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SKILL verified Apache-2.0 Self-run

Overcast Voiceprint

skill-kdr-overcast-overcast-voiceprint · by kdr

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Install

$ agentstack add skill-kdr-overcast-overcast-voiceprint

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

Security review passed
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2mo ago

Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

Preview Execution monitoring

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How agent discovery & health will work →
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About

overcast-voiceprint

Use this skill to answer "is this the same speaker, and where do they talk?" with the local voice-print DB (pyannote/wespeaker speaker embeddings — no media leaves the case, ungated windowed default). It verifies a VOICE, not a phrase. Use the broad overcast skill and overcast/reference/verbs.md for exact flags.

Prerequisites

overcast doctor --json                 # confirm uv + visual-db (pyannote) are ready
scripts/visual-db-uv.sh --voice        # install pyannote.audio (once per machine)
overcast case init --json
overcast index create voices --type voice-print --local --json

Workflow

  1. Enroll the reference speaker (and any known recordings). voice add embeds the

clip's voiced windows into the index:

overcast voice add ./known-speaker.wav --index voices --json     # the reference person
overcast voice add ./interview.m4a --index voices --json          # other recordings to search
  1. Rank WHERE the reference speaker talks inside a clip (pairwise, windowed scan).

voice match scans ` for the speaker; --diarize` upgrades to overlap-aware diarize-then-match (HF_TOKEN + accepted pyannote license, windowed fallback if ungated):

overcast voice match ./call.wav ./known-speaker.wav --json                 # where in call.wav does the sample speaker talk?
overcast voice match ./call.wav ./known-speaker.wav --diarize --min-margin 10 --json  # overlap-aware; gate on margin
  1. Rank WHICH enrolled recordings contain the reference speaker (index search):
overcast voice match ./known-speaker.wav --index voices --json   # members ranked by the speaker's presence
  1. Read the score honestly and corroborate before concluding. similarity is an

anchored-cosine 0–100 RANK score (never 0–1) plus a raw cosine; --min-margin gates best-vs-runner-up. Corroborate with content (a listen transcript) before naming anyone, then promote through triage:

overcast listen ./call.wav --json                                # what was said, to corroborate WHO
overcast finding list --state triage --json                      # a >=80 voice match auto-suggests a lead
overcast finding accept  --target  --json
overcast note "call.wav 00:38-01:12 ranks 87/100 for the known speaker (margin 22); transcript corroborates" --ref  --at 38-72 --confidence medium --json
overcast brief --export ./voiceprint.html --json

Output

For each match: the reference sample, WHERE (time windows) or WHICH (member recordings) the speaker appears, the 0–100 rank score + raw cosine + margin, and the corroborating transcript content — every claim cited to a record.id + media.at. Report a time WINDOW, not a single frame.

Caveats

Not liveness. A cloned / TTS / impersonated voice can score high, so a voice match is a lead to corroborate, never an identification — every record carries payload.caveat; surface it verbatim. The same speaker scores LOWER across languages, heavy compression, or noise, so a miss isn't proof of a different person. --diarize LABELS overlapping speakers, it does not name them. Scores are 0–100 (percent), not 0–1 — set --min-similarity/--min-margin on that scale. Leads flow through finding triage; they stay out of ask/brief until accepted.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

  • Author: kdr
  • Source: kdr/overcast
  • License: Apache-2.0
  • Homepage: https://overcast.video

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.