transcribe-diarize
Skill0 starsChanged 4 months agoMITView on GitHub
Produce a speaker-aware transcript with diarization and optional known-speaker hints using the bundled CLI. Use when the user explicitly needs speaker labels.
- Reads credentials
In this repository37 other files it gives its agents.
- adr-logSkill
- adversarial-reviewSkill
- ai-writing-detoxSkill
- autonomous-video-pipelineSkill
- browser-tutorial-videoSkill
- codex-execution-packSkill
- continuity-ledgerSkill
- github-mobile-opsSkill
- github-seoSkill
- idea-validationSkill
- illustration-promptSkill
- issue-control-loopSkill
- justdoitSkill
- lazy-seniorSkill
- long-context-dispatchSkill
- openclaw-councilSkill
- parallel-worktreesSkill
- playwrightSkill
- product-councilSkill
- product-shapingSkill
- public-artifact-laneSkill
- readme-generatorSkill
- screenshotSkill
- session-to-postSkill
- spec-bundleSkill
- spreadsheetSkill
- spreadsheet-tabular-analysisSkill
- spreadsheet-xlsx-editSkill
- tool-scoutSkill
- transcribe-basicSkill
- transcribeSkill
- triage-findingSkill
- video-builderSkill
- video-render-runSkill
- video-spec-builderSkill
- visual-explainerSkill
- work-shapingSkill
The file
We split this skill into the rules it gives an agent
2 rules. About 0 are common, 1 is distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈1
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 10
Do not use when: a plain fast transcript is enough.
- Very specific: line 18
Run ~/.codex/skills/transcribe/scripts/transcribe_diarize.py with gpt-4o-transcribe-diarize, diarized_json, and any provided …
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Transcribe Diarize
Metadata
- Trigger when: the user explicitly wants speaker labels, diarization, or structured speaker-aware transcript output.
- Do not use when: a plain fast transcript is enough.
Skill Purpose
Produce a diarized transcript with the narrow settings required for speaker-aware output instead of treating every transcription job as a plain text task.
Instructions
- Gather the audio path, desired output location, and any known-speaker reference files. Check that
OPENAI_API_KEYis set before doing anything else. - Run
~/.codex/skills/transcribe/scripts/transcribe_diarize.pywithgpt-4o-transcribe-diarize,diarized_json, and any provided--known-speakerhints under a stable output path such asoutput/transcribe/. - Validate the transcript, speaker labels, and segment boundaries. State clearly when speaker confidence is weak because references were missing or the audio is noisy.
Non-Negotiable Acceptance Criteria
- The task explicitly needs diarization or speaker labels.
- Output format is diarization-capable and the chosen model matches that need.
- Known-speaker hints are used only when they actually exist; they are never invented.
- Confidence or segmentation caveats are called out when labels are weak.
Output
- The diarized transcript path.
- The model, response format, and any known-speaker hints used.
- A short note on label confidence, segmentation quality, or caveats.
Next skill options(only if needed):$transcribe-basic— use when the user decides speaker labeling is unnecessary after all.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

