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 agentmods add skills/kdr/overcast/overcast-wherenpx skills add kdr/overcast --skill overcast-wheregit 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-where)<a href="https://agentmods.dev/skills/kdr/overcast/overcast-where"><img src="https://agentmods.dev/badge/skills/kdr/overcast/overcast-where.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00037 | $0.00627 |
| Opus 5 | $0.00018 | $0.00313 |
| Sonnet 5 | $0.00007 | $0.00125 |
| Haiku 4.5 | $0.00004 | $0.00063 |
Grade A, and why
overcast-where scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
overcast-where
Use this skill to turn a moment into spatial evidence: a bounding box on the
target plus a verified crop. It follows the detector-proposes / VLM-verifies
pattern (open-vocab detection like OWLv2, then confirm the crop) — because chat
VLMs are unreliable at emitting raw coordinates, so a real detector draws the box
and the VLM only judges the crop. Use the broad overcast skill and
overcast/reference/verbs.md for exact flags.
Setup
see --detect needs a detection provider bound (boxes come from OWLv2, not the
brain LLM):
scripts/visual-db-uv.sh --detect # once: prints DETECT_PY (the venv python)
export DETECT_PY="$DETECT_PY"; overcast provider setup apply --preset owl-local --yes --json # persists a portable shipped: ref for detect.py + the venv python
Workflow
-
Have the moment (use
overcast-pinpoint/overcast-frame-grid): timestamp T on record REC. -
Detect the target in that frame, then materialize + verify the box:
overcast see frame://REC@T --detect "<target phrase>" --json # -> see record with detections[]
overcast crop <see-record-id> --all --class "<target phrase>" --pad 0.15 --json
overcast see <crop-path> --prompt "Does this crop show <target>? yes/no + describe" --json
The re-see of each crop is what kills false positives — open-vocab detectors
emit confident boxes for almost any phrase at low thresholds.
- Optionally sharpen the exhibit and record the finding:
overcast enhance <crop-path> --ops upscale,denoise --json
overcast finding create "<target> located at T" --ref <see-record-id> --confidence medium --json
overcast brief --export ./where.md --json
Output
Per confirmed target: the timestamp, the box (from the see --detect record),
the crop path, and the verification verdict. Cite the see detection
record.id + media.at; note the crop is the durable, memory-friendly evidence
artifact.
Caveats
Never ask the brain LLM for coordinates directly — bind a detector and verify
crops. Detector confidence is not calibrated across free-form phrases: a high
score on a rare phrase can still be wrong, so the crop re-check decides. For a
specific PERSON, use face --match instead of --detect. Boxes are per sampled
frame, not tracks.
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.
- 5d ago First seen · 65 lines · 37 tokens per session scan A 0627f32232c7
overcast-where is a skill published in the GitHub repository kdr/overcast (16 stars, last pushed 2d ago), licensed Apache-2.0. It adds 37 tokens to every session and 627 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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