About

Imported from sickn33/agentic-awesome-skills (skills/web-media-getter/SKILL.md). Install upstream with npx skills add sickn33/agentic-awesome-skills --skill web-media-getter. Copyright stays with the author (MIT).

When to Use

Use when a task needs a REAL or ARCHIVAL photo / clip (hero, texture, reference, historical footage) or a reaction / animated GIF, rather than a generated one — fan out across free image/video/GIF sources in one query and download license-tagged results.

Source: connerkward/web-media-getter-skill (MIT).

web-media

Name: web-media-getter
Author: sickn33

Query many free image/video sources in one fan-out, get a normalized result list, optionally download top-K with an attribution sidecar. Zero-dep stdlib script.

Script: webmedia.py (in this dir). Keys: PEXELS_API_KEY, PIXABAY_API_KEY in central/.env (optional — the 5 no-key sources work without them).

Sources

Source	Key?	Best for	Media
openverse	none	CC web images (Flickr, museums)	image
wikimedia	none	factual / historical / landmark photos	image
internetarchive	none	historical/archival images + films	image, video
loc	none	historical US prints/photos	image
nasa	none	space imagery + video	image, video
pexels	free key	modern stock photos + short video clips	image, video
pixabay	free key	modern photos/illustrations + short clips	image, video
klipy	free key	GIFs — recommended (free, unlimited, Tenor drop-in)	gif
giphy	free key	GIFs — biggest library (prod key needs approval)	gif

GIF sources fire only with --type gif. Keys: KLIPY_API_KEY, GIPHY_API_KEY in central/.env. (tenor adapter removed — Google EOL'd the API 2026-06-30.) klipy is the one to get (free + unlimited); its adapter is unverified — assumes Tenor-compatible request/response; verify against docs.klipy.com when you key it. webmedia.py "shrug" --type gif --count 6 --json

Usage

webmedia.py "1950s street scene" --type image --count 8 --json
webmedia.py "rocket launch" --type video --source nasa,internetarchive
webmedia.py "car factory 1930s" --source all --download --out /tmp/cars

--source all (default) | nokey (no-key only) | comma list (wikimedia,pexels)
--type image|video · --count N · --json · --download --out DIR
--download fetches each result's direct media URL and writes attribution.json (source, author, license, url, page_url) alongside the files.

Record schema

{source, title, url, thumb, dl, page_url, author, license, w, h, type} — dl is the directly-downloadable media URL (None when only a page exists).

The video caveat (important)

Archival sources (Internet Archive, Europeana, LoC) host whole films/documentaries, not single shots. So:

Modern single clip → pexels / pixabay (born as short clips, direct MP4). Done.
Historical single shot → retrieve the IA film here, then extract the shot:
- Twelve Labs Marengo search (free 600 min) — pass the IA public MP4 URL, get a timestamped moment for "car on assembly line", clip with ffmpeg. Semantic, cheap.
- or PySceneDetect (free, local) to cut the film into shots, then rank keyframes with CLIP via the muser skill. Fully offline.

Audio: freesound + audio QA

webmedia.py is image/video. For sound effects (real, CC-licensed) and for judging audio (since Claude can't hear), two sibling scripts live in central/scripts/:

freesound-fetch.py "<query>" [count] [max_sec] [out_dir] — searches freesound.org and downloads short hq-mp3 previews. Prints one JSON line per file with license/user for attribution. Key: FREESOUND_API_KEY in central/.env (token-based read; full originals would need OAuth — previews suffice for SFX).
audio-judge.py <file> "<target>" — sends the clip to OpenAI gpt-audio (audio-native) and returns JSON {heard, score, matches, suggestion}, enabling a generate/fetch → judge → iterate loop. Auto-sources a real sk- OPENAI_API_KEY from .env (ignores a local lm-studio stub env var). Pads sub-2s clips so the speech-tuned model doesn't refuse. Caveat: it reliably describes audio and filters obvious mismatches, but it is NOT a trustworthy judge of subjective qualities like "grating" — it labels nearly any beep "sharp/high-pitched". Use it to cull, not to make the final aesthetic call; confirm by ear.

Where this fits

This is the internet-retrieval capability — peer to muser (local semantic search) and fal (generate). A future media router would fan out across all three and rank candidates by relevance (CLIP), handing aesthetic spreads to lookdev. Don't build that router until the model demonstrably mis-routes without it.

Limitations

Results depend on third-party API availability, quotas, credentials, and license metadata quality.
License tags and attribution fields must still be reviewed before commercial or public use.
Relevance ranking can find plausible assets, but final aesthetic fit, brand safety, and audio suitability require human inspection.

Use it

Copy one of these into your project. Installing also returns the manifest and these snippets.

yaml

targets:
  - https://api.opensmartroute.ai/api/v1/registry/sickn33-agentic-awesome-skills-web-media-getter/manifest   # or paste the manifest below

Manifest

An Open Capability Manifest: the router reads it to know what this does, what it costs and when to pick it.

sickn33-agentic-awesome-skills-web-media-getter.ocm.jsonjson

{
  "ocm": "1",
  "id": "sickn33-agentic-awesome-skills-web-media-getter",
  "kind": "skill",
  "name": "web-media-getter",
  "description": "One query across free image / video / GIF APIs (stock + historical/archival + GIF engines), returning normalized, license-tagged results with optional top-K download + attribution sidecar. The retrieval peer to local semantic search and generative media.",
  "publisher": "sickn33",
  "version": "1.0.0",
  "capabilities": {
    "domains": [
      "finance",
      "data_analysis"
    ],
    "tags": [
      "skill-md",
      "media",
      "images",
      "video",
      "gif",
      "stock",
      "archival",
      "attribution",
      "skills-sh"
    ],
    "languages": [
      "en"
    ]
  },
  "quality_prior": 0.6,
  "examples": [
    "One query across free image / video / GIF APIs (stock + historical/archival + GIF engines), returning normalized, license-tagged results with optional top-K download + attribution sidecar. The retrieval peer to local semantic search and generative media."
  ],
  "primary": false,
  "metadata": {
    "source": {
      "provider": "skills.sh",
      "repository": "https://github.com/sickn33/agentic-awesome-skills",
      "path": "skills/web-media-getter/SKILL.md",
      "ref": "HEAD",
      "url": "https://github.com/sickn33/agentic-awesome-skills/blob/HEAD/skills/web-media-getter/SKILL.md",
      "key": "sickn33/agentic-awesome-skills/skills/web-media-getter/SKILL.md"
    },
    "license": "MIT"
  },
  "instructions": "## When to Use\n\nUse when a task needs a REAL or ARCHIVAL photo / clip (hero, texture, reference, historical footage) or a reaction / animated GIF, rather than a generated one — fan out across free image/video/GIF sources in one query and download license-tagged results.\n\n_Source: [connerkward/web-media-getter-skill](https://github.com/connerkward/web-media-getter-skill) (MIT)._\n\n# web-media\n\nQuery many free image/video sources in one fan-out, get a normalized result list,\noptionally download top-K with an attribution sidecar. Zero-dep stdlib script.\n\n**Script:** `webmedia.py` (in this dir). **",
  "cost": {
    "context_tokens": 1258
  }
}

Fetch it by URL: GET /api/v1/registry/sickn33-agentic-awesome-skills-web-media-getter/manifest?version=1.0.0

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