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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name: transcript-fixer
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description: Corrects speech-to-text (ASR/STT) transcription errors in meeting notes, lecture recordings, interviews, and voice memos through dictionary-based rules and AI corrections. This skill should be used when users mention 'transcript', 'ASR errors', 'speech-to-text', 'STT mistakes', 'meeting notes', 'dictation', 'homophone errors', 'voice memo cleanup', or when working with .md/.txt files containing Chinese/English mixed content with obvious transcription errors.
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---
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# Transcript Fixer
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Correct speech-to-text transcription errors through dictionary-based rules, AI-powered corrections, and automatic pattern detection. Build a personalized knowledge base that learns from each correction.
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## When to Use This Skill
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Activate this skill when:
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- Correcting speech-to-text (ASR) transcription errors in meeting notes, lectures, or interviews
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- Building domain-specific correction dictionaries for repeated transcription workflows
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- Fixing Chinese/English homophone errors, technical terminology, or names
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- Collaborating with teams on shared correction knowledge bases
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- Improving transcript accuracy through iterative learning
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## Quick Start
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Initialize (first time only):
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```bash
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uv run scripts/fix_transcription.py --init
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export GLM_API_KEY="<api-key>" # Obtain from https://open.bigmodel.cn/
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```
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Correct a transcript in 3 steps:
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```bash
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# 1. Add common corrections (5-10 terms)
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uv run scripts/fix_transcription.py --add "错误词" "正确词" --domain general
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# 2. Run full correction pipeline
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uv run scripts/fix_transcription.py --input meeting.md --stage 3
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# 3. Review learned patterns after 3-5 runs
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uv run scripts/fix_transcription.py --review-learned
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```
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**Output files**:
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- `meeting_stage1.md` - Dictionary corrections applied
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- `meeting_stage2.md` - AI corrections applied (final version)
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## Example Session
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**Input transcript** (`meeting.md`):
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```
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今天我们讨论了巨升智能的最新进展。
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股价系统需要优化，目前性能不够好。
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```
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**After Stage 1** (`meeting_stage1.md`):
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```
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今天我们讨论了具身智能的最新进展。 ← "巨升"→"具身" corrected
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股价系统需要优化,目前性能不够好。 ← Unchanged (not in dictionary)
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```
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**After Stage 2** (`meeting_stage2.md`):
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```
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今天我们讨论了具身智能的最新进展。
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框架系统需要优化，目前性能不够好。 ← "股价"→"框架" corrected by AI
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```
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**Learned pattern detected:**
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```
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✓ Detected: "股价" → "框架" (confidence: 85%, count: 1)
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Run --review-learned after 2 more occurrences to approve
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```
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## Workflow Checklist
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Copy and customize this checklist for each transcript:
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```markdown
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### Transcript Correction - [FILENAME] - [DATE]
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- [ ] Validation passed: `uv run scripts/fix_transcription.py --validate`
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- [ ] GLM_API_KEY verified: `echo $GLM_API_KEY | wc -c` (should be >20)
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- [ ] Domain selected: [general/embodied_ai/finance/medical]
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- [ ] Added 5-10 domain-specific corrections to dictionary
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- [ ] Tested Stage 1 (dictionary only): Output reviewed at [FILENAME]_stage1.md
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- [ ] Stage 2 (AI) completed: Final output verified at [FILENAME]_stage2.md
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- [ ] Learned patterns reviewed: `--review-learned`
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- [ ] High-confidence suggestions approved (if any)
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- [ ] Team dictionary updated (if applicable): `--export team.json`
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```
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## Core Commands
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```bash
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# Initialize (first time only)
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uv run scripts/fix_transcription.py --init
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export GLM_API_KEY="<api-key>" # Get from https://open.bigmodel.cn/
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# Add corrections
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uv run scripts/fix_transcription.py --add "错误词" "正确词" --domain general
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# Run full pipeline (dictionary + AI corrections)
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uv run scripts/fix_transcription.py --input file.md --stage 3 --domain general
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# Review and approve learned patterns (after 3-5 runs)
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uv run scripts/fix_transcription.py --review-learned
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uv run scripts/fix_transcription.py --approve "错误" "正确"
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# Team collaboration
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uv run scripts/fix_transcription.py --export team.json --domain <domain>
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uv run scripts/fix_transcription.py --import team.json --merge
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# Validate setup
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uv run scripts/fix_transcription.py --validate
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```
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**Database**: `~/.transcript-fixer/corrections.db` (SQLite)
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**Stages**:
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- Stage 1: Dictionary corrections (instant, zero cost)
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- Stage 2: AI corrections via GLM API (1-2 min per 1000 lines)
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- Stage 3: Full pipeline (both stages)
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**Domains**: `general`, `embodied_ai`, `finance`, `medical` (prevents cross-domain conflicts)
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**Learning**: Approve patterns appearing ≥3 times with ≥80% confidence to move from expensive AI (Stage 2) to free dictionary (Stage 1).
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See `references/workflow_guide.md` for detailed workflows and `references/team_collaboration.md` for collaboration patterns.
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## Bundled Resources
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### Scripts
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- **`fix_transcription.py`** - Main CLI for all operations
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- **`examples/bulk_import.py`** - Bulk import example (runnable with `uv run scripts/examples/bulk_import.py`)
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### References
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Load as needed for detailed guidance:
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- **`workflow_guide.md`** - Step-by-step workflows, pre-flight checklist, batch processing
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- **`quick_reference.md`** - CLI/SQL/Python API quick reference
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- **`sql_queries.md`** - SQL query templates (copy-paste ready)
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- **`troubleshooting.md`** - Error resolution, validation
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- **`best_practices.md`** - Optimization, cost management
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- **`file_formats.md`** - Complete SQLite schema
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- **`installation_setup.md`** - Setup and dependencies
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- **`team_collaboration.md`** - Git workflows, merging
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- **`glm_api_setup.md`** - API key configuration
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- **`architecture.md`** - Module structure, extensibility
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- **`script_parameters.md`** - Complete CLI reference
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- **`dictionary_guide.md`** - Dictionary strategies
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## Validation and Troubleshooting
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Run validation to check system health:
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```bash
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uv run scripts/fix_transcription.py --validate
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```
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**Healthy output:**
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```
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✅ Configuration directory exists: ~/.transcript-fixer
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✅ Database valid: 4 tables found
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✅ GLM_API_KEY is set (47 chars)
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✅ All checks passed
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```
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**Error recovery:**
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1. Run validation to identify issue
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2. Check components:
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- Database: `sqlite3 ~/.transcript-fixer/corrections.db ".tables"`
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- API key: `echo $GLM_API_KEY | wc -c` (should be >20)
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- Permissions: `ls -la ~/.transcript-fixer/`
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3. Apply fix based on validation output
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4. Re-validate to confirm
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**Quick fixes:**
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- Missing database → Run `--init`
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- Missing API key → `export GLM_API_KEY="<key>"`
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- Permission errors → Check ownership with `ls -la`
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See `references/troubleshooting.md` for detailed error codes and solutions.
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