overcast-canvass

overcast-canvass is a skill for Claude Code from kdr/overcast. It costs 78 tokens per session (1,310 once invoked), scanned A, original, Apache-2.0.

A camera-search workflow that finds fixed cameras listed in OpenStreetMap and public webcams near a location, then maps or reviews the results. It can turn a street address into coordinates, but the results are leads rather than a complete camera inventory.

In plain words
What is it for?
Use it to investigate which public cameras might overlook a scene, starting from an address or latitude and longitude and reviewing map markers, webcam images, and their coordinates.
Why use it?
It gathers nearby public-camera information in one place while making clear that crowd-mapped and registered webcam sources can be incomplete.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is bash providers/senses/geocode/geocode.sh --query "350 Fifth Ave, New York, NY" --json.

Part of the overcast plugin — 35 skills, 1 hook shipped together

Good fit Use it to investigate which public cameras might overlook a scene, starting from an address or latitude and longitude and reviewing map markers, webcam images, and their coordinates.

Compare 6 skills from other repositories ↓
Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/kdr/overcast
agentmods
npx agentmods add skills/kdr/overcast/overcast-canvass

Made for: Claude Code.

Or install overcast, the plugin that ships this one along with the rest of its 35 skills, 1 hook.

Wrote 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.

agentmods badge for overcast-canvass

README.md
[![agentmods](https://agentmods.dev/badge/skills/kdr/overcast/overcast-canvass.svg)](https://agentmods.dev/skills/kdr/overcast/overcast-canvass)
Your own site
<a href="https://agentmods.dev/skills/kdr/overcast/overcast-canvass"><img src="https://agentmods.dev/badge/skills/kdr/overcast/overcast-canvass.svg" alt="Measured on agentmods" height="20"></a>
Per session 78 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,310 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00078 $0.01310
Opus 5 $0.00039 $0.00655
Sonnet 5 $0.00016 $0.00262
Haiku 4.5 $0.00008 $0.00131

Measured 8d ago against content hash 542e2c20d803, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

overcast-canvass 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 8d 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.

skills/overcast-canvass/SKILL.md · 102 lines

How it starts

The opening of the file, as written. The whole thing — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.

overcast-canvass

Use this skill to run the "door-to-door camera canvass" around a point: which public cameras sit near a location. It is almost entirely existing sources — the overpass source reads OpenStreetMap fixed-camera nodes near a point, and the webcam source lists live public webcams near a point. The only new primitive is a forward geocode (address → coordinates) on the shipped geocode provider. Use the broad overcast skill and overcast/reference/verbs.md for exact flags.

overpass camera hits carry top-level payload.gps, so they plot on map directly. webcam hits are live stills that carry payload.lat/payload.lng (not payload.gps), so they do not appear as map markers — review them as image evidence (open the still / note the coordinates) rather than expecting them on the map. Treat everything as leads, not a complete inventory — OSM cameras are crowd-mapped (incomplete), and webcams are whatever public cams happen to be registered nearby.

Workflow

1. Get a point (<lat>,<lng>)

The canvass runs on coordinates. If you already have them (a map pin, an exif GPS fix, a chronolocate/scene-locate result), use them directly. To turn a street address into a point, use the shipped geocode provider's forward mode (OSM Nominatim, no key — same opt-in privacy note as reverse geocoding: it egresses the queried address to a third party):

# forward geocode: address -> {lat,lng,place}
bash providers/senses/geocode/geocode.sh --query "350 Fifth Ave, New York, NY" --json
# -> {"verb":"geocode","payload":{"place":"Empire State Building, ...","lat":40.748,"lng":-73.985,"mode":"forward"},"state":"ready"}

Read payload.lat / payload.lng for the point. A non-match returns a clean ready record with place:null (never a crash); point OVERCAST_GEOCODE_URL at your own Nominatim/Photon endpoint for volume.

2. Fan the camera sources around the point at a radius

Register both camera sources centered on the point, then scan. man_made=surveillance is the primary OSM tag for a fixed camera; man_made=camera catches some mappings; surveillance:type / camera:* subtags carry direction/mount detail on the nodes that have them.

Read the full file on GitHub · 102 lines

Changes

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.

  1. 8d ago First seen · 102 lines · 78 tokens per session scan A 542e2c20d803

Subscribe to this mod's changes

overcast-canvass is a skill published in the GitHub repository kdr/overcast (16 stars, last pushed 5d ago), licensed Apache-2.0. It adds 78 tokens to every session and 1,310 once invoked, about $0.0004 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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