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ebartiebarti/skills/ai-context-engine-production/SKILL.md

ai-context-engine-production

Skill1 starsChanged 6 months agoMITView on GitHub

Production deployment of the Context Engine: moderation gatekeepers (two-stage protocol), policy-driven meta-control (the 5 principles), reusable control-deck templates, domain adaptation (legal + marketing case studies), API + worker + Docker + observability deployment topology, and business-value framing for stakeholders.

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Files
SKILL.md

AI Context Engine Production

Knowledge from "Context Engineering for Multi-Agent Systems" (Chapters 8-10). The safeguards, applications, and deployment topology that take the engine to production.

Quick Start

  1. Check guidelines.md to find which files to load
  2. Load only relevant files (each topic has knowledge.md, rules.md, examples.md)
  3. Apply guidance to your work

Contents

References

CategoryPurpose
moderationTwo-stage moderation protocol, gatekeeper integration, fail-safe
policy-driven-controlThe 5 principles, policy as outermost context layer
control-decksReusable templates: high-fidelity RAG / context reduction / grounded reasoning
domain-applicationsLegal compliance + strategic marketing case studies
production-deploymentFastAPI orchestration, async workers, Docker, observability
business-value5 enterprise capabilities, ROI framing, stakeholder messaging

Workflows

WorkflowPurpose
workflows/add-moderation.mdWire two-stage moderation gatekeeper into the engine
workflows/adapt-to-domain.mdAdapt the engine to a new vertical (legal, medical, marketing, etc.)
workflows/deploy-to-production.mdFull go-live (env → API → workers → Docker → observability)

Guidelines

See guidelines.md for task-based file selection.

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