--- name: overcast-copycat-sweep description: >- 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. --- # overcast-copycat-sweep Use this skill when the task is to find copies, re-uploads, or reskins of a creator's original media (video theft / freebooting) and build an evidence-backed report. Use the broad `overcast` skill and `overcast/reference/verbs.md` for exact flags. Escalate tier by tier — never capture what metadata already rules out. ## Workflow 1. Fingerprint the original (once per case). Reskins defeat exact hashes, so fingerprint three ways — distinctive frames, the creator's face, and the transcript: ```bash overcast doctor --sources --json overcast case init --json overcast case setup --name copycat-sweep --target "" --source "x:video:" --yes --no-index --json overcast watch ./original.mp4 --json # content + transcript into case memory overcast index create originals --type image-ransac --local --json overcast image add ./title-card.png --index --json # + diagrams, key frames ``` 2. Sweep sources for candidates published AFTER the original — media-targeted (`x:video:`) queries with topic keywords, not exact titles: ```bash overcast source add 'youtube:search:' --json overcast scan --since --limit 20 --json ``` 3. Triage on scan metadata alone (no downloads): keep hits whose `published` postdates the original, whose `duration` is close to it, or whose `title`/`snippet` echoes it; carry `author` and `views` into the report. 4. Escalate survivors — capture, then match every fingerprint layer: ```bash overcast capture --json overcast image match --index --draw --json # frames survive reskins/subtitles; --draw writes match-overlay proof overcast face --match ./creator.jpg --json # the face survives re-branding overcast listen --json # verbatim transcript = strongest signal overcast ask "does this captured video repeat the original's content? cite moments" --json ``` Pass `--draw` on `image match` so each matched frame writes a RANSAC overlay (original ↔ suspect keypoints). Cite the `image` match record as the finding's `--ref` in step 5 — the brief embeds that overlay in the finding card as visual proof. Each `image match` / `face --match` / `listen`-on-a-match already **auto-suggests** a finding (image RANSAC ≥1 inlier, face ≥75%): run `overcast finding list --state triage --json` to see the leads, then `finding accept ` (usually enough — the lead is already there) or `finding dismiss `. Step 5's manual `finding create --ref ` stays valid for a richer because-clause (dedup suppresses the duplicate). **Local mode (no external source).** The skill works entirely on local files: skip steps 2–3 and run `image match` / `face --match` / `listen` directly on candidate videos already on disk (or captured earlier). This is how you compare a suspected rip you already have against the original, and how the pipeline is tested offline (fingerprint an original, confirm a reskinned copy, reject an unrelated clip) — no scan, no API. `scan --local` also sweeps the case's own media/indexes when no source is enabled. 5. Record verdicts and report; keep a standing watch. One `finding` per confirmed copycat stating the because-clause (which layers matched, with scores), and ALWAYS one narrative note tagged `tldr` — even when the sweep comes up clean ("checked N sources, M candidates triaged, no copycats found") — because the brief's TL;DR / sources-checked / matches header is derived from exactly these records: ```bash overcast finding create "copycat: re-uploaded by @ ( views) — image frames 3x (best 94 inliers), face 87/100" --ref --confidence high --json overcast note "checked x + youtube ( hits); candidates escalated; confirmed: @ ..." --tag tldr --json overcast target close --as answered --note "copycats found + reported" --json # once a line resolves # Wait for the note result before exporting, so the TL;DR is included. overcast brief --export ./copycats.html --json # short by default (verdict-led); add --full for the frame-by-frame dump overcast monitor --every 1d --json ``` Point the finding's `--ref` at the `image match` record (not the raw scan hit) so its match-draw overlay rides into the finding card as visual proof. ## Output For each confirmed copycat return: post URL, `author`, `views`, `published`, which layers matched (image frames / face / transcript), the strongest `record.id` + `media.at` citations, and the exported brief path. The exported brief opens with the TL;DR narrative (from the `tldr`-tagged note), the sources-checked rollup, and the matches & findings verdicts; a clean sweep must still say so explicitly ("checked, found none"). ## Caveats Copycats retitle and re-caption, so search topic keywords and confirm with the visual/transcript layers: burned-in subtitles and translated dubs defeat text matching but not `image` frame matching or `face --match`. Face similarity is 0–100 (percent), not 0–1; `image match` reports a RANSAC inlier count (unbounded integer) plus an inlier ratio (0–1) — there is no 0–100 image similarity. A repost/quote is a share, not a rip — confirm the account re-uploaded the media natively (check `x:video:from:`). Apify-backed sources bill per result — prefer few, broad queries over many narrow ones. Keyword overlap is NOT a match: accounts pump many videos that share your topic words, so text triage only shortlists — the frame/face/transcript layers decide. Do not trust an `image match` inlier count alone; a high count on a degenerate homography is the main false positive. `image match` gates on planar-projection validity by default (`--draw` writes the overlay so you can eyeball coherent correspondences vs lines collapsing to a point). Call a video a confirmed rip only when the gated match survives AND the transcript/face agree.