Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add kdr/overcast --skill overcast-scene-locategit clone --depth 1 https://github.com/kdr/overcastWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/kdr/overcast/overcast-scene-locate)<a href="https://agentmods.dev/skills/kdr/overcast/overcast-scene-locate"><img src="https://agentmods.dev/badge/skills/kdr/overcast/overcast-scene-locate/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/kdr/overcast/overcast-scene-locate"><img src="https://agentmods.dev/badge/skills/kdr/overcast/overcast-scene-locate.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00052 | $0.01774 |
| Opus 5 | $0.00026 | $0.00887 |
| Sonnet 5 | $0.00010 | $0.00355 |
| Haiku 4.5 | $0.00005 | $0.00177 |
Grade A, and why
overcast-scene-locate scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.
overcast-scene-locate
Use this skill when the task is "where was this taken?": geolocate an image or
video from what is visible in it. Use the broad overcast skill and
overcast/reference/verbs.md for exact flags. Escalate cheap-before-billed —
description and OCR are free; reverse image search bills per result, so run it only
on the strongest clues.
Workflow
- Check embedded metadata FIRST, then read the scene for clues (both free). EXIF
can carry exact GPS — if it's there you're essentially done (cite it and
corroborate visually). Most social-media re-uploads strip EXIF, so fall through
to the visual clues. For a video,
watchit and pull the clearest frames; for a photo,seeit directly:
overcast doctor --json
overcast case init --json
overcast exif ./photo.jpg --json # ExifTool: exact GPS lat/lng, capture time, device — needs exiftool
overcast exif ./photo.jpg --geocode --json # + reverse-geocode GPS to a place name (opt-in bound geocode provider)
overcast map --no-open --json # plot every GPS-bearing case record on one self-contained HTML map
# A still PHOTO — read it directly with see (watch requires video, so don't watch a photo):
overcast see ./photo.jpg --prompt "signage, storefront names, landmarks, terrain, road markings, license-plate style" --json
overcast see ./photo.jpg --ocr --json # street signs, storefronts, plates, notices
# A VIDEO — watch it, then read the clearest frames via frame://:
overcast watch ./clip.mp4 --json
overcast see frame://<watch-record-id>@<seconds> --prompt "signage, storefront names, landmarks, terrain, vegetation, road markings, side of road traffic drives on" --json
overcast see frame://<watch-record-id>@<seconds> --ocr --json # street signs, storefronts, plates, notices
- Materialize the strongest clue regions as crops.
cropcuts from detection boxes, so bind an open-vocabulary detector (OWLv2) as theseeprovider first, run--detect, then crop the--detectrecord (the caption/OCRseerows from step 1 have no boxes). Crops become the reverse-search queries:
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 118 lines · 52 tokens per session scan A 6cc6c0faf3c3
overcast-scene-locate is a skill published in the GitHub repository kdr/overcast (16 stars, last pushed 6d ago), licensed Apache-2.0. It adds 52 tokens to every session and 1,774 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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