zjunlp/transcribe
skills/skill-collections/ai-audio-speech/transcribe/SKILL.md
Transcribe audio files to text with optional diarization and known-speaker hints. Use when a user asks to transcribe speech from audio/video, extract text from recordings, or label speakers in interviews or meetings.
$ install --global
skillsauthnpx skillsauth add zjunlp/Skills transcribeInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
3
Scanners Passed
9
Scanners in report
Clean
TrivyContainer and dependency vulnerability scanner
95%
Clean
SemgrepStatic code analysis for vulnerabilities
95%
Clean
mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
Skipped
Socket.devSupply chain security analysis
50%
Skipped
VirusTotalMulti-engine malware detection
50%
Skipped
CrowdStrikeAdvanced threat intelligence
50%
Skipped
OSV-ScannerOpen Source Vulnerability database check
50%
Skipped
OWASP Dep-Check
50%
Last scanned: Apr 23, 2026, 3:35 PM13.8s5 files scanned
SKILL.md
- name:
- transcribe
- description:
- Transcribe audio files to text with optional diarization and known-speaker hints. Use when a user asks to transcribe speech from audio/video, extract text from recordings, or label speakers in interviews or meetings.
Audio Transcribe
Transcribe audio using OpenAI, with optional speaker diarization when requested. Prefer the bundled CLI for deterministic, repeatable runs.
Workflow
- Collect inputs: audio file path(s), desired response format (text/json/diarized_json), optional language hint, and any known speaker references.
- Verify
OPENAI_API_KEYis set. If missing, ask the user to set it locally (do not ask them to paste the key). - Run the bundled
transcribe_diarize.pyCLI with sensible defaults (fast text transcription). - Validate the output: transcription quality, speaker labels, and segment boundaries; iterate with a single targeted change if needed.
- Save outputs under
output/transcribe/when working in this repo.
Decision rules
- Default to
gpt-4o-mini-transcribewith--response-format textfor fast transcription. - If the user wants speaker labels or diarization, use
--model gpt-4o-transcribe-diarize --response-format diarized_json. - If audio is longer than ~30 seconds, keep
--chunking-strategy auto. - Prompting is not supported for
gpt-4o-transcribe-diarize.
Output conventions
- Use
output/transcribe/<job-id>/for evaluation runs. - Use
--out-dirfor multiple files to avoid overwriting.
Dependencies (install if missing)
Prefer uv for dependency management.
uv pip install openai
If uv is unavailable:
python3 -m pip install openai
Environment
OPENAI_API_KEYmust be set for live API calls.- If the key is missing, instruct the user to create one in the OpenAI platform UI and export it in their shell.
- Never ask the user to paste the full key in chat.
Skill path (set once)
export CODEX_HOME="${CODEX_HOME:-$HOME/.codex}"
export TRANSCRIBE_CLI="$CODEX_HOME/skills/transcribe/scripts/transcribe_diarize.py"
User-scoped skills install under $CODEX_HOME/skills (default: ~/.codex/skills).
CLI quick start
Single file (fast text default):
python3 "$TRANSCRIBE_CLI" \
path/to/audio.wav \
--out transcript.txt
Diarization with known speakers (up to 4):
python3 "$TRANSCRIBE_CLI" \
meeting.m4a \
--model gpt-4o-transcribe-diarize \
--known-speaker "Alice=refs/alice.wav" \
--known-speaker "Bob=refs/bob.wav" \
--response-format diarized_json \
--out-dir output/transcribe/meeting
Plain text output (explicit):
python3 "$TRANSCRIBE_CLI" \
interview.mp3 \
--response-format text \
--out interview.txt
Reference map
references/api.md: supported formats, limits, response formats, and known-speaker notes.
Related Skills
zjunlp/scikit-learn
development
VerifiedTrustedCommunity
Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.
7SKILL.mdUpdated Apr 24, 2026
zjunlp/reactome-database
development
VerifiedTrustedCommunity
Query Reactome REST API for pathway analysis, enrichment, gene-pathway mapping, disease pathways, molecular interactions, expression analysis, for systems biology studies.
7SKILL.mdUpdated Apr 24, 2026
zjunlp/rdkit
tools
VerifiedTrustedCommunity
Cheminformatics toolkit for fine-grained molecular control. SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure search, 2D/3D generation, similarity, reactions. For standard workflows with simpler interface, use datamol (wrapper around RDKit). Use rdkit for advanced control, custom sanitization, specialized algorithms.
7SKILL.mdUpdated Apr 24, 2026
zjunlp/pyopenms
development
VerifiedTrustedCommunity
Python interface to OpenMS for mass spectrometry data analysis. Use for LC-MS/MS proteomics and metabolomics workflows including file handling (mzML, mzXML, mzTab, FASTA, pepXML, protXML, mzIdentML), signal processing, feature detection, peptide identification, and quantitative analysis. Apply when working with mass spectrometry data, analyzing proteomics experiments, or processing metabolomics datasets.
7SKILL.mdUpdated Apr 24, 2026