TasteForge Video

For the complete standalone creative pipeline, use taste-distillation then taste-application. Those ECC skills ship their Python scripts directly: measure references, generate or pass through existing takes, grade, cut, composite, verify, and hand off to Blender and Resolve. No separate video repository is required for that flow. This skill documents ECC's packaged offline engine and its strict evidence contract.

TasteForge turns "make it feel like this reference" into a repeatable, inspectable workflow: interview taste, distill it into a structured style pack, validate the pack, apply its measured cadence and look to local media, and export an editable timeline. The canonical implementation is the tasteforge package shipped inside ECC at skills/taste-application/scripts/tasteforge/. Ito-Markets/ito-video is an example project that consumes the packaged ECC engine.

When to Use

• The user asks to interview for video taste before any footage is made ("ask me about the look", "interview me about aesthetic direction").
• The user wants to distill an aesthetic into structured constraints — a reusable style pack rather than vibes ("turn these references into a pack").
• The user wants to validate a style pack (is the metadata complete, schema-valid, cadence measured, spec distilled?).
• The user wants to apply a style pack to local footage — plan a cut from the pack's measured cadence over local clips, deterministically.
• The user wants to export EDL/FCPXML — an editable, frame-exact handoff to DaVinci Resolve / Premiere / Final Cut.
• The user asks for a generated-media provenance audit — where did this pack, spec, or cut come from; what was measured locally versus generated by a provider; what was dry-run.
• The user asks to discover or plan file-driven multimodal image, video, or 3D-asset outputs from local reference files, including separate manifests, subject-anchored CV effects, or Resolve effect recipes.
• The user mentions TasteForge, style packs, flashethereal, taste distillation, cadence/rhythm planning, multimodal discovery, distill/apply workflows, or a taste interview for video.

Local Deterministic Operations vs Provider Generation

This boundary is the core of this compatibility skill. Its operations are local, deterministic, and offline:

Provider generation must fail closed in ECC. Any live Fal (or other provider) call — generating shots, minting prop meshes, hosted VLM distillation — requires explicit separately authorized execution under a separate lane with its own review. In this compatibility lane, ECC never calls Fal, never reads any API key or other credentials (FAL_KEY included), uploads no media, and mutates no provider account state. When a request needs provider generation, state exactly that boundary, run the local half (interview, pack validation, planning, export), and stop.

Never claim a Fal workflow is saved. A local reference to a Fal endpoint, model id, or dry-run URL (they appear inside pack metadata) is reference-only: it never means a provider-side workflow was saved, persisted, or is authorized to run. Anything produced offline carries dry-run/dry_run semantics — say "dry-run spec" or "deterministic plan", never "generated by the model".

Canonical Implementation

• Repository: affaan-m/ECC; Python distribution ecc-tasteforge, package directory skills/taste-application/scripts/tasteforge/. Install from the extracted ECC package with python3 -m pip install ./skills/taste-application/scripts. The example project Ito-Markets/ito-video pins a specific ECC commit.
• CLI: python3 -m tasteforge <command> — provenance, inspect, validate, interview, distill, apply, export, multimodal. --live flags exit with code 2 and refuse.

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