Capture, transcribe, edit, and distribute podcast episodes with automatic audio cleanup, timestamped transcripts, and multi-platform preparation—reducing manual editing from 5 hours to 1 hour per episode.
This workflow automates the end-to-end podcast production pipeline: capture raw audio, remove background noise, generate timestamped transcripts, edit and prepare clips for distribution, and publish to multiple platforms simultaneously. Designed for podcast creators and production teams, this pack reduces manual editing from 5 hours to 1 hour per episode and saves 20–25 hours per month across the full production cycle. ---
Reusable capability packages the community has verified work with the tools in this pack.
Turn a raw transcript into a decision-focused recap: outcomes, owners, deadlines, open threads.
From an episode transcript, produce timestamped chapter markers with topic labels ready for Apple Podcasts and YouTube chapters.
Generate headline and body variants respecting platform character limits and style rules, then rank them on a clarity and hook rubric.
Extract vendor, line items, tax, and total from a photo of a receipt into a single clean JSON row — ready for expense systems.
Combine coverage data with mutation-testing survivors to produce a ranked list of untested behaviors — the ones most likely to ship bugs.
Turn a list of competitor URLs into a normalized feature and pricing matrix you can paste into a deck — without the 'plan names mean different things at each company' problem.
Agentic loop that exercises a web app like a new user, files bug reports with screenshots + reproduction steps.
Run a set of consent + provenance checks before a voice cloning job is accepted.
Generate a valid JSON Schema from example data, with nullability and enum detection baked in.
Find and remove long silences, ums, and ahs in raw audio with configurable thresholds — for podcast and voiceover cleanup.
Auto-curated by the AIDiveForge recommendation matrix. These skills are predicted to enhance this pack based on category, capability, and domain signals.
Generate a valid JSON Schema from example data, with nullability and enum detection baked in.
Why: caps 0/0 · io-match · name-mention
Run a set of consent + provenance checks before a voice cloning job is accepted.
Why: caps 0/0 · io-match · name-mention
Agentic loop that exercises a web app like a new user, files bug reports with screenshots + reproduction steps.
Why: caps 0/0 · io-match · name-mention
Turn a list of competitor URLs into a normalized feature and pricing matrix you can paste into a deck — without the 'plan names mean different things at each company' problem.
Why: caps 0/0 · io-match · name-mention
Combine coverage data with mutation-testing survivors to produce a ranked list of untested behaviors — the ones most likely to ship bugs.
Why: caps 0/0 · io-match · name-mention
Extract vendor, line items, tax, and total from a photo of a receipt into a single clean JSON row — ready for expense systems.
Why: caps 0/0 · io-match · name-mention
Generate headline and body variants respecting platform character limits and style rules, then rank them on a clarity and hook rubric.
Why: caps 0/0 · io-match · name-mention
From an episode transcript, produce timestamped chapter markers with topic labels ready for Apple Podcasts and YouTube chapters.
Why: caps 0/0 · io-match · name-mention
Find and remove long silences, ums, and ahs in raw audio with configurable thresholds — for podcast and voiceover cleanup.
Why: caps 0/0 · io-match · name-mention
Turn a raw transcript into a decision-focused recap: outcomes, owners, deadlines, open threads.
Why: caps 0/0 · io-match · name-mention
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