Release v1.8.0: Add transcript-fixer skill
## 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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daymade
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transcript-fixer/references/sql_queries.md
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transcript-fixer/references/sql_queries.md
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# SQL Query Reference
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Database location: `~/.transcript-fixer/corrections.db`
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## Basic Operations
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### Add Corrections
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```sql
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-- Add a correction
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INSERT INTO corrections (from_text, to_text, domain, source)
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VALUES ('巨升智能', '具身智能', 'embodied_ai', 'manual');
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INSERT INTO corrections (from_text, to_text, domain, source)
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VALUES ('奇迹创坛', '奇绩创坛', 'general', 'manual');
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```
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### View Corrections
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```sql
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-- View all active corrections
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SELECT from_text, to_text, domain, source, usage_count
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FROM active_corrections
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ORDER BY domain, from_text;
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-- View corrections for specific domain
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SELECT from_text, to_text, usage_count, added_at
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FROM active_corrections
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WHERE domain = 'embodied_ai';
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```
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## Context Rules
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### Add Context-Aware Rules
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```sql
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-- Add regex-based context rule
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INSERT INTO context_rules (pattern, replacement, description, priority)
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VALUES ('巨升方向', '具身方向', '巨升→具身', 10);
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INSERT INTO context_rules (pattern, replacement, description, priority)
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VALUES ('近距离的去看', '近距离地去看', '的→地 副词修饰', 5);
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```
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### View Rules
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```sql
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-- View all active context rules (ordered by priority)
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SELECT pattern, replacement, description, priority
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FROM context_rules
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WHERE is_active = 1
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ORDER BY priority DESC;
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```
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## Statistics
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```sql
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-- View correction statistics by domain
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SELECT * FROM correction_statistics;
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-- Count corrections by source
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SELECT source, COUNT(*) as count, SUM(usage_count) as total_usage
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FROM corrections
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WHERE is_active = 1
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GROUP BY source;
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-- Most frequently used corrections
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SELECT from_text, to_text, domain, usage_count, last_used
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FROM corrections
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WHERE is_active = 1 AND usage_count > 0
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ORDER BY usage_count DESC
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LIMIT 10;
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```
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## Learning and Suggestions
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### View Suggestions
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```sql
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-- View pending suggestions
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SELECT * FROM pending_suggestions;
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-- View high-confidence suggestions
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SELECT from_text, to_text, domain, frequency, confidence
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FROM learned_suggestions
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WHERE status = 'pending' AND confidence >= 0.8
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ORDER BY confidence DESC, frequency DESC;
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```
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### Approve Suggestions
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```sql
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-- Insert into corrections
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INSERT INTO corrections (from_text, to_text, domain, source, confidence)
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SELECT from_text, to_text, domain, 'learned', confidence
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FROM learned_suggestions
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WHERE id = 1;
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-- Mark as approved
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UPDATE learned_suggestions
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SET status = 'approved', reviewed_at = CURRENT_TIMESTAMP
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WHERE id = 1;
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```
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## History and Audit
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```sql
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-- View recent correction runs
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SELECT filename, domain, stage1_changes, stage2_changes, run_timestamp
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FROM correction_history
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ORDER BY run_timestamp DESC
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LIMIT 10;
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-- View detailed changes for a specific run
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SELECT ch.line_number, ch.from_text, ch.to_text, ch.rule_type
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FROM correction_changes ch
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JOIN correction_history h ON ch.history_id = h.id
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WHERE h.filename = 'meeting.md'
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ORDER BY ch.line_number;
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-- Calculate success rate
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SELECT
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COUNT(*) as total_runs,
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SUM(CASE WHEN success = 1 THEN 1 ELSE 0 END) as successful,
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ROUND(100.0 * SUM(CASE WHEN success = 1 THEN 1 ELSE 0 END) / COUNT(*), 2) as success_rate
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FROM correction_history;
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```
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## Maintenance
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```sql
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-- Deactivate (soft delete) a correction
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UPDATE corrections
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SET is_active = 0
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WHERE from_text = '错误词' AND domain = 'general';
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-- Reactivate a correction
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UPDATE corrections
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SET is_active = 1
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WHERE from_text = '错误词' AND domain = 'general';
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-- Update correction confidence
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UPDATE corrections
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SET confidence = 0.95
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WHERE from_text = '巨升' AND to_text = '具身';
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-- Delete old history (older than 90 days)
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DELETE FROM correction_history
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WHERE run_timestamp < datetime('now', '-90 days');
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-- Reclaim space
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VACUUM;
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```
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## System Configuration
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```sql
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-- View system configuration
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SELECT key, value, description FROM system_config;
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-- Update configuration
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UPDATE system_config
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SET value = '5'
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WHERE key = 'learning_frequency_threshold';
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-- Check schema version
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SELECT value FROM system_config WHERE key = 'schema_version';
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```
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## Export
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```sql
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-- Export corrections as CSV
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.mode csv
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.headers on
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.output corrections_export.csv
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SELECT from_text, to_text, domain, source, confidence, usage_count, added_at
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FROM active_corrections;
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.output stdout
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```
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For JSON export, use Python script with `service.export_corrections()` instead.
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## See Also
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- `references/file_formats.md` - Complete database schema documentation
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- `references/quick_reference.md` - CLI command quick reference
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- `SKILL.md` - Main user documentation
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