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.
npx skills add autonomous-ai/autonomous-os --skill speaker-recognizergit clone --depth 1 https://github.com/autonomous-ai/autonomous-osWrote 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.
[](https://agentmods.dev/skills/autonomous-ai/autonomous-os/speaker-recognizer)<a href="https://agentmods.dev/skills/autonomous-ai/autonomous-os/speaker-recognizer"><img src="https://agentmods.dev/badge/skills/autonomous-ai/autonomous-os/speaker-recognizer.svg" alt="Measured on agentmods" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00132 | $0.00993 |
| Opus 5 | $0.00066 | $0.00496 |
| Sonnet 5 | $0.00026 | $0.00199 |
| Haiku 4.5 | $0.00013 | $0.00099 |
Grade A, and why
speaker-recognizer scanned grade A with 1 finding 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 7d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
When in doubt → see `reference/enroll-flows.md`. All curl + error handling → `reference/api.md`. How it starts
The opening of the file, as written. The whole thing — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Speaker Recognizer
Each mic transcript is prefixed Speaker - Name: when recognized, or Unknown Speaker: [voice:voice_N] ... (audio save[d] at <path>...) otherwise. The audio path is the WAV of whoever spoke this turn — use it (with paths from prior same-tag turns when needed) to enroll on POST /speaker/enroll.
Self-enrollment only — never enroll one person's voice under another person's name.
Decision matrix — pick ONE action per turn
| Signals in current turn | Prior same-tag turns? | Action |
|---|---|---|
Unknown Speaker: + path + name + ≥25 words |
— | Enroll now with current path only. |
Unknown Speaker: + path + name + <25 words |
≥1 prior path same [voice:N] |
Enroll now with all same-tag paths (oldest→newest). |
Unknown Speaker: + path + name + <25 words |
none | Ask one follow-up: "say your name + ~25–30 words". |
Unknown Speaker: + path + NO name + <25 words |
none | Ask one follow-up. |
Unknown Speaker: + path + NO name |
≥1 prior path same [voice:N] (still no name) |
Reply with a SHORT ack ("Mm, nghe rồi" / "Got it"). NEVER NO_REPLY. Don't re-ask. |
Speaker - <Name>: |
— | Already identified — skill not needed. |
| "who do you know?" / "list voices" | — | GET /speaker/list. |
| "forget my voice" / "remove Alex" | — | POST /speaker/remove. |
| Telegram voice note + intro | — | Convert to WAV + enroll with Telegram fields. |
| Telegram voice note + "who is this?" | — | POST /speaker/recognize. |
When in doubt → see reference/enroll-flows.md. All curl + error handling → reference/api.md.
Quick enroll (mic)
curl -s -X POST http://127.0.0.1:5001/speaker/enroll \
-H "Content-Type: application/json" \
-d '{"name": "darren", "wav_paths": ["<path1>", "<path2>"]}'
Confirm AFTER the API returns ok: "Nice to meet you, !".
Hard rules
- Self-enrollment only — "this is my friend Bob" → refuse politely; Bob must speak himself.
- Lowercase normalized name — same
nameasface-enrollfor the same person (/root/local/users/<name>/is shared). - Minimum voice for one-turn enroll: ~25 words (aim 25–30) OR combine with prior same-tag turns to ~5–10s total.
- Cluster claim is automatic — pass any path inside
voice_<N>/and the server pulls every sibling WAV. One path is enough. - Two-turn path mapping —
<pathA>= turn BEFORE follow-up,<pathB>= turn AFTER. Never swap. - Telegram audio must be 16 kHz mono WAV before enroll — convert with
ffmpeg -ar 16000 -ac 1; same folder as source. Skip if already.wav. /speaker/identity(not re-enroll) when only linking Telegram info to an existing mic profile.- Don't spam "who are you?" — at most once per cluster, and include the "25–30 words" guidance in the same message.
- Never go silent on Unknown Speaker fragments — when no name and you've already asked, emit a short ack. NO_REPLY is forbidden.
- Confirm every enroll AFTER the API returns ok.
- Don't narrate technical details — no "base64", "ffmpeg", "POST /speaker/enroll".
- Never write files directly — always use the HTTP API.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 7d ago First seen · 53 lines · 132 tokens per session scan A 533bde91aa6c
speaker-recognizer is a skill published in the GitHub repository autonomous-ai/autonomous-os (277 stars, last pushed 2d ago), licensed Apache-2.0. It adds 132 tokens to every session and 993 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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