Tasteforge Video

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.
• Schemas are the contract: taste profile, pack manifest, grade, cadence, spec, timeline events, application reports (provider is enum-locked to "none"; dry_run to true).
• Recovered-source lineage and deliberate exclusions live in the repo's skills/taste-application/SOURCE.md. Run python3 -m tasteforge provenance for the machine- readable version.

Use the installed ECC engine for local deterministic commands and interpret its JSON. Install the packaged engine if absent; do not reconstruct its logic inline. taste.resolve is a compatibility import of tasteforge.resolve, so the creative scripts and example project share one verified Resolve adapter.

The python3 -m tasteforge CLI uses the installed ecc-tasteforge distribution. The standalone taste-distillation and taste-application scripts ship in ECC's opt-in media-generation module with their own Python requirements. Neither path requires publishing the user's repository or media.

Before resuming a saved checkout, record its commit and inspect local branches and worktrees for later implementation fixes. Run the canonical package's tests and python3 -m tasteforge apply --help; ECC's text and fixture tests do not prove that the selected Python checkout implements this contract.

Chaining the Creative Skills

Use only the stages the project needs. The taste skill's historical angelcore/cloud-trance palette and beat grammar are optional creative examples; they must not override the current brief or merge distinct numbered genres. Use each genre's actual references for its direction, including 3D Cyber Glitch and Fluid Sketch. TasteForge does not replace these skills or require every renderer. Keep 3D materials, geometry, wireframe behavior, motion, and composition explicit in the genre signature. A 3D request manifest is a plan for an asset; it is not a mesh. A subject-anchor descriptor names a tracking requirement; it is not evidence that a subject was detected or tracked. Inspect actual tracks, track-loss behavior, and rendered subject frames before claiming that CV effects have been applied reliably.

Workflow

1. Interview (interview): collect answers for the look axes — palette, grain, lighting, focal length, camera motion, subject framing, grade, mood adjectives, avoid list — and separately the content brief. Keep look and content separate; merging them is the classic failure.
2. Distill (distill): map the profile onto the spec schema offline, embedding the pack's measured grounding (black/white point, contrast, per-zone chroma, palette, cut rhythm) when a pack is supplied. The result is a dry-run spec: deterministic, provider "none".
3. Validate (validate / inspect): check the pack against its schemas; report errors vs warnings (missing stills in a metadata-only pack are a warning, not an error).
4. Apply (apply): plan shot durations from the pack's measured cadence (seeded, deterministic) over the user's local clips; produce the application report and frame-exact timeline events.
5. Export (export): write CMX3600 EDL + FCPXML 1.9 with rational, NTSC-safe times for import into a real NLE.
6. Audit (provenance): report lineage — recovered-source digests, generation history, fixture provenance, provider references as pointer-only records.

Applying Real Footage Without Repeated Sources

When the brief requires no repeated clips, use a canonical checkout supporting apply --no-repeat --fps, and set the output frame rate explicitly. If those flags are absent, report the implementation gap rather than silently using legacy round-robin selection. Strict mode uses each normalized source path at most once in manifest order and rejects insufficient or too-short sources. Prepare enough reviewed selects to fill the cadence plan. This is source-level uniqueness, not support for distinct in/out ranges from the same recording.

The application report is a cut plan. It does not perform visual shot ranking, grade footage, apply a LUT, render overlays, or import a Resolve project. Keep the pack's measured reference cadence separate from the output frame rate.

The export CLI expects {"clips": [...]}. Wrap the application's timeline_events under clips before exporting, and pass the same --fps used for application; export's default frame rate must not reconform the plan. Check the emitted event count, total frames, unique sources, and media linkage before handing the timeline to the editing workflow. When that workflow applies overlapping effects in an NLE, allocate compatible tracks and read back every requested start, end, and duration. A returned item or a successful append call alone does not prove that every scheduled effect was placed; reject missing, shifted, or truncated placements before rendering.

File-Driven Multimodal Contract

Use this path when local references must drive dry-run generation plans for image, video, and 3D-asset outputs while preserving genre separation:

python3 -m tasteforge multimodal --config workflow.json --out-dir out/multimodal

The config names numbered genres and local evidence files. Keep these candidate genres distinct rather than blending them into one generic aesthetic:

1. Flash Ethereal
2. 3D Cyber Glitch
3. Fluid Sketch

The command measures local references with ffprobe/ffmpeg and emits one style spec per genre, separate image, video, and 3D-asset manifests, provenance, and a Resolve effect recipe. The effect schedule must be seeded aperiodic. CV effects require a real subject anchor whose exact lost-track policy is disable_effect_until_track_recovers; continue_without_anchor and every other policy fail closed. Every effect carries placement constraints that preserve faces and readable type and prevent decorative corner meshes from replacing full-frame 3D work.

The returned receipt is the bundle boundary. It binds every emitted evidence artifact by relative path, byte size, SHA-256, genre, modality, provider_execution:false, and exact reference/time provenance. The receipt requires provider_calls:0 as an exact integer (the JSON boolean false is invalid), provider_execution:false, and dry_run:true. Every genre spec also requires explicit dry_run:true. The Resolve effect recipe requires that same exact integer provider_calls:0, provider_execution:false, and dry_run:true. Every modality manifest and every nested request must contain all four exact fail-closed fields: integer provider_calls:0, provider_execution:false, dry_run:true, and submit:false; each request also requires provider_call_mode:"disabled". A missing field is a rejection, not a default, and dry_run:false must be rejected before output is written.

Treat booleans as invalid numbers everywhere in timeline, evidence, probe, and source-duration data. Every such numeric value must be a finite real: reject true, false, NaN, infinities, negative event starts, non-positive durations, out-of-range evidence times, and events ending beyond the declared finite positive timeline. Whole-file evidence uses an explicit whole-file time basis and never invents timestamps.

Receipt references are the duration authority. Key each validated reference duration by its cited SHA-256; duplicate occurrences of one digest must agree on duration or the bundle is invalid. Every effect evidence source_duration and every subject-anchor source_duration must equal that digest's validated receipt duration, not merely contain its cited time. Probe duration and all probe measurements must describe the same stable bytes used for byte count and SHA-256. If the source mutates while probing or rehashes differently while it is still available, fail closed rather than emitting or accepting a receipt.

Always run bundle validation after creation. A missing image, video, or 3D-asset manifest must fail closed. Genericized or duplicate genres, periodic schedules, unanchored CV effects, missing placement constraints, provider-execution flags, unbound output files, byte-size drift, or SHA-256 tampering must fail closed. Reject output roots, intermediates, or artifacts that are symlinks, and reject special files (including FIFOs and devices); outputs must remain regular files under a real directory tree. If local ffmpeg or ffprobe is unavailable, the CLI must return its bounded nonzero local-media-processing error without a Python traceback. Do not repair a failed receipt by deleting evidence or weakening validation.

Example Session

# after installing the ECC engine; paths below are your project inputs
python3 -m tasteforge validate stylepacks/flashethereal
python3 -m tasteforge interview --answers answers.json --genre flashethereal --out profile.json
python3 -m tasteforge distill --profile profile.json --pack stylepacks/flashethereal --out spec.json
python3 -m tasteforge apply --pack stylepacks/flashethereal --media media.json --duration 20 --out report.json
python3 -m tasteforge export --events events.json --out-dir out --title flashethereal-cut
python3 -m tasteforge provenance

When shots must be generated, pass the reviewed brief and style direction to taste-application under the user's explicit provider authorization. The offline CLI remains fail-closed; its plans and editable timelines do not prove a provider job ran.