tacit

Harness your tacit knowledge into AI agent context — on-device, always-on transcription.

curl -fsSL https://raw.githubusercontent.com/sangmin7648/tacit/main/install.sh | sh
tacit setup
tacit listen   # start capturing

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Spoken ideas disappear. tacit transcribes them on-device, classifies them with Ollama, and surfaces them as live context in any AI conversation — automatically.

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How it works

speak → capture → VAD → STT → classify → store → retrieve

1. Capture — Records microphone and system audio simultaneously in real time
2. Process — Voice Activity Detection filters silence; Whisper transcribes speech on-device
3. Classify — Ollama extracts title, category, keywords, and summary from the transcript
4. Store — Saves a structured Markdown entry to ~/.tacit/memory/<category>/
5. Retrieve — /tacit.knowledge searches your knowledge base from inside any Claude conversation

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Features

• Fully automatic — speak naturally; tacit handles transcription, classification, and storage without any manual steps
• On-device STT — powered by whisper.cpp; no audio ever leaves your machine
• Dual audio sources — captures microphone and system audio simultaneously
• Language-agnostic — Whisper auto-detects language; works with Korean, English, or mixed conversation
• AI-native retrieval — first-class Claude Code CLI skill integration for in-conversation search

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Use with AI

After tacit setup, two skills are available inside Claude Code conversations:

/tacit.knowledge — search your spoken history

/tacit.knowledge summarize the search ranking discussion from earlier
/tacit.knowledge find the API design we talked about last week
/tacit.knowledge any ideas about the onboarding flow from last month?

/tacit.memorize — save the current conversation

/tacit.memorize
/tacit.memorize skill development   # optional hint guides categorization

Analyzes the current Claude conversation thread and saves it as a structured knowledge entry — automatically available to future /tacit.knowledge queries.

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Configuration

~/.tacit/config.yaml — all fields are optional.

Field	Type	Default	Description
whisper_model	string	base	Whisper model size: tiny, base, small, medium, large. Larger = more accurate, slower.
min_speech_duration	duration	8s	Minimum segment length to process. Shorter segments are skipped.
silence_duration	duration	1500ms	Duration of silence required to end a speech segment.
speech_threshold	float	0.5	VAD confidence threshold (0–1). Higher = more conservative.
energy_threshold	int	200	Audio energy gate. Frames below this value are rejected before VAD.

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Architecture

graph LR
    MIC[Microphone\n16kHz mono] --> CAP[Capture\nmalgo]
    SYS[System Audio\nScreenCaptureKit] --> CAP
    CAP --> VAD[VAD\nten-vad / Silero]
    VAD --> BUF[Segment Buffer]
    BUF --> STT[STT\nwhisper.cpp]
    STT --> CLS[Classify\nOllama]
    CLS --> KB[Knowledge Base\n~/.tacit/memory/]

Loading

Storage format — each entry is a plain Markdown file:

---
title: "Search ranking discussion"
category: "dev"
created_at: "2026-04-14T15:30:45+09:00"
keywords: ["search", "ranking", "BM25", "lexical", "recall"]
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One-sentence AI-generated summary.

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Raw transcribed text from speech.

Entries are stored under ~/.tacit/memory/<category>/YYYYMMDD-HHMMSS.md — plain files, no proprietary database, fully editable.

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Requirements

• macOS (Apple Silicon)
• Claude Code CLI

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Build from source

Requirements: Go 1.23+, CMake, macOS

git clone --recursive https://github.com/sangmin7648/tacit.git
cd tacit
make build
make install   # installs to ~/.local/bin/tacit

If ~/.local/bin is not in your PATH:

export PATH="$HOME/.local/bin:$PATH"

Run the end-to-end test to verify the full pipeline:

make e2e-test

Contributing

Issues and pull requests are welcome. Please open an issue first for significant changes.

make test       # run unit tests
make e2e-test   # build + process test audio through full pipeline

Note: Do not run go build ./... directly — pkg/stt uses CGo against whisper.cpp and requires make build to compile first.