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cdaf

Read CDAF sidecar files (.cdaf) instead of processing video with vision. Use whenever a task involves understanding, summarizing, searching, editing, or selecting from video files (b-roll, raw clips, footage libraries) — check for a .cdaf sidecar FIRST before analyzing any video

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Precio sin confirmar★ 114 Estrellas de GitHubRegistro actualizado · 7 sept 2026agent-skill

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Documentación de origen, no instrucciones para este sitio. Revisa los permisos antes de ejecutar comandos.

CDAF — Cached Descriptive Asset Files

A .cdaf file is a timestamped, pre-computed description of a video, sitting next to it with the same basename (clip.mp4 → clip.cdaf). Reading it costs a few hundred text tokens; analyzing the video directly costs orders of magnitude more (~263 tokens per second of footage on Gemini-class models). Always prefer the sidecar when it is fresh.

Format spec and tooling: https://github.com/UditAkhourii/cdaf

The rule

Before analyzing ANY video file (.mp4, .mov, .mkv, .webm, .avi, .m4v):

Check for a sidecar: same directory, same basename, .cdaf extension.
Verify freshness before trusting it (see below). A stale sidecar describes an older version of the video — using it is worse than not having one.
If fresh: read the sidecar instead of processing the video. Use it as the account of what the video contains — within the limits below.
If missing or stale: generate one (see below) so the cost is paid once. If you cannot generate, fall back to direct video analysis.

Verifying freshness

The sidecar header carries bytes (file size) and sha256 (content hash) of the exact video it describes. Verification never needs an API key or network access.

Cheap check (usually enough): compare the video's current file size to the header's bytes value. Different size → provably stale.
Strict check: cdaf validate <video> (exit 0 = fresh), or hash the file yourself and compare to the header's sha256:
- PowerShell: (Get-FileHash clip.mp4 -Algorithm SHA256).Hash.ToLower()
- POSIX: sha256sum clip.mp4 / shasum -a 256 clip.mp4
Use the strict check when the decision is expensive to get wrong (publishing, final edits); the cheap check suffices for exploration.

Reading a sidecar

It is plain UTF-8 text — use the Read tool directly, or cdaf read <video> (which verifies the hash automatically and refuses to print a stale sidecar).

Format: a key: value header between --- CDAF/1.0 and ---, then markdown:

## Summary — what the clip is
## Segments — [MM:SS.d-MM:SS.d] description lines covering the whole video (see the trust note below before cutting on these timestamps)
## Transcript — spoken words with timestamps (or (no speech))
## On-screen Text — visible text with timestamps (or (none))
## Tags — retrieval keywords

How much to trust a fresh sidecar

Freshness proves the sidecar describes these exact bytes. It does not prove the description is complete or correct — it is generated text, the record of one model's pass. Three limits matter when a mistake is expensive:

Timestamps are approximate. Boundaries inferred by a model drift (over a second on measured clips), miss real cuts, and occasionally mark cuts that do not exist. They are fine for locating, ranking, and rough trims. Before cutting on them, verify against the container:
```
ffmpeg -v error -i clip.mp4 -vf "select='gt(scene,0.1)',metadata=print:file=-" -f null -
```
Descriptions can add, not just omit. Shown a whole video at once, a model may narrate the outcome a clip implies but never shows — reporting that a task was completed when the footage only shows it being started. Such entries are fluent, specific, and indistinguishable from correct ones. Treat any claim that something was finished, fixed, repaired, or achieved as unverified: check the frames before relying on it, and say the sidecar is your source when you report it. An omission is a visible gap; an addition reads exactly like a fact.
Fine visual state is unreliable. Strike-through and other mark-up on a list (crossed out, checked, highlighted), small or stylised text, and subtle motion are often missed or reported at chance. Incidental background text on packaging or labels has produced confident phantom brand names. If such a detail carries the meaning of the shot, look at the frame.

Provenance keys. When a producer records how the body was made, x- keys tell you which parts were measured rather than inferred:

Key	Means
`x-shot-source: ffmpeg-scene-detect@<threshold>`	Boundaries came from the container and are frame-exact — no need for the ffmpeg check above.
`x-shot-isolation: per-shot`	Each segment was described without sight of the others, which suppresses invented continuity between shots.
`x-transcript-timing: measured-rms`	Transcript times were measured from audio. `none` means no measurable speech.

Absent these keys, assume the weaker case: inferred boundaries, whole-video context, and guessed transcript times.

None of this argues for re-watching by default — that would forfeit the entire saving. Verify the specific claim your decision rests on, not the whole clip.

Generating sidecars

Two providers. Both write the same v1.0 format and either output passes cdaf validate.

Local model — no API key, no cost, footage stays on the machine

cdaf generate <video> --local          # or --provider local

Needs ffmpeg and an OpenAI-compatible endpoint serving a model with a vision encoder (default http://127.0.0.1:8090/v1, override with --base-url / --model, or the CDAF_BASE_URL / CDAF_LOCAL_MODEL env vars). An audio encoder, where the model has one, is used for the transcript. Check the endpoint is up before offering this route:

curl -s localhost:8090/props   # llama-server: reports which modalities are loaded

Slower per clip than the API, but free and private, and cost scales per shot rather than per second of footage — so long clips are far cheaper here. Set CDAF_PROVIDER=local to make it the default.

Gemini API

cdaf generate <video-or-directory>      # skips sidecars that are already fresh
cdaf generate <video> --force           # regenerate even if fresh
cdaf generate ./footage --detail rich   # brief | standard | rich

Needs Python >= 3.10 and GEMINI_API_KEY (free tier: https://aistudio.google.com/apikey). Install the CLI once:

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

Faster per clip and handles whole directories, but calls a paid API. Ask the user before batch-generating a large library, and tell them roughly how many videos you are about to process.

Working across a footage library

Survey coverage: cdaf status <dir> lists every video as FRESH/STALE/MISSING.
To find footage matching a need ("sunset city shots"), grep the .cdaf files — never open the videos: search *.cdaf for the relevant keywords, then rank by the Segments detail.
Batch-fill gaps: cdaf generate <dir> (fresh sidecars are skipped automatically).

What NOT to do

Do not treat a sidecar as fresh without at least the size check.
Do not invent visual details beyond what the sidecar states; if the task needs information the sidecar lacks (exact colors, a specific frame), say so and fall back to targeted direct analysis of just the needed timestamp range.
Do not edit .cdaf files by hand to "update" them — the header hash would then describe a video the body no longer matches. Regenerate with cdaf generate <video> --force instead.

Ver fuente ↗

Metadatos del archivo

name: cdaf
description: Read CDAF sidecar files (.cdaf) instead of processing video with vision. Use whenever a task involves understanding, summarizing, searching, editing, or selecting from video files (b-roll, raw clips, footage libraries) — check for a .cdaf sidecar FIRST before analyzing any video directly.

Ver texto original

---
name: cdaf
description: Read CDAF sidecar files (.cdaf) instead of processing video with vision. Use whenever a task involves understanding, summarizing, searching, editing, or selecting from video files (b-roll, raw clips, footage libraries) — check for a .cdaf sidecar FIRST before analyzing any video directly.
---

# CDAF — Cached Descriptive Asset Files

A `.cdaf` file is a timestamped, pre-computed description of a video, sitting next to
it with the same basename (`clip.mp4` → `clip.cdaf`). Reading it costs a few hundred
text tokens; analyzing the video directly costs orders of magnitude more (~263 tokens
per second of footage on Gemini-class models). **Always prefer the sidecar when it is
fresh.**

Format spec and tooling: https://github.com/UditAkhourii/cdaf

## The rule

Before analyzing ANY video file (`.mp4`, `.mov`, `.mkv`, `.webm`, `.avi`, `.m4v`):

1. **Check for a sidecar**: same directory, same basename, `.cdaf` extension.
2. **Verify freshness** before trusting it (see below). A stale sidecar describes an
older version of the video — using it is worse than not having one.
3. **If fresh**: read the sidecar instead of processing the video. Use it as the
account of what the video contains — within the limits below.
4. **If missing or stale**: generate one (see below) so the cost is paid once. If you
cannot generate, fall back to direct video analysis.

## Verifying freshness

The sidecar header carries `bytes` (file size) and `sha256` (content hash) of the
exact video it describes. **Verification never needs an API key or network access.**

- **Cheap check (usually enough)**: compare the video's current file size to the
header's `bytes` value. Different size → provably stale.
- **Strict check**: `cdaf validate <video>` (exit 0 = fresh), or hash the file
yourself and compare to the header's `sha256`:
- PowerShell: `(Get-FileHash clip.mp4 -Algorithm SHA256).Hash.ToLower()`
- POSIX: `sha256sum clip.mp4` / `shasum -a 256 clip.mp4`
- Use the strict check when the decision is expensive to get wrong (publishing,
final edits); the cheap check suffices for exploration.

## Reading a sidecar

It is plain UTF-8 text — use the Read tool directly, or `cdaf read <video>` (which
verifies the hash automatically and refuses to print a stale sidecar).

Format: a `key: value` header between `--- CDAF/1.0` and `---`, then markdown:

- `## Summary` — what the clip is
- `## Segments` — `[MM:SS.d-MM:SS.d] description` lines covering the whole video
(see the trust note below before cutting on these timestamps)
- `## Transcript` — spoken words with timestamps (or `(no speech)`)
- `## On-screen Text` — visible text with timestamps (or `(none)`)
- `## Tags` — retrieval keywords

## How much to trust a fresh sidecar

Freshness proves the sidecar describes *these exact bytes*. It does not prove the
description is complete or correct — it is generated text, the record of one model's
pass. Three limits matter when a mistake is expensive:

- **Timestamps are approximate.** Boundaries inferred by a model drift (over a second
on measured clips), miss real cuts, and occasionally mark cuts that do not exist.
They are fine for locating, ranking, and rough trims. Before cutting on them, verify
against the container:
```bash
ffmpeg -v error -i clip.mp4 -vf "select='gt(scene,0.1)',metadata=print:file=-" -f null -
```

- **Descriptions can add, not just omit.** Shown a whole video at once, a model may
narrate the outcome a clip implies but never shows — reporting that a task was
completed when the footage only shows it being started. Such entries are fluent,
specific, and indistinguishable from correct ones. Treat any claim that something
was **finished, fixed, repaired, or achieved** as unverified: check the frames before
relying on it, and say the sidecar is your source when you report it. An omission is
a visible gap; an addition reads exactly like a fact.

- **Fine visual state is unreliable.** Strike-through and other mark-up on a list
(crossed out, checked, highlighted), small or stylised text, and subtle motion are
often missed or reported at chance. Incidental background text on packaging or
labels has produced confident phantom brand names. If such a detail carries the
meaning of the shot, look at the frame.

**Provenance keys.** When a producer records how the body was made, `x-` keys tell you
which parts were measured rather than inferred:

| Key | Means |
|---|---|
| `x-shot-source: ffmpeg-scene-detect@<threshold>` | Boundaries came from the container and are frame-exact — no need for the ffmpeg check above. |
| `x-shot-isolation: per-shot` | Each segment was described without sight of the others, which suppresses invented continuity between shots. |
| `x-transcript-timing: measured-rms` | Transcript times were measured from audio. `none` means no measurable speech. |

Absent these keys, assume the weaker case: inferred boundaries, whole-video context,
and guessed transcript times.

None of this argues for re-watching by default — that would forfeit the entire saving.
Verify the specific claim your decision rests on, not the whole clip.

## Generating sidecars

Two providers. Both write the same v1.0 format and either output passes `cdaf validate`.

### Local model — no API key, no cost, footage stays on the machine

```bash
cdaf generate <video> --local # or --provider local
```

Needs `ffmpeg` and an OpenAI-compatible endpoint serving a model with a vision encoder
(default `http://127.0.0.1:8090/v1`, override with `--base-url` / `--model`, or the
`CDAF_BASE_URL` / `CDAF_LOCAL_MODEL` env vars). An audio encoder, where the model has
one, is used for the transcript. Check the endpoint is up before offering this route:

```bash
curl -s localhost:8090/props # llama-server: reports which modalities are loaded
```

Slower per clip than the API, but free and private, and cost scales per **shot** rather
than per second of footage — so long clips are far cheaper here. Set `CDAF_PROVIDER=local`
to make it the default.

### Gemini API

```bash
cdaf generate <video-or-directory> # skips sidecars that are already fresh
cdaf generate <video> --force # regenerate even if fresh
cdaf generate ./footage --detail rich # brief | standard | rich
```

Needs Python >= 3.10 and `GEMINI_API_KEY`
(free tier: https://aistudio.google.com/apikey). Install the CLI once:

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

Faster per clip and handles whole directories, but calls a paid API. **Ask the user
before batch-generating a large library**, and tell them roughly how many videos you are
about to process.

## Working across a footage library

- Survey coverage: `cdaf status <dir>` lists every video as FRESH/STALE/MISSING.
- To find footage matching a need ("sunset city shots"), grep the `.cdaf` files —
never open the videos: search `*.cdaf` for the relevant keywords, then rank by the
Segments detail.
- Batch-fill gaps: `cdaf generate <dir>` (fresh sidecars are skipped automatically).

## What NOT to do

- Do not treat a sidecar as fresh without at least the size check.
- Do not invent visual details beyond what the sidecar states; if the task needs
information the sidecar lacks (exact colors, a specific frame), say so and fall
back to targeted direct analysis of just the needed timestamp range.
- Do not edit `.cdaf` files by hand to "update" them — the header hash would then
describe a video the body no longer matches. Regenerate with
`cdaf generate <video> --force` instead.

Revisar el código fuente

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Ejecutarlo: Requisitos sin confirmar. Consulta los costes del agente, API y servicios en la fuente.
Licencia: MIT
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Licencia: MIT

Dependency or permission surface needs review
Permission surface may require sandboxing
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Stars/forks activity: 114 stars, 6 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

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Fuente y notas de uso

Indexado

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Repositorio fuente: UditAkhourii/cdaf
Licencia: MIT
Versión: 1.0.0
Último push de GitHub: 28 ago 2026
Registro actualizado: 7 sept 2026
Ruta de instrucciones: skills/claude-code/cdaf/SKILL.md @ e56206463127

Versión declarada en el registro; consulta las versiones de la fuente.

Calidad

64/100

Prometedor

Confianza

62/100

Solo sandbox

Auditoría

74/100

Requiere revisión

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Dependency or permission surface needs review
Permission surface may require sandboxing
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Stars/forks activity: 114 stars, 6 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

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  "agent_proven": {
    "version": "agent-proven-v1",
    "score": 0,
    "tier": "unproven",
    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
      "totalOutcomes": 0,
      "successfulOutcomes": 0,
      "failedOutcomes": 0,
      "installAttempts": 0,
      "installSuccessRate": null,
      "successRate": null,
      "recentSuccessRate": null,
      "recentFailureRate": null,
      "riskBlocked": 0,
      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 74,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "Stars/forks activity: 114 stars, 6 forks; issue activity unavailable in current metadata",
      "Dependency/runtime risk: command execution surface, credential or environment access",
      "Permission surface: secrets or environment access, shell or command execution"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 64,
    "label": "Promising"
  },
  "supply": {
    "track": "Research and knowledge work",
    "scenario": "Research agents",
    "maintenance": "2mo since push",
    "risk": "Needs review"
  },
  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "high-compliance environments without internal security review",
    "No major risk signals from current metadata",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "Quality score needs review",
    "Permission surface needs review: secrets or environment access, shell or command execution"
  ],
  "agent_contract": {
    "task_input": "Use cdaf in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 70/100 Manual review",
      "Audit: 74/100 Needs review",
      "Safety: 34/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "uditakhourii-cdaf (cdaf)",
      "install_command": "npx skills add UditAkhourii/cdaf --skill cdaf",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "uditakhourii-cdaf",
      "task": "Use cdaf in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/uditakhourii-cdaf",
    "api": "https://www.openagentskill.com/api/agent/skills/uditakhourii-cdaf",
    "audit": "https://www.openagentskill.com/skills/uditakhourii-cdaf/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=uditakhourii-cdaf&task=Use%20cdaf%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20cdaf%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20cdaf%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/uditakhourii-cdaf/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/uditakhourii-cdaf"
  }
}

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Creador: UditAkhourii
Fuente: UditAkhourii/cdaf
Indexado por: Índice comunitario de OpenAgentSkill

La atribución enlaza al repositorio público o al perfil del creador. Los creadores pueden reclamar la ficha para actualizar las señales de propiedad.

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