Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub Β· AI
Percept Speaker ID
Identifies and tracks speakers in multi-person conversations, mapping speaker labels to names and managing voice command authorization levels.
skill openclawclawhub Free
Identifies and tracks speakers in multi-person conversations, mapping speaker labels to names and managing voice command authorization levels.
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
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Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- jarvis563
- Source page
- View original listing
- Canonical URL
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Documentation
percept-speaker-id
Speaker identification and management for multi-person conversations.
What it does
Tracks who said what in conversations. Maps anonymous speaker labels (SPEAKER_0, SPEAKER_1) to real names, maintains speaker profiles, and gates voice command authorization.
When to use
User asks "who said that?" or wants speaker-attributed transcripts User wants to configure which people can trigger voice commands Agent needs to know who is speaking in a multi-person conversation
Requirements
percept-listen skill installed and running Omi pendant (provides is_user flag for primary speaker)
How it works
Omi sends transcript segments with speaker labels (SPEAKER_0, SPEAKER_1, etc.) Percept resolves labels to names using the speakers registry is_user flag from Omi identifies the pendant wearer as the primary speaker Speaker profiles track first/last seen timestamps and authorization status
Speaker registry
Located at percept/data/speakers.json: { "SPEAKER_00": { "name": "David", "is_owner": true, "approved": true }, "SPEAKER_01": { "name": "Rob", "is_owner": false, "approved": true } } Manage via Percept dashboard (port 8960) β Settings β Speakers.
Authorization levels
Owner (is_owner: true): Full command access, always authorized Approved (approved: true): Can trigger wake word commands Unknown: Logged only, commands not executed
Future: Voice embeddings
Planned: pyannote speaker diarization with 192-dim voice embeddings for automatic speaker recognition via cosine similarity. Currently speaker mapping is manual.
Links
GitHub: https://github.com/GetPercept/percept
Category context
Agent frameworks, memory systems, reasoning layers, and model-native orchestration.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
1 Docs
- SKILL.md