overcast-visual-target-search

overcast-visual-target-search is a skill for Claude Code from kdr/overcast. It costs 33 tokens per session (718 once invoked), scanned A, original, Apache-2.0.

A visual-search workflow for finding people, logos, objects, landmarks, or other references in local videos, images, and captured case media, with timestamps and confidence information.

In plain words
What is it for?
Use it to match a person from a reference image, detect open-ended objects, review findings, extract crops, and export a timestamped visual-search brief.
Why use it?
It turns a large collection of media into searchable evidence and supports reviewing, accepting, or dismissing possible matches.

Skill for Claude Code

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

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

Good fit Use it to match a person from a reference image, detect open-ended objects, review findings, extract crops, and export a timestamped visual-search brief.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/kdr/overcast/overcast-visual-target-search
Install

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.

Any agent
npx skills add kdr/overcast --skill overcast-visual-target-search
Clone the repo
git clone --depth 1 https://github.com/kdr/overcast

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-visual-target-search

README.md
[![agentmods](https://agentmods.dev/badge/skills/kdr/overcast/overcast-visual-target-search/github.svg)](https://agentmods.dev/skills/kdr/overcast/overcast-visual-target-search)
Your own site
<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.

agentmods 80×15 button for overcast-visual-target-search

Your own site · 80×15
<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>
Per session 33 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 718 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 $0.00033 $0.00718
Opus 5 $0.00016 $0.00359
Sonnet 5 $0.00007 $0.00144
Haiku 4.5 $0.00003 $0.00072

Measured 9d ago against content hash b430ffab1c5b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

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.

skills/overcast-visual-target-search/SKILL.md · 66 lines

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.

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 triageaccept/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.

Read the full file on GitHub · 66 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. 9d ago First seen · 66 lines · 33 tokens per session scan A b430ffab1c5b

Subscribe to this mod's changes

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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