Moved to a separate marketplace. Drops the plugin directory, the manifest entry, and the README/CLAUDE.md sections describing it. ultraplan-local references to the optional architecture/overview.md contract are kept (filesystem-level discovery, drift-WARN), but marketplace-name pointers in ultraplan-local docs may follow.
54 lines
2.4 KiB
JSON
54 lines
2.4 KiB
JSON
{
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"$schema": "https://anthropic.com/claude-code/marketplace.schema.json",
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"name": "ktg-plugin-marketplace",
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"owner": {
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"name": "Kjell Tore Guttormsen",
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"email": "ktg@fromaitochitta.com"
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},
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"metadata": {
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"description": "Open-source Claude Code plugins for AI-assisted development, security, and planning",
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"version": "1.0.0"
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},
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"plugins": [
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{
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"name": "llm-security",
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"source": "./plugins/llm-security",
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"description": "Security scanning, auditing, and threat modeling for Claude Code projects. OWASP LLM Top 10 (2025) and Agentic AI Top 10."
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},
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{
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"name": "config-audit",
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"source": "./plugins/config-audit",
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"description": "Multi-agent workflow for analyzing, reporting, and optimizing Claude Code configuration across your entire machine"
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},
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{
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"name": "ultraplan-local",
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"source": "./plugins/ultraplan-local",
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"description": "Four-command context-engineering pipeline (brief → research → plan → execute) with specialized agent swarms, external research triangulation, adversarial review, session decomposition, and headless execution"
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},
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{
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"name": "linkedin-thought-leadership",
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"source": "./plugins/linkedin-thought-leadership",
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"description": "Build LinkedIn thought leadership with algorithmic understanding, strategic consistency, and authentic engagement. Updated for the January 2026 360Brew algorithm change."
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},
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{
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"name": "graceful-handoff",
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"source": "./plugins/graceful-handoff",
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"description": "Produce session-handoff artifacts, commit and push pending work, and print a copy-paste prompt for the next session. Designed for context-constrained models like Opus 4.7."
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},
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{
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"name": "ai-psychosis",
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"source": "./plugins/ai-psychosis",
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"description": "Meta-awareness tools for healthy AI interaction patterns. Detects reinforcement loops, scope escalation, narrative crystallization, and other compulsive patterns."
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},
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{
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"name": "ms-ai-architect",
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"source": "./plugins/ms-ai-architect",
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"description": "Microsoft AI Solution Architect — structured architecture guidance for the full Microsoft AI stack."
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},
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{
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"name": "okr",
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"source": "./plugins/okr",
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"description": "Expert OKR guidance for Norwegian public sector. Write, review, cascade, track and govern OKR based on Google/Doerr methodology adapted for 4-month tertial cycles."
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}
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]
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}
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