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 GetPercept/percept --skill percept-listengit clone --depth 1 https://github.com/GetPercept/perceptWrote 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/getpercept/percept/percept-listen)<a href="https://agentmods.dev/skills/getpercept/percept/percept-listen"><img src="https://agentmods.dev/badge/skills/getpercept/percept/percept-listen.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 | $0.00000 | $0.00437 |
| Opus 5 | $0.00000 | $0.00218 |
| Sonnet 5 | $0.00000 | $0.00087 |
| Haiku 4.5 | $0.00000 | $0.00044 |
Grade A, and why
percept-listen scanned grade A with 0 findings 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
percept-listen
Ambient audio capture and transcription for OpenClaw agents via wearable devices.
What it does
Connects a wearable microphone (Omi pendant or Apple Watch) to your OpenClaw agent. Audio is transcribed locally and streamed as structured conversation data — speaker-tagged, timestamped, and searchable.
When to use
- User wants their agent to hear ambient conversations
- User asks to "start listening" or "turn on the mic"
- User mentions Omi pendant, wearable, or ambient audio
Requirements
- Percept server running locally (
pip install getpercept && percept start) - Omi pendant paired via phone, OR Apple Watch with Percept app
- Webhook configured: Omi app → Settings → Webhooks →
https://<your-tunnel>/webhook/transcript
Setup
# Install Percept
pip install getpercept
# Start the receiver (default port 8900)
percept start
# Or run directly
PYTHONPATH=. python -m uvicorn src.receiver:app --host 0.0.0.0 --port 8900
Configure a tunnel (Cloudflare, ngrok, Tailscale) so Omi can reach your local server.
How it works
- Omi pendant captures audio → phone does STT → sends transcript segments via webhook
- Percept receiver processes segments into conversations
- Conversations are stored in local SQLite with FTS5 full-text search
- All processing stays local — no audio leaves your machine
Data locations
- SQLite DB:
percept/data/percept.db - Live transcript:
/tmp/percept-live.txt - Conversations:
percept/data/conversations/
Configuration
Wake words, speaker names, and all settings are managed via the Percept dashboard (port 8960) or directly in the SQLite database.
Links
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 · 57 lines · 0 tokens per session scan A 5832ed66afe3
percept-listen is a skill published in the GitHub repository GetPercept/percept (8 stars, last pushed 5mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 437 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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