By ddunnock
Semantically search IEEE, INCOSE, and ISO systems engineering standards. Retrieve relevant knowledge snippets and apply RAG to ground your engineering specifications using a local Python MCP server with Qdrant vector database and OpenAI embeddings.
Requires secrets
Needs API keys or credentials to function
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npx claudepluginhub ddunnock/claude-plugins --plugin knowledge-mcpTransform concept development artifacts into INCOSE-compliant formal requirements. AI-assisted requirements development with hybrid quality checking (16 deterministic + 9 semantic INCOSE GtWR v4 rules), verification planning, bidirectional traceability, gap analysis against concept architecture, assumption lifecycle management, and ReqIF export. Organized around functional blocks from concept development. Includes cross-cutting notes registry, need/requirement split workflow, gap discovery agent, assumption tracker, 5 specialized agents (quality-checker, tpm-researcher, skeptic, gap-analyst, document-writer), 16 scripts, 10 commands, and hooks for automatic state updates. Use when developing requirements, formalizing needs, writing specifications, building traceability, analyzing coverage gaps, managing assumptions, or preparing for systems engineering reviews.
Stream long-form content to markdown files with resume capability and context preservation
Transform documentation using the Diátaxis framework
Walk through the NASA Phase A concept development lifecycle: ideation, problem definition, black-box architecture, drill-down with gap analysis, and document generation. Produces concept documents and solution landscape summaries with cited research. Includes 7 specialized agents (ideation, problem analysis, architecture, domain research, gap analysis, skeptic verification, document writing), 6 scripts (session management, source/assumption tracking, web research with crawl4ai), 9 commands, hooks for automatic state updates, and tiered research tool detection. Use when developing a concept, exploring a new idea, brainstorming a system concept, running Phase A, creating a concept document, or conducting feasibility studies.
Project-focused specification and task management with PLANS taxonomy, ADR-style architecture decisions, SMART acceptance criteria, SEAMS-enhanced clarification, tech-specific designer agents, anti-pattern detection (50 patterns across 5 tech stacks), 4-level verification (stub detection + wiring checks), execution orchestration (deviation rules, checkpoint taxonomy, auth gates, session continuity), specialized analysis agents, git checkpoints, mandatory approval gates, and session-memory hooks. Outputs to speckit/ directory.
Upstash Context7 MCP server for up-to-date documentation lookup. Pull version-specific documentation and code examples directly from source repositories into your LLM context.
Upstash Context7 MCP server for up-to-date documentation lookup. Pull version-specific documentation and code examples directly from source repositories into your LLM context.
Permanent coding companion for Claude Code — survives any update. MCP-based terminal pet with ASCII art, stats, reactions, and personality.
Official GitHub MCP server for repository management. Create issues, manage pull requests, review code, search repositories, and interact with GitHub's full API directly from Claude Code.
GitLab DevOps platform integration. Manage repositories, merge requests, CI/CD pipelines, issues, and wikis. Full access to GitLab's comprehensive DevOps lifecycle tools.