164 lines
9.9 KiB
Markdown
164 lines
9.9 KiB
Markdown
# ktg-plugin-marketplace
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Open-source Claude Code plugins for AI-assisted development, security, and planning.
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Built for my own Claude Code workflow and shared openly for anyone who finds them useful. Solo project — bug reports and feature requests are welcome, pull requests are not accepted.
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## Installation
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```bash
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claude plugin marketplace add https://git.fromaitochitta.com/open/ktg-plugin-marketplace.git
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```
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Then open Claude Code and type `/plugin` to browse and install plugins from the marketplace.
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## Compatibility
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- Claude Code CLI, desktop app, and IDE extensions
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- macOS, Linux, Windows
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- No external dependencies (all scanners and hooks are self-contained)
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---
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## Plugins
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### [LLM Security](plugins/llm-security/) `v5.1.0`
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Security scanning, auditing, and threat modeling for agentic AI projects.
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Built on OWASP LLM Top 10 (2025), OWASP Agentic AI Top 10, and the AI Agent Traps taxonomy (Google DeepMind, 2025). Three layers of protection:
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- **Automated enforcement** — 8 hooks that block dangerous operations in real time (prompt injection, secrets in code, destructive commands, supply chain guardrails)
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- **Deterministic scanning** — 15 Node.js scanners for byte-level analysis: Shannon entropy, Unicode codepoints, typosquatting detection, taint flow, DNS resolution, git forensics
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- **Advisory analysis** — 18 commands that scan, audit, and model threats with structured reports, letter grades, and actionable remediation
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Key commands: `/security posture`, `/security audit`, `/security scan`, `/security threat-model`, `/security plugin-audit`
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6 specialized agents · 15 scanners · 8 hooks · 13 knowledge docs
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→ [Full documentation](plugins/llm-security/README.md)
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---
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### [Config-Audit](plugins/config-audit/) `v3.0.1`
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Configuration intelligence for Claude Code — health checks, feature discovery, and auto-fix.
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Claude Code reads instructions from 7+ file types across multiple scopes. This plugin tells you what's wrong, what's missing, and what's silently conflicting:
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- **Health** — 7 deterministic scanners verify correctness across every configuration file (broken imports, deprecated settings, conflicting rules, permission contradictions)
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- **Opportunities** — context-aware recommendations for Claude Code features you're not using
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- **Action** — auto-fix with mandatory backups, syntax validation, rollback support, and human-in-the-loop workflow
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Key commands: `/config-audit posture`, `/config-audit discover`, `/config-audit feature-gap`, `/config-audit fix`
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6 agents · 8 scanners · 15 commands · 482+ tests
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→ [Full documentation](plugins/config-audit/README.md)
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---
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### [Ultra {research | plan | execute} - local](plugins/ultraplan-local/) `v1.6.0`
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Deep research, implementation planning, and autonomous execution with specialized agent swarms, adversarial review, and failure recovery.
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Three commands, one pipeline: research first, then plan, then execute.
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- **`/ultraresearch-local`** — Deep multi-source research with triangulation: 5 local agents + 4 external agents + Gemini bridge, producing structured briefs with confidence ratings
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- **`/ultraplan-local`** — Interview, 6-8 specialized agents explore the codebase in parallel, adversarial review by plan-critic and scope-guardian. Accepts research briefs via `--research`
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- **`/ultraexecute-local`** — Step-by-step implementation with git checkpoints, automatic failure recovery, and parallel session decomposition
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Defense-in-depth security: plugin hooks block destructive commands and sensitive path writes, prompt-level denylist works in headless sessions, pre-execution plan scan catches dangerous commands before they run, scoped `--allowedTools` replaces `--dangerously-skip-permissions` in parallel sessions.
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Modes: default, spec-driven, research-enriched, foreground, quick, decompose, export
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19 specialized agents · 3 commands · 2 security hooks · No cloud dependency
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→ [Full documentation](plugins/ultraplan-local/README.md)
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---
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### [AI Psychosis](plugins/ai-psychosis/) `v1.0.0`
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Meta-awareness tools that counteract sycophancy, reinforcement loops, and compulsive AI interaction patterns.
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AI assistants are structurally optimized to be agreeable. This creates reinforcement loops where productive collaboration is often a mirror showing you what you want to see. Research documents psychotic episodes triggered by sustained AI interaction in individuals with no prior psychiatric history.
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- **Layer 1 — Behavioral instructions** — SKILL.md rules that modify Claude's behavior: no unearned affirmations, mandatory risk identification, pattern naming
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- **Layer 2 — Programmatic detection** — 4 hooks that measure session duration, dependency language, rapid-fire bursts, edit ratios, and late-night usage with progressive alerts
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- **Layer 3 — Interaction reports** — `/interaction-report` slash command for aggregated session statistics across configurable timeframes (weekly, monthly, all-time). Opt-in
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- **Layer 4 — Contemplative references** — optional references to contemplative approaches when interaction flags are elevated. Opt-in
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Research-informed thresholds. Alerts are progressive and never blocking. Privacy-first: prompt text is never logged. Layers 3 and 4 are off by default.
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1 skill · 1 command · 4 hooks
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→ [Full documentation](plugins/ai-psychosis/README.md)
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---
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### [MS AI Architect — Azure AI and Microsoft Foundry](plugins/ms-ai-architect/) `v1.7.0` `🇳🇴 Norwegian`
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Microsoft AI solution architecture guidance for Norwegian public sector and enterprise.
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Meet Cosmo Skyberg — a structured architect persona who understands the problem before recommending technology. Every recommendation is grounded in 387 reference documents and verified against live Microsoft Learn documentation via MCP:
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- **Structured advisory** — 7-phase methodology from business need to architecture recommendation and optional diagram
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- **Regulatory assessments** — ROS analysis (NS 5814), DPIA/PVK, security scoring (6×5), EU AI Act classification, cost estimation in NOK (P10/P50/P90)
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- **Norwegian public sector** — Digdir architecture principles, Utredningsinstruksen, NSM, Schrems II data residency, EU AI Act compliance workflow
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- **Automated freshness** — sitemap-based change detection polls Microsoft Learn weekly, flags which reference files need updating based on source page changes, and discovers new relevant pages
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Key commands: `/architect`, `/architect:ros`, `/architect:security`, `/architect:dpia`, `/architect:utredning`, `/architect:cost`
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12 specialized agents · 24 commands · 5 skills (387 reference docs) · 2 hooks · sitemap-based KB monitoring
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→ [Full documentation](plugins/ms-ai-architect/README.md)
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---
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### [LinkedIn Thought Leadership](plugins/linkedin-thought-leadership/) `v1.1.0`
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Build authentic LinkedIn authority through algorithmic understanding, strategic consistency, and AI-assisted content creation.
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Updated for the January 2026 360Brew algorithm change, which validates your creator profile before distributing content. v1.1.0 adds guided onboarding, carousel posts, multi-URL comparison, voice drift scoring, industry-specific content angles, and month-over-month analytics.
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- **Guided onboarding** — `/linkedin:onboarding` walks new users through profile → setup → first post in one flow
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- **360Brew profile optimization** — audit your profile against LinkedIn's creator validation criteria
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- **Full content pipeline** — ideation, drafting, publishing, 48-hour monitoring, and analytics
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- **Content Matrix System** — 40+ post ideas from a single topic using 8 universal angles and 48 industry-specific variants
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- **Voice training** — learns your authentic writing style and detects drift with 6-dimension scoring
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- **Analytics pipeline** — import LinkedIn CSV exports, weekly/monthly reports, day-of-week heatmap
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- **Growth strategy** — phase-specific guidance from foundation (0-1K followers) through authority (10K+)
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Key commands: `/linkedin:onboarding`, `/linkedin:post`, `/linkedin:quick`, `/linkedin:carousel`, `/linkedin:react`, `/linkedin:report`
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16 specialized agents · 27 commands · 6 skills · 9 hooks · 24 reference docs
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→ [Full documentation](plugins/linkedin-thought-leadership/README.md)
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---
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### [OKR for Public Sector](plugins/okr/) `v1.3.0` `🇳🇴 Norwegian`
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Turn strategy into measurable goals. An AI coach that learns your organization, tracks progress across cycles, and guides you from first OKR to organizational mastery.
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Most OKR tools explain methodology. This plugin *knows your organization*. After a one-time onboarding conversation, it remembers your maturity level, strategic goals, current OKR, and cultural challenges. Every interaction builds on that knowledge — so you spend time on strategy, not re-explaining context.
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- **Strategy to OKR** — transform goals from virksomhetsplan, tildelingsbrev, or any strategic document into well-structured OKR with guided writing, quality checks, and alignment scoring
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- **Gap analysis** — `/okr:gap` compares your strategic documents against current OKR and shows what's covered, what's missing, and what to do about it
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- **Cross-cycle learning** — `/okr:analyse` tracks score trends, recurring antipatterns, and alignment progress across cycles with visual charts
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- **Proactive coaching** — automatically tells you where you are in the cycle and what to focus on — progress checks mid-cycle, retrospective prep near the end
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- **19 antipattern detection** — catches sandbagging, activity-disguised-as-KR, set-and-forget, and 16 more named failure modes before they take root
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- **Built for norsk offentlig sektor** — 4-month tertials, DFO terminology, tillitsvalgt involvement, Riksrevisjon-ready documentation, governance chain from Stortingsmelding to team OKR
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Key commands: `/okr:skriv`, `/okr:kvalitet`, `/okr:gap`, `/okr:analyse`, `/okr:kaskade`, `/okr:sporing`, `/okr:governance`, `/okr:oppsett`
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7 agents · 10 commands · 4 hooks · 16 reference docs
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→ [Full documentation](plugins/okr/README.md)
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---
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## License
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MIT
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