Getting it into your agent
There is no command for this one: it runs only inside a plugin, and the catalogue could not identify which plugin ships it. The source is linked below.
Wrote 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/ahkedia/lyra-ai/voice-capture)<a href="https://agentmods.dev/skills/ahkedia/lyra-ai/voice-capture"><img src="https://agentmods.dev/badge/skills/ahkedia/lyra-ai/voice-capture/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ahkedia/lyra-ai/voice-capture"><img src="https://agentmods.dev/badge/skills/ahkedia/lyra-ai/voice-capture.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00035 | $0.00822 |
| Opus 5 | $0.00017 | $0.00411 |
| Sonnet 5 | $0.00007 | $0.00164 |
| Haiku 4.5 | $0.00003 | $0.00082 |
Grade A, and why
voice-capture 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 12d 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.
curl -s -X POST "https://api.notion.com/v1/pages" \ How it starts
The opening of the file, as written. The whole thing — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Voice Capture — Think Out Loud, Lyra Captures It
Step 1: Transcribe with mlx-whisper
When a voice message arrives, OpenClaw saves it as a file (usually /tmp/voice_XXXX.ogg or .oga).
# Convert to wav first (mlx-whisper works best with wav)
/opt/homebrew/bin/ffmpeg -i /tmp/VOICE_FILE.ogg /tmp/voice_transcript.wav -y -loglevel quiet
# Transcribe (tiny model is fast; use small for better accuracy on Hinglish)
/Users/akashkedia/Library/Python/3.9/bin/mlx_whisper \
--model mlx-community/whisper-small-mlx \
--output-format txt \
--output-dir /tmp \
/tmp/voice_transcript.wav
# Read result
cat /tmp/voice_transcript.txt
If the file path isn't known, check recent temp files:
ls -t /tmp/*.ogg /tmp/*.oga /tmp/*.m4a /tmp/*.wav 2>/dev/null | head -3
Hinglish note: mlx-whisper handles Hindi-English code-switching well with --language hi if needed.
Step 2: Classify
Classify transcribed text into ONE of:
- Insight — a realization or lesson ("I just realized...")
- Decision — something decided ("I've decided to...", "Going with option B")
- Idea — a new concept or feature ("What if we...", "I had an idea for...")
- Question — an open question ("I keep wondering why...", "Should I...")
- Pattern — a recurring observation ("Every time I...", "I've noticed that...")
Extract:
- Title: 5-10 word summary
- Tags from: job-hunt, relocation, content, n26, sme-lending, ai, personal, abhigna
Step 3: Save to Second Brain
NOTION_KEY=$(cat ~/.config/notion/api_key)
curl -s -X POST "https://api.notion.com/v1/pages" \
-H "Authorization: Bearer $NOTION_KEY" \
-H "Notion-Version: 2025-09-03" \
-H "Content-Type: application/json" \
-d '{
"parent": {"database_id": "e4027aaf-d2ff-49e1-babf-7487725e2ef4"},
"properties": {
"Name": {"title": [{"text": {"content": "TITLE_HERE"}}]},
"Type": {"select": {"name": "TYPE_HERE"}},
"Source": {"select": {"name": "Voice"}},
"Date": {"date": {"start": "DATE_HERE"}},
"Tags": {"multi_select": [{"name": "TAG1"}]},
"Notes": {"rich_text": [{"text": {"content": "FULL_TRANSCRIPTION_HERE"}}]}
}
}'
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.
- 12d ago First seen · 79 lines · 35 tokens per session scan A 6f59ea95c66b
voice-capture is a skill published in the GitHub repository ahkedia/lyra-ai (5 stars, last pushed 23d ago), licensed MIT. It adds 35 tokens to every session and 822 once invoked, about $0.0002 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 2026-08-31.
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