate covering more than ~22% of frame.
  2. Rank by separation, not by brightness. "Share of bright saturated pixels" ranks a washed-out frame top and a black frame with one intense flare — the actual signature — near the bottom. Score p99.5(energy) / median(energy).

Also mask before scoring: burnt-in typography is bright, saturated and high-contrast, so an unmasked run yields a perfect plate of someone else's title card.

Grounding the VLM

Feed the measurements into the system prompt before asking for a description. Ungrounded, a VLM will report "no apparent colour grading, neutral" on footage with a +24.9 a* cast. Grounded, it describes the cast correctly and infers the secondary accent independently.

Ban hedging words (varied, mixed, dynamic, some, often, neutral, or) — a model cannot render "varied lighting". Enforce the ban in code, not just in the prompt: it was violated in roughly one run in three. Re-ask per-field, keep the least-hedged answer after N attempts rather than failing.

Caveat worth stating to the user: once the spec is grounded in the measurements it is no longer an independent check on them.

LUT Baking Gotchas

  • A LUT can only encode a per-pixel RGB function. Anything distribution-dependent (histogram matching, percentile anchors) must be reduced to a constant before baking, or it silently measures the uniform LUT grid instead of the footage.
  • cv2.cvtColor(LAB2RGB) clamps internally, so an out-of-gamut test using it reports 0%. Convert Lab→linear sRGB by hand; a real measurement was 83.3% OOG.
  • Offset chroma transfer, not affine. Affine divides by the source σ and overshoots — on real footage it flipped b* to +11.6 against a −17.5 target. Offset took MAE from 6.23 to 2.13.
  • Gamut compression cost 3.8x runtime for identical MAE. Make it opt-in.

Anti-Patterns

Don'tWhy
Tune against synthetic test footageCost four separate wrong conclusions on one project; real footage overturned every one
Trust MAE alone1.88 MAE looked like success on a visibly broken frame
Use mean/std for chromaRight-skewed; pushes ~3x too hard
Compare only endpoint zonesBoth ends are near-neutral by construction
Describe the look and stopThe spec is for content and structure; the pack is for colour

Handoff

The pack is the interface. Once it exists, use the taste-application skill to generate and assemble against it, or hand look.cube to a colourist directly.

Bundled Code

scripts/ in this skill is a working implementation, not pseudocode. It has no project-specific assumptions: point it at any reference videos and it produces a pack.

pip install -r scripts/requirements.txt
export FAL_KEY=...            # only needed for the stages that call fal

Every network call is stubbed under TASTE_FORGE_DRY_RUN=1 or --dry-run, so the plan, prompts, track layout and manifest can be inspected without spending.

Signals

GitHub stars
270k
Forks
40k
Last commit
Sep 2026

ahel review

  • K1binfo
    installs-packages
  • K1binfo
    installs-packages (in scripts/taste/falapi.py)

Automated review, not a security audit. Ruleset v1+k2.

Questions

What does the style pack contain?
A 3D LUT of the colour grade, a shot-length distribution for cut rhythm, hero stills, screen-blend overlay plates, and a text spec for a generative model.
How does it measure the colour grade?
It measures chroma per luminance zone and uses median and MAD statistics to build the 3D LUT.
How does it detect cuts?
It detects cuts with an adaptive threshold and masks static interface furniture.
Can I use the style pack with any generative model?
The pack includes a text spec for a generative model, but the skill does not name or integrate with specific models.
Advanced
Item type
skill
Key
taste-distillation
Source
github.com/affaan-m/ecc