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    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIGitHub CopilotGitHub CopilotGemini CLIGemini CLI+20 more

    Bulk Content Repurposing to 6 Platforms, Fact-Checked

    1

    Point it at a folder of posts or transcripts and get LinkedIn, X, email, YouTube, IG, and video-script versions of each. Every output is checked for made-up numbers, AI clichés, and copy-paste sameness.

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    Bulk Content Repurposing to 6 Platforms, Fact-Checked

    Bulk Content Repurposing to 6 Platforms, Fact-Checked

    Example session with this skill installed

    Repurpose these two markdown files into LinkedIn, X thread, email, YouTube description, Instagram caption and short-form video script for every piece. Each ## section is its own post. I don't have a voice profile, so infer one from the posts. Start the schedule Monday 2026-10-05 and give me Buffer CSVs too.

    • Read your context and instructions
    • Compiled the bulk content repurposing
    • Generated the data export

    Run summary

    Done: 7 pieces × 6 formats, validator PASS after 3 rounds.

    Ingested: 7 pieces from your 2 files. Only transcript_1 has timestamps, so only it got YouTube chapters, from its real stamps.

    • Voice (inferred): first-person, plainspoken, practical; no emoji; no CTA URL (none in your sources).
    • Round 1: 0 failures, 7 warnings:
    == post_3 ==
      WARN  email.subject          grounding  proper noun "Tuesday's" not in source (unknown: Tuesday's)
      WARN  instagram_caption      grounding  proper noun 'Monday' not in source (unknown: Monday)
      WARN  instagram_caption      grounding  proper noun 'Tuesday' not in source (unknown: Tuesday)
      WARN  linkedin               grounding  proper noun 'Monday' not in source (unknown: Monday)
      WARN  linkedin               grounding  proper noun 'Tuesday' not in source (unknown: Tuesday)
    == batch ==
      WARN  instagram CTA          sameness   pieces short_1, long_1, transcript_1, tempting_1 end on the same line: 'Full post linked in bio.'
      WARN  links                  placeholder 28 link placeholder(s) like '[link]' in 7 piece(s) (post_1, post_2, post_3, short_1, long_1, transcript_1, tempting_1) - fill in real URLs before scheduling, or set cta.url in voice.json
    PASS - 7 piece(s), 0 fail, 7 warn  [grounding: on; voice: on; slop: on]
    

    Round 2: cut the invented "Monday/Tuesday" from post_3, rewrote the 4 identical Instagram closers. 1 warning left.

    Round 3: reworded two Instagram CTAs promising a "scoring sheet" and "feeding checklist" your posts don't have. Final: PASS - 7 piece(s), 0 fail, 1 warn (the [link] reminder).

    One piece in full: transcript_1

    Verbatim from repurposed.md; the other 6 pieces match this layout.

    LinkedIn (scheduled Thu 2026-10-15 08:30 local)

    I went back to a paper task list after cycling through Todoist, Things and Notion.
    
    The apps worked fine. The problem was that they made adding and reorganizing tasks effortless, and for me that low friction was the actual problem.
    
    Zero friction to add meant every half-formed idea went straight onto the list. It ballooned to 200 items and stopped meaning anything. Zero friction to reorganize meant I'd spend twenty minutes moving tasks between three views and call it productive.
    
    Paper fixed both by being annoying on purpose. Writing something down takes effort, so only things that matter make it onto the page. Reorganizing means rewriting the list, so I leave it alone and do the work.
    
    I'm not saying digital tools are bad. The lesson is narrower: less friction isn't always better. Sometimes friction is a feature you only notice once it's gone.
    
    Full post: [link]
    

    X thread (6 posts) (scheduled Mon 2026-10-12 12:00 local)

    1/ Why I went back to paper for my task list after Todoist, Things and Notion:
    
    2/ The apps worked. They just made adding and reorganizing tasks frictionless, and that turned out to be my problem.
    
    3/ No friction to add: every half-formed idea went on the list, it hit 200 items, and the list stopped meaning anything.
    
    4/ No friction to reorganize: I once spent twenty minutes moving tasks between three views and called it productive.
    
    5/ Paper is annoying on purpose. Writing is a filter, and rewriting the list is enough of a pain that I just do the tasks.
    
    6/ Digital tools aren't bad. But less friction isn't always better. Sometimes friction is doing useful work. Full post: [link]
    

    Email (scheduled Tue 2026-11-10 07:00 local)

    Subject: I quit task apps for paper
    Preview: The apps worked fine. The low friction was the problem.

    After Todoist, Things and Notion, I'm back on a paper list, and the reason is a little boring: frictionless adding turned my list into a 200-item junk drawer, and frictionless reorganizing ate my time. Here's why paper's built-in friction fixed both: [link]
    

    YouTube description

    I went back to paper for my task list after trying Todoist, Things and Notion. Here's why.
    
    0:00 Why paper, after every app
    0:29 Low friction was the problem
    0:47 The 200-item junk drawer
    1:05 Friction as a filter
    1:41 The actual lesson
    
    Written version: [link]
    

    Instagram caption (scheduled Fri 2026-10-16 11:00 local)

    200 items on my task list, and none of them meant anything.
    
    That's what zero friction did. Paper fixed it by being annoying on purpose: if it's worth writing down, it's worth doing, and rewriting the list is enough hassle that I just do the work instead.
    
    Would you go back to paper? The whole story is in the bio link.
    
    #productivity #papertodolist #analog #taskmanagement #focus
    

    Short-form video script (45s target, 101 words)

    I tried Todoist, Things, Notion, the whole rotation, and went back to paper. Not because the apps were broken. They worked fine. The problem was they made everything frictionless. Adding a task cost nothing, so every half-formed idea went on the list, and it ballooned to 200 items that meant nothing. Reorganizing cost nothing, so I'd spend twenty minutes shuffling tasks between three views and call it productive. Paper is annoying on purpose. Writing something down is a filter. Rewriting the list is a hassle, so you just do the tasks. Less friction isn't always better. Sometimes friction is the feature.
    

    Files written

    FileWhat's in it
    repurposed.mdAll 7 pieces × 6 formats with scheduled slots
    schedule.csv65 rows, 2026-10-05 → 2026-11-17 (44 X posts, 7 each for LinkedIn, Instagram and email)
    buffer_linkedin.csv, buffer_instagram.csv7 rows each, Buffer bulk-upload format
    voice.jsonInferred voice profile, reusable

    Next steps

    1. Fill the 28 [link] placeholders, or give me your URL to set in voice.json and I'll re-render.
    2. Add Instagram images: Image URL in buffer_instagram.csv is empty.
    3. Skim repurposed.md, then bulk-upload the Buffer CSVs. Name any piece to rework.

    bulk-content-repurposing-to-6-platforms-.csv

    CSV · data export

    Generated

    Example file from a real run - the skill writes it into your workspace.

    Connects securely to your tools. The creator never sees your data.

    What you get

    Turn a folder of blog posts or video transcripts into 6 platform-ready formats for every piece in one run.Fail any output that states a number, stat, or quote not found in your source.Catch AI clichés and copy-paste sameness across the whole batch, not one post at a time.Get a review document plus a staggered schedule CSV, with a Buffer bulk-upload option.

    About this skill

    Repurposing with AI is easy. Trusting the output is not. This skill takes a whole batch of long-form content: a folder of blog posts, articles, or video transcripts (.md, .txt, .html, .docx, .srt, .vtt). For every piece it writes six platform-native formats: a LinkedIn post, an X thread, an email blurb, a YouTube description, an Instagram caption, and a short-form video script. Then a validation script checks all of it before it reaches you.

    What the checks catch:

    • Made-up facts. Every number, percentage, dollar amount, year, and quote in the output must appear in your source. If the AI invents a "73% of marketers" stat, the batch fails until it's removed.
    • AI-slop. About 60 phrases and patterns readers recognize instantly ("in today's fast-paced world", "it's not just X, it's Y", em-dash overload, emoji spam), each with a written rationale.
    • Batch sameness. Every LinkedIn post opening "Most people…", or every caption ending on the same CTA, is invisible one post at a time. The script compares the whole batch.
    • Real platform rules, counted the way the platforms count them. X's weighted character count (links count as 23), YouTube's chapter rules (timestamps it can't find in your source get rejected), Instagram's 5-hashtag cap, LinkedIn and Instagram hook cut-offs.
    • Your voice. An optional voice profile sets tone, banned words, the CTA link per platform, emoji policy, and required hashtags. The script enforces it.

    You get a clean review document grouped by piece, plus a schedule CSV staggered across a calendar, with an option for Buffer's bulk-upload format. Optional Threads and Bluesky posts are included.

    Built eval-first. The test suite and before/after results are in the changelog: on a planted fabricated-stats test, the old version passed everything and v2 failed all 5 invented claims.

    How to install

    Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.

    ~30 seconds
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