web-media-getter

name: web-media-getterdescription: "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."risk: safesource: communitysource_type: communitysource_repo: connerkward/web-media-getter-skilldate_added: "2026-06-16"author: Conner K Wardlicense: MITtags:   - media  - images  - video  - gif  - stock  - archival  - attributiontools:   - claude-code  - antigravity  - cursor  - gemini-cli  - codex-cli

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

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 prerequisite: webmedia.py is not bundled in this catalog copy. The named central/scripts helpers and central/.env location are also external prerequisites; use only a separately reviewed upstream installation. Keys: PEXELS_API_KEY, PIXABAY_API_KEY in central/.env (optional — the 5 no-key sources work without them).

Sources

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.) Verify current provider pricing, quotas and API schema; no unlimited/free tier is established here. 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. Use only the explicitly configured provider key/source; never search unrelated .env files for a usable key or override a local endpoint/key to force paid cloud usage. 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.

Downloads need an authorized output directory, HTTPS allowlisted destinations, bounded redirects/bytes/time and non-overwriting filenames. Verify the original asset page/license, commercial/redistribution/attribution and subject rights; preview availability is not a license. Hosted semantic search/audio judging transmits media and can incur costs, requiring destination/payload/budget authorization. Promotional credits and model hearing claims are historical, not verified capabilities.

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.