AI Agent Workflow Collection

Give any agent senses (video/audio/image understanding) and OSINT reach (search/capture/monitor) organized around an investigation case. Use when the user wants to analyze a video/audio/image, scan or monitor sources for a target, or ask/brief over accumulated findings. Drives the overcast CLI (built on pi + the tinycloud/Cloudglue perception backend); see reference/verbs.md for the full verb surface.

Save media into global archive buckets reusable across cases, stand up bucket indexes (face/semantic/image/audio/ask) with the archive setup wizard, and run "does this match anything I've archived?" checks from inside any case.

Map a target's internet-exposed hosts and services with the shodan source, capture host reports, brief the exposure, and optionally stand up a monitor for newly exposed services.

Hunt re-uploads and reskins of original video content across X / YouTube / TikTok — escalate from cheap metadata triage to frame/face/transcript matching and produce citable copycat findings.

Turn a case into a visual evidence board — materialize face and object crops, link the same person across clips, connect themes, and render the corkboard as a CSI brief plus a live monitor wall.

Google-dork a target domain for exposed documents, directory listings, login portals, and misconfigurations using the dork source (real Google operators), then capture the result pages and produce a cited exposure brief.

Make unreadable footage legible — denoise/upscale a marked moment, re-run OCR and detection on the enhanced output, and record what was recovered with honest provenance (interpolation is a lead, not proof).

Localize the exact instant of a one-way state change (light on, door opens, poster removed) by binary-searching frames — ~log2(N) VLM calls.

Triage a whole clip in one VLM call — tile sampled frames into a labeled contact sheet, ask which cells show the target, then zoom in.

Install and configure overcast for this harness: install the CLI, verify the system ffmpeg, and configure reusable provider profiles. Use once per machine/profile before driving the overcast skill.

Build a persistent local face database out of case media — the mugshot book — then run a suspect photo through it to identify who they are and where else they appear, with cited similarity scores.

Analyze screen recordings, product demos, customer support videos, and audio notes into actionable, cited bug reports for coding agents.

Pinpoint WHEN a specific thing happens in a video — funnel from cheap coarse candidates (shots / CLIP / grid) to a frame-verified time window.

Find the interval a person or object is present — anchor one appearance, then sweep outward with face-match / detect until it drops off both sides.

Trace a suspicious or viral clip back to its earliest appearance and originator — reverse-image-search distinctive frames, sweep sources with no recency floor, and return a cited origin verdict.

Scan or monitor public sources for a target, capture relevant hits, sense media, and produce cited investigation briefs.

Work out where a photo or clip was taken — check embedded EXIF/GPS first, then pull signage, landmarks, and terrain clues, reverse-image-search the strongest ones, and corroborate to a location with cited evidence.

Create small, installable agent skills that wrap focused Overcast workflows. Use when the user asks to make an Overcast skill for a specific investigation, media analysis, recon, monitoring, or case-memory workflow.

Run a standing surveillance watch on public sources for a target — auto-sense new media, auto-suggest matches into a triage queue, and keep a live control-room wall — so new evidence surfaces itself over time.

Reconstruct a single event from multiple clips — sense each one, cross-anchor shared moments, surface corroborations and contradictions, and produce one cited chronological brief.

Find a person, logo, object, landmark, or visual reference across local clips or captured media with timestamped Overcast evidence.

Locate WHERE in a frame a target is — open-vocab detect, crop the box, then VLM-verify the crop so hallucinated boxes don't survive.

Work a recorded call or audio clip already in the case — separate the speakers, read the background scene for location clues, isolate and re-transcribe voices, and correlate content across recordings.