Two-phase correction pipeline: deterministic dictionary rules (instant, free) followed by AI-powered error detection. Corrections accumulate in ~/.transcript-fixer/corrections.db, improving accuracy over time.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiontranscript-fixerExecute the skills CLI command in your project's root directory to begin installation:
Fetches transcript-fixer from daymade/claude-code-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate transcript-fixer. Access via /transcript-fixer in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Two-phase correction pipeline: deterministic dictionary rules (instant, free) followed by AI-powered error detection. Corrections accumulate in ~/.transcript-fixer/corrections.db, improving accuracy over time.
All scripts use PEP 723 inline metadata — uv run auto-installs dependencies. Requires uv (install guide).
# First time: Initialize database
uv run scripts/fix_transcription.py --init
# Phase 1: Dictionary corrections (instant, free)
uv run scripts/fix_transcription.py --input meeting.md --stage 1
After Stage 1, Claude reads the output and fixes remaining ASR errors natively (no API key needed):
See references/example_session.md for a concrete input/output walkthrough.
Alternative: API batch processing (for automation without Claude Code):
export GLM_API_KEY="<api-key>" # From https://open.bigmodel.cn/
uv run scripts/fix_transcript_enhanced.py input.md --output ./corrected
Two-phase pipeline with persistent learning:
uv run scripts/fix_transcription.py --init--add "错误词" "正确词" --domain <domain>--input file.md --stage 1 (instant, free)--stage 3 with GLM_API_KEY for API mode--add "错误词" "正确词" after each session--review-learned and --approve high-confidence suggestionsDomains: general, embodied_ai, finance, medical, or custom (e.g., 火星加速器)
Learning: Patterns appearing ≥3 times at ≥80% confidence auto-promote from AI to dictionary
After fixing, always save reusable corrections to dictionary. This is the skill's core value — see references/iteration_workflow.md for the complete checklist.
Adding wrong dictionary rules silently corrupts future transcripts. Read references/false_positive_guide.md before adding any correction rule, especially for short words (≤2 chars) or common Chinese words that appear correctly in normal text.
When running inside Claude Code, use Claude's own language understanding for Phase 2:
--input file.md --stage 1uv run scripts/generate_word_diff.py original.md corrected.md diff.html\n\n at logical topic transitionsUse GLM_API_KEY + Stage 3 for batch processing, standalone usage without Claude Code, or reproducible automated processing.
When the script outputs [CLAUDE_FALLBACK] (GLM API error), switch to native mode automatically.
Timestamp repair:
uv run scripts/fix_transcript_timestamps.py meeting.txt --in-place
Split transcript into sections (rebase each to 00:00:00):
uv run scripts/split_transcript_sections.py meeting.txt \
--first-section-name "课前聊天" \
--section "正式上课::好，无缝切换嘛。" \
--rebase-to-zero
Word-level diff (recommended for reviewing corrections):
uv run scripts/generate_word_diff.py original.md corrected.md output.html
*_stage1.md — Dictionary corrections applied*_corrected.txt — Final version (native mode) or *_stage2.md (API mode)*_对比.html — Visual diff (open in browser)Read references/database_schema.md before any database operations.
sqlite3 ~/.transcript-fixer/corrections.db "SELECT * FROM active_corrections;"
sqlite3 ~/.transcript-fixer/corrections.db "SELECT value FROM system_config WHERE key='schema_version';"
| Stage | Description | Speed | Cost |
|---|---|---|---|
| 1 | Dictionary only | Instant | Free |
| 1 + Native | Dictionary + Claude AI (default) | ~1min | Free |
| 3 | Dictionary + API AI + diff report | ~10s | API calls |
Scripts:
fix_transcription.py — Core CLI (dictionary, add, audit, learning)fix_transcript_enhanced.py — Enhanced wrapper for interactive usefix_transcript_timestamps.py — Timestamp normalization and repairgenerate_word_diff.py — Word-level diff HTML generationsplit_transcript_sections.py — Split transcript by marker phrasesReferences (load as needed):
false_positive_guide.md (read before adding rules), database_schema.md (read before DB ops)iteration_workflow.md, workflow_guide.md, example_session.mdquick_reference.md, script_parameters.mddictionary_guide.md, sql_queries.md, architecture.md, best_practices.mdtroubleshooting.md, installation_setup.md, glm_api_setup.md, team_collaboration.mduv run scripts/fix_transcription.py --validate checks setup health. See references/troubleshooting.md for detailed resolution.
After correcting a transcript, if the content is from a meeting, lecture, or interview, suggest structuring it:
Transcript corrected: [N] errors fixed, saved to [output_path].
Want to turn this into structured meeting minutes with decisions and action items?
Options:
A) Yes — run /meeting-minutes-taker (Recommended for meetings/lectures)
B) Export as PDF — run /pdf-creator on the corrected text
C) No thanks — the corrected transcript is all I need
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
Keeps context tight: transcript-fixer is the kind of skill you can hand to a new teammate without a long onboarding doc.
Registry listing for transcript-fixer matched our evaluation — installs cleanly and behaves as described in the markdown.
transcript-fixer is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
Useful defaults in transcript-fixer — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
transcript-fixer fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Registry listing for transcript-fixer matched our evaluation — installs cleanly and behaves as described in the markdown.
transcript-fixer has been reliable in day-to-day use. Documentation quality is above average for community skills.
Useful defaults in transcript-fixer — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
transcript-fixer fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Solid pick for teams standardizing on skills: transcript-fixer is focused, and the summary matches what you get after install.
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