thedixitjain 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: 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--downloadfetches each result's direct media URL and writesattribution.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
muserskill. 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 withlicense/userfor attribution. Key:FREESOUND_API_KEYincentral/.env(token-based read; full originals would need OAuth — previews suffice for SFX).audio-judge.py <file> "<target>"— sends the clip to OpenAIgpt-audio(audio-native) and returns JSON{heard, score, matches, suggestion}, enabling a generate/fetch → judge → iterate loop. Auto-sources a realsk-OPENAI_API_KEYfrom.env(ignores a locallm-studiostub 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.
Source: sickn33/agentic-awesome-skills → skills/web-media-getter/SKILL.md
Also appears in: sickn33/agentic-awesome-skills/plugins/agentic-awesome-skills/skills/web-media-getter/SKILL.md, sickn33/agentic-awesome-skills/plugins/agentic-awesome-skills-claude/skills/web-media-getter/SKILL.md