Multilingual Content Localization
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. ---
What This Pack Gets Done
The Stack
Skills that enhance this pack Community
Browse all skills →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 ClaudeRecommended skills for this pack
Auto-curated by the AIDiveForge recommendation matrix. These skills are predicted to enhance this pack based on category, capability, and domain signals.
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JSON Schema Builder transform 31%
Generate a valid JSON Schema from example data, with nullability and enum detection baked in.
Why: caps 0/0 · io-match · name-mention
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Browser Testing Agent Loop post 31%
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
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Competitor Feature Matrix transform 31%
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
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Test Gap Finder post 31%
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
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Receipt OCR Normalizer post 31%
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
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Ad Creative Variant Generator enhance 31%
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
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Academic Paper Review post 31%
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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