Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add jamditis/claude-skills-journalism --skill interview-transcriptiongit clone --depth 1 https://github.com/jamditis/claude-skills-journalismWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/jamditis/claude-skills-journalism/interview-transcription)<a href="https://agentmods.dev/skills/jamditis/claude-skills-journalism/interview-transcription"><img src="https://agentmods.dev/badge/skills/jamditis/claude-skills-journalism/interview-transcription/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/jamditis/claude-skills-journalism/interview-transcription"><img src="https://agentmods.dev/badge/skills/jamditis/claude-skills-journalism/interview-transcription.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00026 | $0.03343 |
| Opus 5 | $0.00013 | $0.01672 |
| Sonnet 5 | $0.00005 | $0.00669 |
| Haiku 4.5 | $0.00003 | $0.00334 |
Grade A, and why
interview-transcription scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
subprocess.run(cmd, check=True, capture_output=True) How it starts
The opening of the file, as written. The whole thing — 422 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Interview transcription and management
Practical workflows for journalists managing interviews from preparation through publication.
When to activate
- Preparing questions for an interview
- Processing audio/video recordings
- Creating or managing transcripts
- Organizing notes from multiple sources
- Building a source relationship database
- Generating timestamped quotes for fact-checking
- Converting recordings to publishable quotes
Recording setup for transcription
For pre-interview research, question design, attribution agreements, and consent scripts, use the interview-prep skill. The notes here cover only the recording configuration that affects transcription quality.
# Standard recording configuration for clean transcription
RECORDING_SETTINGS = {
'format': 'wav', # Lossless for transcription
'sample_rate': 16000, # Whisper resamples to 16k anyway; 16k saves disk
'channels': 1, # Mono is fine for speech; stereo only if mics are positionally distinct
'backup': True, # Always run a backup recorder
}
# File naming convention
# YYYY-MM-DD_source-lastname_topic.wav
# Example: 2026-05-08_smith_budget-hearing.wav
Two-device rule. Always record on two devices. Phone as backup minimum. If using a wireless lav mic, the recorder built into the lav unit is one device; the phone running a backup app is the second.
Mono is preferred unless each speaker has their own dedicated microphone routed to a distinct channel. Stereo with both speakers bleeding into both channels is worse for diarization than clean mono.
Transcription workflows
Automated transcription pipeline
Vanilla OpenAI Whisper transcribes audio to text but does not assign speaker labels. To get diarized output ("Speaker 1:" / "Speaker 2:" / etc.) you need a tool that combines Whisper with a diarization model, typically WhisperX (m-bain/whisperX), which wraps faster-whisper transcription with pyannote.audio diarization and produces word-level timestamps with speaker IDs in one pass.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 422 lines · 26 tokens per session scan A 1b73a54a40c7
interview-transcription is a skill published in the GitHub repository jamditis/claude-skills-journalism (391 stars, last pushed 4d ago), licensed MIT. It adds 26 tokens to every session and 3,343 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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