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-visual-target-searchgit 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-visual-target-search)<a href="https://agentmods.dev/skills/kdr/overcast/overcast-visual-target-search"><img src="https://agentmods.dev/badge/skills/kdr/overcast/overcast-visual-target-search/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-visual-target-search"><img src="https://agentmods.dev/badge/skills/kdr/overcast/overcast-visual-target-search.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 | $0.00033 | $0.00718 |
| Opus 5 | $0.00016 | $0.00359 |
| Sonnet 5 | $0.00007 | $0.00144 |
| Haiku 4.5 | $0.00003 | $0.00072 |
Grade A, and why
overcast-visual-target-search 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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
overcast-visual-target-search
Use this skill when the task is to locate a visual target across videos, images,
or captured case media. Use the broad overcast skill and
overcast/reference/verbs.md for exact flags.
Workflow
For a person with a reference image:
overcast doctor --json
overcast case init --json
overcast face ./clip.mp4 --match ./person.jpg --json # a match ≥75% auto-suggests a finding
overcast finding list --state triage --json # triage the auto-suggested lead(s)…
overcast finding accept <id> --json # …accept a real match (or `dismiss <id>`)
overcast crop <face-record-id> --all --class face --json
overcast ask "where does the reference person appear, with timestamps and confidence?" --json
overcast brief --export ./visual-search.md --json # short by default; --full for the per-match timeline
For an object or open-vocabulary target (--detect needs a bound OWLv2 detector —
build it once with scripts/visual-db-uv.sh --detect (it prints DETECT_PY), then
export DETECT_PY=… and bind via the preset: overcast provider setup apply --preset owl-local --yes, which persists a portable shipped: ref for detect.py and uses the
venv python, NOT system python3 which lacks torch/transformers):
overcast see ./clip.mp4 --detect "red backpack" --json
overcast crop <see-record-id> --all --class "red backpack" --json
overcast ask "list target detections with timestamps, confidence, and crop paths" --json
For logos, landmarks, or near-duplicate visual references:
overcast index create refs --type image-ransac --local --json
overcast index add ./reference-logo.png --to <index-id> --json
overcast image match ./clip.mp4 --index <index-id> --json # a RANSAC hit auto-suggests a finding
overcast finding list --state triage --json # then accept/dismiss the lead
Output
Return timestamped matches, similarity or confidence where available, source
record.id, media.at, and cropped evidence paths created by crop.
face --match / image match auto-suggest findings — resolve them with
finding list --state triage → accept/dismiss so a run doesn't leave an
un-triaged queue; the default brief is short, --full for the per-match timeline.
State whether the match came from face --match, see --detect, or local
image-ransac matching.
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 · 66 lines · 33 tokens per session scan A b430ffab1c5b
overcast-visual-target-search is a skill published in the GitHub repository kdr/overcast (16 stars, last pushed 5d ago), licensed Apache-2.0. It adds 33 tokens to every session and 718 once invoked, about $0.0002 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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