Install
$ agentstack add skill-cgallic-kai-cmo-harness-kai-repurpose ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
Take 1 pillar → produce 15-25 channel-specific assets. The content multiplier.
Phase 0: Load Product Context
Check if MARKETING.md exists in the project root (same directory as CLAUDE.md, README.md, package.json).
If it exists: Read it — skip product discovery questions. It has the product name, ICP, value prop, monetization, brand voice, current channels, and competitive landscape.
If it does NOT exist: Auto-explore the codebase to create it in the project root (next to CLAUDE.md). Do NOT ask the user what the product is. Read CLAUDE.md, README.md, PROJECT.md, package.json, landing pages, and any project files. Search for email/ad/analytics config. Then create MARKETING.md using the template from /kai-email-system. Present draft to user for confirmation.
Phase 1: Pillar Input
Read from MARKETING.md. Only ask about things not covered there:
- Source content — paste it, link it, or point to the file
- Platforms — which channels should get derivative content?
- Priority — what matters most? (social reach, email engagement, SEO, video views)
Load: E:\Dev2\kai-cmo-harness-work\knowledge\playbooks\content-repurposing.md
Phase 2: Extraction Map
Read the pillar content and extract:
- Key insights (3-5 standalone ideas)
- Data points (stats, numbers, results)
- Quotable lines (bold claims, memorable phrases)
- Steps/frameworks (any process or list)
- Stories/examples (anecdotes, case studies)
- Contrarian takes (anything that challenges conventional wisdom)
Generate workspace/repurposed/_extraction-map.md:
| Extract | Type | Best Channel | Format | |---------|------|-------------|--------| | "Stat about X" | Data point | X, LinkedIn | Single post | | "3-step framework" | Process | Instagram carousel, LinkedIn | Carousel / text post | | "Contrarian claim" | Take | X thread, TikTok | Thread / video script | | ... | ... | ... | ... |
Quote Mining Pass
When the source is a transcript, podcast, webinar, interview, or long article, run a quote mining pass before derivative production.
Load E:\Dev2\kai-cmo-harness\harness\references\transcript-video-research-rules.md before quote mining third-party video, audio, podcast, webinar, or transcript material.
Create workspace/repurposed/_quote-bank.md with:
- Source location: file path, URL, episode name, transcript timestamp, or paragraph locator
- Quote or paraphrase: mark direct quotes clearly; keep direct quotes short
- Use case: hook, proof, objection, story, CTA, email subject, clip candidate
- Risk note: unsupported claim, private detail, medical/legal/financial claim, or needs approval
- Attribution requirement: guest name, customer approval, anonymous/internal, or source citation
Rules:
- Preserve exact source locations so every derivative can be traced back.
- Do not invent quotes, credentials, or expert authority.
- Treat quote mining as source extraction, not rewriting. Rewrite only in derivative files.
- Draft as a dry run first; publishing or scheduling requires explicit approval.
Phase 3: Derivative Production
From one pillar, produce:
Standard Derivative Set (adapt based on platforms)
| # | Asset | Platform | Format | |---|-------|----------|--------| | 1 | LinkedIn text post (insight #1) | LinkedIn | 1200 chars | | 2 | LinkedIn text post (insight #2) | LinkedIn | 1200 chars | | 3 | LinkedIn carousel (framework) | LinkedIn | 8-10 slides outline | | 4 | X/Twitter thread (full argument) | X | 5-7 tweets | | 5 | X/Twitter single tweet (data point #1) | X | 280 chars | | 6 | X/Twitter single tweet (data point #2) | X | 280 chars | | 7 | X/Twitter single tweet (contrarian take) | X | 280 chars | | 8 | Instagram carousel (key takeaways) | Instagram | 5-7 slides outline | | 9 | Instagram caption (story/example) | Instagram | 150-300 words | | 10 | TikTok video script (hook + insight) | TikTok | 30-60 seconds | | 11 | TikTok video script (contrarian take) | TikTok | 15-30 seconds | | 12 | Email newsletter section | Email | 100-200 words | | 13 | Email standalone (value-add) | Email | 300-500 words | | 14 | LinkedIn article (expanded angle) | LinkedIn | 700-1000 words | | 15 | YouTube Shorts script | YouTube | 30-60 seconds | | 16-25 | Additional platform-specific variants | Various | Various |
Rules
- Each derivative must stand alone (no "as I wrote in my blog post...")
- Adapt tone to platform (LinkedIn = professional, X = punchy, TikTok = conversational)
- Different hook for each piece (even when based on the same insight)
- Every piece gets quality gate check: zero banned words, zero AI slop, platform limits
- Credit the pillar only if the platform norms expect it
Phase 4: Output
workspace/repurposed/
├── _extraction-map.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
Phase 5: Publishing Schedule
Generate a suggested posting schedule — stagger derivatives over 1-2 weeks so they don't cannibalize each other. Lead with the highest-reach platform, follow with niche channels.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: cgallic
- Source: cgallic/kai-cmo-harness
- License: MIT
- Homepage: https://meetkai.xyz
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.