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 OpenClaudia/openclaudia-skills --skill podcast-editgit clone --depth 1 https://github.com/OpenClaudia/openclaudia-skillsWrote 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/openclaudia/openclaudia-skills/podcast-edit)<a href="https://agentmods.dev/skills/openclaudia/openclaudia-skills/podcast-edit"><img src="https://agentmods.dev/badge/skills/openclaudia/openclaudia-skills/podcast-edit/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/openclaudia/openclaudia-skills/podcast-edit"><img src="https://agentmods.dev/badge/skills/openclaudia/openclaudia-skills/podcast-edit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Rogue Agent · line 7 Skill modifies its own code, configuration, or behavior at runtime. Self-modification enables an agent to escalate privileges, disable safety constraints, or install persistent backdoors.Fix: Prevent the skill from modifying its own code, SKILL.md, or configuration files. Treat skill files as read-only at runtime.
- medium Data Exfiltration · line 47 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 74 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Excessive Agency · line 222 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00064 | $0.06519 |
| Opus 5 | $0.00032 | $0.03259 |
| Sonnet 5 | $0.00013 | $0.01304 |
| Haiku 4.5 | $0.00006 | $0.00652 |
Grade A, and why
podcast-edit 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s https://api.openai.com/v1/audio/transcriptions \ How it starts
The opening of the file, as written. The whole thing — 433 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Podcast Edit Skill
Process raw podcast/meeting recordings into polished podcast episodes.
Capabilities
- Smart trimming — Find where the actual podcast starts/ends by transcribing and detecting intros/outros
- Filler word removal — Remove verbal tics: 嗯, 呃, 啊, 哦, 对对对, um, uh, etc.
- Silence trimming — Cut long dead air (>2s) down to natural pauses (~0.6s)
- Audio enhancement — Noise reduction, EQ, multi-speaker volume balancing, loudness normalization to podcast standard (−16 LUFS)
- Re-cutting a finished episode — Surgically remove flagged sections from an already-rendered episode without re-running the whole pipeline
- Highlight clips & reel — Cut shareable soundbites and stitch a ~1-minute reel with music
- Video cut — Apply the same edit to a Zoom/Riverside video recording (see "Video episodes")
Prerequisites
ffmpegandffprobeinstalledOPENAI_API_KEYin environment (for Whisper API transcription)- Python 3 with stdlib only (no extra deps for the helper script)
- Optional:
resemblyzer(pip install resemblyzer) — only for speaker diarization when building highlight reels
Workflow
Step 1: Inspect the audio file
ffprobe -v quiet -print_format json -show_format -show_streams "INPUT_FILE"
Note: duration, sample rate, channels, codec, bitrate.
Step 2: Find podcast start/end (if user says to trim front/back)
Split into 5-minute chunks and transcribe via OpenAI Whisper API with segment-level timestamps:
# Extract chunk
ffmpeg -y -i "INPUT_FILE" -ss OFFSET -t 300 -ar 16000 -ac 1 /tmp/chunk_OFFSET.mp3
# Transcribe
curl -s https://api.openai.com/v1/audio/transcriptions \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-F file="@/tmp/chunk_OFFSET.mp3" \
-F model="whisper-1" \
-F response_format="verbose_json" \
-F language="LANG" \
-F 'timestamp_granularities[]=segment' > /tmp/transcript_OFFSET.json
Scan transcriptions for:
- Start markers: "welcome", "hello everyone", "大家好", "欢迎", intro music, first substantive topic sentence
- End markers: "see you next time", "bye", "下期见", "感谢收听", followed by post-show chat
What ships with it
2 files 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 · 433 lines · 64 tokens per session scan A 8903487e21aa
podcast-edit is a skill published in the GitHub repository OpenClaudia/openclaudia-skills (689 stars, last pushed today), licensed MIT. It adds 64 tokens to every session and 6,519 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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