plugin.json

{
  "$schema": "https://agent-plugins.org/schemas/1.0.0/plugin.schema.json",
  "name": "production-rag-genai-copilot",
  "version": "0.2.0",
  "description": "Engineering-focused RAG architecture, implementation, debugging, evaluation, security, incidents, and reliability workflows.",
  "author": {
    "name": "Tekris Labs",
    "email": "support@tekrislabs.com",
    "url": "https://tekrislabs.com/"
  },
  "homepage": "https://tekrislabs.com/",
  "keywords": [
    "rag",
    "genai",
    "retrieval",
    "embeddings",
    "groundedness",
    "citations",
    "evaluation",
    "rag-security",
    "observability",
    "agents"
  ],
  "skills": "./skills/",
  "extensions": {
    "com.openai": {
      "interface": {
        "displayName": "RAG & GenAI Copilot",
        "shortDescription": "Build and debug reliable RAG",
        "longDescription": "Build, inspect, improve, evaluate and secure retrieval-augmented generation systems using focused production workflows. Diagnose ingestion, parsing, indexing, retrieval, reranking, grounded answers, citations and access-control failures. Design measurable evaluation sets, reliability plans, incident playbooks, observability and latency/cost trade-offs. Supports practical coding and real specialist-agent delegation when tools are available. This is a skills-only plugin: it does not independently query private indexes, execute production evaluations, access your repositories or deploy changes.",
        "developerName": "Tekris Labs",
        "category": "Developer Tools",
        "capabilities": [
          "Design RAG architectures with evidence, freshness, authorization, and evaluation boundaries",
          "Build or improve RAG pipelines when coding tools and project access are available",
          "Diagnose ingestion, chunking, indexing, retrieval, reranking, and context failures",
          "Evaluate ranking, grounded answers, citations, abstention, and regressions by slice",
          "Review data isolation, prompt injection, poisoned documents, and access controls",
          "Plan production containment, safe rollback, and incident recovery",
          "Compare vector search, hybrid retrieval, rerankers, models, and trade-offs",
          "Add tracing for answer evidence, latency, cost, failures, and index freshness",
          "Coordinate real specialist agents where supported; otherwise work sequentially",
          "Prepare regression checks and release evidence without inventing test results"
        ],
        "defaultPrompt": [
          "Debug this RAG failure using my retrieval traces and identify the first failing layer.",
          "Design a secure production RAG architecture for this corpus and access model.",
          "Create an evaluation plan for retrieval, grounded answers, citations, and abstention."
        ],
        "brandColor": "#1B2636",
        "logo": "./assets/logo.svg",
        "composerIcon": "./assets/composer-icon.svg",
        "websiteURL": "https://tekrislabs.com/",
        "supportURL": "https://tekrislabs.com/contact",
        "privacyPolicyURL": "https://tekrislabs.com/privacy",
        "termsOfServiceURL": "https://tekrislabs.com/terms"
      },
      "onboardingSkill": "./skills/production-rag-genai-copilot/SKILL.md",
      "publication": {
        "release_notes": "0.2.0: Refactored one broad production RAG skill into 15 focused workflows; expanded technical references, incident/evaluation templates, host-aware agent coordination, benchmark scenarios, and offline validation. Added Tekris Labs publisher and policy URLs. Skills-only; no remote MCP server."
      }
    }
  }
}