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## New Skill: transcript-fixer v1.0.0 Correct speech-to-text (ASR/STT) transcription errors through dictionary-based rules and AI-powered corrections with automatic pattern learning. **Features:** - Two-stage correction pipeline (dictionary + AI) - Automatic pattern detection and learning - Domain-specific dictionaries (general, embodied_ai, finance, medical) - SQLite-based correction repository - Team collaboration with import/export - GLM API integration for AI corrections - Cost optimization through dictionary promotion **Use cases:** - Correcting meeting notes, lecture recordings, or interview transcripts - Fixing Chinese/English homophone errors and technical terminology - Building domain-specific correction dictionaries - Improving transcript accuracy through iterative learning **Documentation:** - Complete workflow guides in references/ - SQL query templates - Troubleshooting guide - Team collaboration patterns - API setup instructions **Marketplace updates:** - Updated marketplace to v1.8.0 - Added transcript-fixer plugin (category: productivity) - Updated README.md with skill description and use cases - Updated CLAUDE.md with skill listing and counts 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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Setup Guide
Complete installation and configuration guide for transcript-fixer.
Table of Contents
Installation
Dependencies
Install required dependencies using uv:
uv pip install -r requirements.txt
Or sync the project environment:
uv sync
Required packages:
anthropic- For Claude API integration (future)requests- For GLM API callsdifflib- Standard library for diff generation
Database Initialization
Initialize the SQLite database (first time only):
uv run scripts/fix_transcription.py --init
This creates ~/.transcript-fixer/corrections.db with the complete schema:
- 8 tables (corrections, context_rules, history, suggestions, etc.)
- 3 views (active_corrections, pending_suggestions, statistics)
- ACID transactions enabled
- Automatic backups before migrations
See file_formats.md for complete database schema.
API Configuration
GLM API Key (Required for Stage 2)
Stage 2 AI corrections require a GLM API key.
- Obtain API key: Visit https://open.bigmodel.cn/
- Register for an account
- Generate an API key from the dashboard
- Set environment variable:
export GLM_API_KEY="your-api-key-here"
Persistence: Add to shell profile for permanent access:
# For bash
echo 'export GLM_API_KEY="your-key"' >> ~/.bashrc
source ~/.bashrc
# For zsh
echo 'export GLM_API_KEY="your-key"' >> ~/.zshrc
source ~/.zshrc
Verify Configuration
Run validation to check setup:
uv run scripts/fix_transcription.py --validate
Expected output:
🔍 Validating transcript-fixer configuration...
✅ Configuration directory exists: ~/.transcript-fixer
✅ Database valid: 0 corrections
✅ All 8 tables present
✅ GLM_API_KEY is set
============================================================
✅ All checks passed! Configuration is valid.
============================================================
Environment Setup
Python Environment
Required: Python 3.8+
Recommended: Use uv for all Python operations:
# Never use system python directly
uv run scripts/fix_transcription.py # ✅ Correct
# Don't use system python
python scripts/fix_transcription.py # ❌ Wrong
Directory Structure
After initialization, the directory structure is:
~/.transcript-fixer/
├── corrections.db # SQLite database
├── corrections.YYYYMMDD.bak # Automatic backups
└── (migration artifacts)
Important: The .db file should NOT be committed to Git. Export corrections to JSON for version control instead.
Next Steps
After setup:
- Add initial corrections (5-10 terms)
- Run first correction on a test file
- Review learned suggestions after 3-5 runs
- Build domain-specific dictionaries
See workflow_guide.md for detailed usage instructions.