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Kai Repurpose

Community
cgallic
kai-repurpose

Take one pillar content piece and produce 15-25 derivative assets across platforms — social posts, email, LinkedIn article, video scripts, newsletter section, infographic brief, and more. The ultimate content multiplier. Use when "repurpose this", "turn this into", "make social posts from this blog", "content repurposing", "1 to many", "atomize this content", "extract posts from", or any request to derive multiple pieces from one source.

Overview

Publishercgallic
Repositorykai-cmo-harness
Skill namekai-repurpose
Stars
61
Forks
8
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by cgallic on GitHub. Read the source before you install it.

Installation

Install the Kai Repurpose AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/cgallic/kai-cmo-harness/tree/main/harness/skills-v2/kai-repurpose
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/cgallic/kai-cmo-harness.git /tmp/kai-cmo-harness
mkdir -p .claude/skills
cp -r /tmp/kai-cmo-harness/harness/skills-v2/kai-repurpose .claude/skills/kai-repurpose
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Kai Repurpose in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Kai Repurpose on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Kai Repurpose is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Kai root note: knowledge/, harness/, and scripts/ paths in this skill live in the Kai install, not the user's project. Resolve them against the first ancestor directory of this SKILL.md that contains a knowledge/ folder (the Kai plugin root, ~/.claude/kai, or the kai-cmo-harness repo). MARKETING.md, memory/, and any output files live in the current project. If a referenced scripts/ command is not available in this install, say so, skip it, and continue with the file-based guidance — never fabricate its output.

Objective

One pillar piece turned into 15–25 channel-native assets, each standing on its own, staged into a posting schedule that keeps them from cannibalizing each other. A derivative that reads as a repost of the pillar has failed — the same insight gets a different hook, a different length, and the platform's own register every time it appears.

Done when

Work type campaign — floor E5/C3/O4 (harness/eco-floors.yaml), composite. Every derivative is a child work item under its own type: social-post (E5/C2/O3), email-lifecycle (E5/C3/O3), blog-post for the expanded LinkedIn article (E5/C3/O3). The campaign is CLOSED only when every child is CLOSED and the campaign-level threshold is met; one unshipped asset keeps it open.

  • E5 — each published derivative returns a provider id and its public permalink reads back matching the approved text. This skill drafts; publishing is a separate approved action.
  • C3 — every piece passes banned_word_check with zero violations, respects its platform's character and format limits, and a non-author read the set end to end for hook repetition.
  • O4 — reach, engagement, and click-through against a threshold declared before the first post, read at the declared window from platform insights.

Constraints

  • Read MARKETING.md from the project root first. If it does not exist, build it from the codebase — CLAUDE.md, README.md, PROJECT.md, package.json, landing pages, email/ad/analytics config — using the template from /kai-email-system, and confirm the draft. Do not ask the user what the product is.
  • Three things must be known before production: the source content (pasted, linked, or a file path), which platforms get derivatives, and what matters most (social reach, email engagement, SEO, video views).
  • Transcript, podcast, webinar, interview, or long-article sources require a quote mining pass first, and harness/references/transcript-video-research-rules.md loads before mining any third-party video, audio, podcast, webinar, or transcript material.
  • Every quote keeps its exact source location — file path, URL, episode name, transcript timestamp, or paragraph locator — so any derivative can be traced back.
  • Never invent quotes, credentials, or expert authority. Quote mining is source extraction; rewriting happens only in derivative files.
  • Draft as a dry run. Publishing or scheduling requires explicit human approval.
  • Each derivative stands alone — no "as I wrote in my blog post." Credit the pillar only where platform norms expect it.
  • Different hook for every piece, even two pieces built on the same insight.
  • Tone adapts to platform: LinkedIn professional, X punchy, TikTok conversational.
  • Every piece runs the quality gate: zero banned words, zero AI slop, platform limits respected.
  • Stagger the release over 1–2 weeks. Lead with the highest-reach platform, follow with niche channels.

Context

NeedLoad
Repurposing mechanics and channel fitknowledge/playbooks/content-repurposing.md
Mining third-party video, audio, or transcriptsharness/references/transcript-video-research-rules.md
Organic social format contract and gate minimumsharness/skill-contracts/social-post.yaml
Product, ICP, voice, current channelsMARKETING.md (project root)

Extraction map at workspace/repurposed/_extraction-map.md — one row per extract, with type, best channel, and format. Extract six kinds of material: key insights (3–5 standalone ideas), data points (stats, numbers, results), quotable lines (bold claims, memorable phrases), steps and frameworks, stories and examples, and contrarian takes.

Quote bank at workspace/repurposed/_quote-bank.md — per entry: source location; the quote or paraphrase, with direct quotes marked clearly and kept short; use case (hook, proof, objection, story, CTA, email subject, clip candidate); risk note (unsupported claim, private detail, medical/legal/financial claim, needs approval); and attribution requirement (guest name, customer approval, anonymous/internal, source citation).

Standard derivative set — adapt to the platforms chosen, but these are the format contracts:

#AssetPlatformFormat
1–2Text post per insightLinkedIn1200 chars
3Carousel (framework)LinkedIn8–10 slides outline
4Thread (full argument)X5–7 tweets
5–6Single tweet per data pointX280 chars
7Single tweet (contrarian take)X280 chars
8Carousel (key takeaways)Instagram5–7 slides outline
9Caption (story/example)Instagram150–300 words
10Video script (hook + insight)TikTok30–60 seconds
11Video script (contrarian take)TikTok15–30 seconds
12Newsletter sectionEmail100–200 words
13Standalone value-add emailEmail300–500 words
14Expanded-angle articleLinkedIn2,200–3,000 words
15Shorts scriptYouTube30–60 seconds
16–25Additional platform-specific variantsVariousVarious

Output tree — same paths as v1:

workspace/repurposed/
├── _extraction-map.md
├── _quote-bank.md
├── _source-pillar.md          # copy of original for reference
├── linkedin/                  # post-insight-1.md, post-insight-2.md, carousel-framework.md, article-expanded.md
├── x-twitter/                 # thread-full.md, tweet-stat-1.md, tweet-stat-2.md, tweet-contrarian.md
├── instagram/                 # carousel-takeaways.md, caption-story.md
├── tiktok/                    # script-insight.md, script-contrarian.md
├── email/                     # newsletter-section.md, standalone-email.md
├── youtube/                   # shorts-script.md
└── _quality-report.md

Escalate when

  • The pillar does not contain 15 distinct ideas — padding produces derivatives that hurt the brand.
  • A quote's source location cannot be established, or its attribution requirement is unresolved.
  • Source material contains medical, legal, or financial claims that need substantiation before any derivative repeats them.
  • A third-party transcript's usage permission is unclear.
  • The user wants the set scheduled or published without approval.

Frequently asked questions

What does the Kai Repurpose AI skill do?

Take one pillar content piece and produce 15-25 derivative assets across platforms — social posts, email, LinkedIn article, video scripts, newsletter section, infographic brief, and more. The ultimate content multiplier. Use when "repurpose this", "turn this into", "make social posts from this blog", "content repurposing", "1 to many", "atomize this content", "extract posts from", or any request to derive multiple pieces from one source.

Why use Kai Repurpose on TypingMind?

Because you install it once and use it with any model. Kai Repurpose is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Kai Repurpose in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cgallic/kai-cmo-harness/tree/main/harness/skills-v2/kai-repurpose. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Kai Repurpose?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Kai Repurpose?

As many as you like. As long as a model supports skills, you can use Kai Repurpose with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Kai Repurpose AI skill free?

It is published on GitHub by cgallic. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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