overcast-connect-the-dots

name	overcast-connect-the-dots
description	String the board — build the case knowledge graph, read its hubs (shared media, targets, device fingerprints, places, typed entities), focus the neighborhood around a node, optionally run the opt-in LLM entity pass, and promote real connections into evidence via findings.

overcast-connect-the-dots

Use this skill to "connect the dots": render the whole case as one relational graph and find the links that tie records, people, devices, places, and entities together. This is the relational/entity board; the visual crop corkboard is overcast-crime-board (faces + object crops with red string). Use the broad overcast skill and overcast/reference/verbs.md for exact flags.

Workflow

1. Build the graph and read the hubs. graph emits ONE self-contained interactive HTML force-graph — records, shared-media hubs, targets, accepted/open findings, cluster people, device fingerprints, places, and regex-harvested typed entities (email / phone / @handle / url / domain / hashtag + exif serial + scan identity lifts) — with every edge carrying its provenance record id:

overcast doctor --json
overcast case init --json
overcast graph --no-open --json           # build; inspect the payload's nodes/edges/hubs
overcast graph --json                     # (also opens the viewer) hand-rolled canvas, no CDN/egress

2. Focus a node's 2-hop neighborhood — a target, a finding, a record id, a media ref, or an entity's text. The anchor is never trimmed; --limit drops lowest-degree leaf entities first, --since is capture-time-aware (an in-window finding pulls its out-of-window source record back in):

overcast graph --focus <target-id> --json          # everything around a line of investigation
overcast graph --focus "[phone]" --json       # everything touching an entity value
overcast graph --since 7d --limit 250 --json        # recent, trimmed

3. (Optional) Run the opt-in LLM entity/relation pass. --extract sends evidence TEXT to your brain LLM (BYO, text-only), caches to .overcast/graph/extract.jsonl (delete the file to re-extract), and marks every result leads-not-proof (payload.caveat). It co-filters with --since:

overcast graph --extract --json                     # adds LLM-inferred entities/relations (cached)
overcast graph --extract --since 7d --focus <target-id> --json

4. Promote real connections into evidence. Graph edges and --extract output are NOT evidence on their own — a link you confirm becomes a finding stamped onto a line of investigation, which is what enters ask/brief:

overcast finding create "@handle in scan REC and phone in property REC resolve to the same person" --ref <record-id> --target <target-id> --confidence medium --json
overcast finding list --state triage --json         # if a match verb auto-suggested the link
overcast finding accept <id> --target <target-id> --json
overcast note "graph: <n> shared-media hubs; strongest cross-link = <a> to <b> via <record-id>" --tag tldr --json
overcast brief --export ./connect-the-dots.html --json

Output

The graph HTML plus a written read of its structure: the shared-media hubs, the target-to-evidence threads, device-fingerprint memberships, and the typed entities that recur across records — each asserted connection cited to the edge's provenance record.id, and each CONFIRMED connection promoted to a finding.

Caveats

graph is operational — it (and --extract output) stays OUT of ask/brief evidence; only findings you accept carry a link into the narrative. --extract is a brain-LLM inference (BYO): treat its entities/relations as leads to verify against the underlying records, never proof — every extracted item carries payload.caveat. Regex-harvested entities over-match (a string that looks like a handle may not be one); confirm before drawing the string. --limit trims leaves to keep the canvas legible, so a very large case may hide low-degree entities — raise it or --focus.