Convert English content into multiple languages with culturally adapted audio and subtitled videos. Reach global audiences in 2-3 hours instead of weeks of manual translation.
This workflow converts English content into multiple languages with culturally adapted audio and professionally subtitled videos, enabling you to reach global audiences in 2–3 hours instead of weeks of manual translation work. It's designed for content creators, marketing teams, and agencies who need to scale content across international markets quickly. You'll save 25–35 hours per month by automating translation, voiceover generation, video localization, and subtitle creation. ---
Reusable capability packages the community has verified work with the tools in this pack.
Structured critical review of a paper (method, claims, threats to validity) in the voice of a third reviewer.
Generate a valid JSON Schema from example data, with nullability and enum detection baked in.
Agentic loop that exercises a web app like a new user, files bug reports with screenshots + reproduction steps.
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.
Combine coverage data with mutation-testing survivors to produce a ranked list of untested behaviors — the ones most likely to ship bugs.
Extract vendor, line items, tax, and total from a photo of a receipt into a single clean JSON row — ready for expense systems.
Generate headline and body variants respecting platform character limits and style rules, then rank them on a clarity and hook rubric.
Parse a PDF into a structured outline (sections, tables, footnotes) without losing layout semantics.
✓ Verified with ClaudeCompress an entire repo into a single LLM-digestible context bundle with an import graph and hot-file list.
✓ Verified with ClaudeRun two prompt variants against a fixed test set, score with a rubric LLM, and tell you which wins (and why).
✓ Verified with ClaudeRewrite a draft to match a target publication's house style (sentence length, voice, diction) without changing meaning.
✓ Verified with ClaudeAuto-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
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
Structured critical review of a paper (method, claims, threats to validity) in the voice of a third reviewer.
Why: caps 0/0 · io-match · name-mention
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