Taste Distillation
SkillMediaTaste-distillation is a skill that turns a set of reference videos into a reusable style pack. The taste Claude skill measures footage numerically,
Available today. Use it from your connected AI after setup.
No other account needed.
Have reference video clips whose look you want to capture, and a Python environment with the dependencies listed in scripts/requirements.txt.
Then ask your AI: use the Taste Distillation skill
What your AI can do with it
- Measure reference footage into a style pack folder with grade.json, cadence.json and a man
- Produce a 33^3 look.cube LUT that can be dropped into Resolve as a node LUT
- Record every detected shot boundary and derive a shot-length distribution for pacing
- Extract full-res hero stills from the longest shots as conditioning images
- Lift screen-blend overlay plates onto black and mint GLB prop meshes
- Write a VLM-grounded text spec (spec.json and grounding.txt) for a generative model
Getting started
- Have reference video clips whose look you want to capture, and a Python environment with the dependencies listed in scripts/requirements.txt.
- Add the taste-distillation skill to your agent so it can run the scripts it ships with.
- Ask the agent to distill the look of your clips into a style pack, keeping each named genre in its own pack.
- Run with --dry-run first if you want a pass that reads no credentials and submits no jobs.
- Use the generated pack folder in later editing or generation steps, such as the companion taste-application skill.
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:
- Absolute thresholds fail. On a bright reference an
L>55 AND chroma>12selection 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. - 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't | Why |
|---|---|
| Tune against synthetic test footage | Cost four separate wrong conclusions on one project; real footage overturned every one |
| Trust MAE alone | 1.88 MAE looked like success on a visibly broken frame |
| Use mean/std for chroma | Right-skewed; pushes ~3x too hard |
| Compare only endpoint zones | Both ends are near-neutral by construction |
| Describe the look and stop | The 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
- 268k
- Forks
- 40k
- Last commit
- Sep 2026
ahel review
K1binfo
installs-packagesK1binfo
installs-packages (in scripts/taste/falapi.py)
Automated review, not a security audit. Ruleset v1+k2.
Questions
- What exactly is a Claude skill?
- A skill is a packaged capability an agent can load and run. Taste-distillation ships its implementation in scripts/ and, when activated, measures reference videos into a reusable style pack of LUTs, pacing data, stills, plates and a written spec.
- How do I install the Taste skill in Claud?
- Add the skill to your agent setup so its scripts are available, install the Python dependencies in scripts/requirements.txt, then ask your agent to distill a style pack from your reference clips. No internet connection or account sign-in is needed for local measurement.
Advanced
- Item type
- skill
- Key
taste-distillation- Source
- github.com/affaan-m/ecc