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