CDAF — Cached Descriptive Asset Files

Stop making AI agents watch the same video twice.

CDAF is an open sidecar format for video: a plain-text, timestamped description that lives next to the video file with the same basename. Generate it once, and every AI agent that touches that footage afterward reads a few hundred text tokens instead of running a full video-understanding pass.

npx cdaf-skill

Works on Windows, macOS, and Linux. That installs the agent skill, so your coding agent checks for a .cdaf sidecar and verifies it against the video's hash before it ever spends tokens watching. Then describe your footage once:

pip install "cdaf[generate] @ git+https://github.com/UditAkhourii/cdaf.git#subdirectory=cli"
cdaf generate ./footage

footage/
├── sunset-drone.mp4     ← plays everywhere, untouched
└── sunset-drone.cdaf    ← what agents read instead of watching

Measured (reproducible benchmark, gemini-2.5-flash, 20 questions): answering from the sidecar matched direct video analysis on accuracy — 20/20 vs 19/20 — at 10.1× fewer prompt tokens per question (303 vs 3,066) and ~35% lower latency. The ratio grows linearly with clip length (~50× for 60-second footage). In production use at scale, this pattern cut video-workflow AI costs to roughly 1/25th.

A CDAF asset is clip.mp4 + clip.cdaf: a UTF-8 sidecar with a minimal key: value header and a markdown body. The header carries the SHA-256 and byte size of the exact video described — edit the video and every conforming tool detects the sidecar as stale and refuses to use it. The body is optimized for the actual reader (an LLM):

--- CDAF/1.0
video: sunset-drone.mp4
sha256: 4a7d1ed4…
bytes: 48211394
duration: 00:00:31.500
generator: gemini-2.5-flash
created: 2026-08-26T14:03:12Z
---

## Summary
A 31-second aerial drone clip of a coastal highway at golden hour…

## Segments
[00:00.0-00:05.2] Wide aerial establishing shot: a two-lane coastal highway…
[00:05.2-00:12.8] The drone pushes in and descends toward the highway…

## Transcript
(no speech)

## On-screen Text
(none)

## Tags
drone, aerial, coastal highway, golden hour, sunset, b-roll, …

Full normative rules (freshness semantics, versioning, section grammar): SPEC.md. A complete example: examples/sunset-drone.cdaf.

The cdaf Python package is split by dependency weight:

• Zero-dependency core (sidecar.py) — parse, serialize, chunked SHA-256, freshness checking, segment extraction. Agents and CI can validate sidecars with nothing but the standard library.
• Generator (generate.py) — Gemini Files API, bring-your-own-key, brief/standard/rich detail profiles, token-usage capture. Only imported when you generate.
• Probe (probe.py) — optional ffprobe metadata (duration/resolution/fps), degrades gracefully when ffprobe is absent.

from cdaf import load, check_freshness, sidecar_path_for

sc = load(sidecar_path_for("footage/sunset-drone.mp4"))
if check_freshness("footage/sunset-drone.mp4", sc) == "fresh":
    context_for_llm = sc.body

The format is model-agnostic: the engine's Gemini backend is one implementation, and Claude/GPT/local-VLM backends slot in behind the same Sidecar type.

Requires Python ≥ 3.10. Bring your own Gemini API key (free tier available) — your footage goes to your key, not to us.

pip install "cdaf[generate] @ git+https://github.com/UditAkhourii/cdaf.git#subdirectory=cli"
export GEMINI_API_KEY=your-key          # PowerShell: $env:GEMINI_API_KEY="your-key"

cdaf generate ./footage                 # describe every video, skip fresh sidecars
cdaf status ./footage                   # FRESH / STALE / MISSING report
cdaf read ./footage/sunset-drone.mp4    # print the description (verifies hash first)
cdaf validate ./footage/clip.mp4        # exit 0 iff sidecar is well-formed and fresh

Working from a clone instead? pip install ./cli[generate]. A PyPI release (pip install cdaf) is on the roadmap.

Flags: --detail brief|standard|rich, --model <gemini-model>, --force. validate/read/status need no dependencies and no API key. Safety lives in the tool, not just in guidance: cdaf read refuses to print a stale sidecar.

The skill turns the format into behavior: before analyzing any video, check for the sidecar; verify freshness (size check for exploration, full hash for consequential decisions); read it instead of watching; grep .cdaf files to search whole libraries; regenerate when stale.

Install it with npx cdaf-skill (see above), or pick a scope:

Re-running is safe: an unchanged skill is left alone, and a modified one is backed up to SKILL.md.bak before updating. Prefer to copy it by hand? The file is skills/claude-code/cdaf/SKILL.md.

Any other agent framework — the whole contract is one system-prompt paragraph:

Before analyzing a video file, look for a .cdaf file with the same basename. If its bytes/sha256 header matches the video file, read it instead of processing the video.

Programmatic and agent-native editors (Remotion, HyperFrames, prompt-to-edit tools) already do everything in text — compositions are code, cut decisions are data. Video understanding is the one step that forces them out of the text domain, priced per exposure (~263 tokens per second of footage on Gemini-class models). With CDAF:

• Selection becomes retrieval — "sunset coastal shots, no people" is a grep over .cdaf files, not a multimodal sweep of the library.
• Cut lists come from segment timestamps — [00:05.2-00:12.8] lines map straight to Remotion <Sequence>s or HyperFrames clips.
• Captions and audio sync for free — the Transcript section aligns speech to the timeline.
• The library appreciates — 40 candidate clips ≈ 473k tokens to watch, ~24k to read — every session after the first.

Fully reproducible: bench.py synthesizes test videos with ffmpeg from scripted recipes (known colors, hard cuts, timed word overlays), so ground truth is exact and grading is objective — no LLM judge, no dataset license.

python benchmarks/bench.py make     # synthesize testset (needs ffmpeg)
python benchmarks/bench.py run     # both conditions via Gemini (needs GEMINI_API_KEY)
python benchmarks/bench.py report  # writes benchmarks/RESULTS.md

Each sidecar-answered question saves 2,763 prompt tokens, so generating a sidecar (3,601 tokens) breaks even after ≈1.3 questions per video — everything after that is the ~10× saving. Per-clip and per-question detail: benchmarks/RESULTS.md.

https://zenodo.org/records/22110594 — Cached Descriptive Asset Files (CDAF): A Sidecar Format for Token-Efficient Video Understanding in Agentic Pipelines — arXiv draft (cs.MM / cs.AI): format rationale, benchmark methodology and results, production case study, integration analysis for agentic editors, limitations.

• PyPI release of the cdaf CLI (pip install cdaf)
• MCP server exposing cdaf_read / cdaf_status / cdaf_generate to any MCP client
• Hosted free converter (no local install; bring-your-own-key or free quota)
• Additional generator backends (Claude, GPT, local VLMs)
• Node/TypeScript port of the engine
• Natural-footage benchmark extension with human-verified QA
• Signed sidecars (signature header) for cross-organization trust

MIT — see LICENSE.