# Ceap Enterprise Context

> Use for CEAP enterprise context aggregation, engineering memory, knowledge graphs, cross-system analysis, integrations, D365 or ERP context, documentation intelligence, and workflow observation.

- Skill: `rweisssieker-xp/ceap-enterprise-context` (Agent Skill)
- Install (CLI): `npx skillmds@latest add rweisssieker-xp/ceap-enterprise-context`
- Raw SKILL.md: https://api.skillmd.com/api/skills/rweisssieker-xp/ceap-enterprise-context/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Docs & Writing
- Author: rweisssieker-xp (https://skillmd.com/u/rweisssieker-xp)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/rweisssieker-xp/ceap-enterprise-context

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# CEAP Enterprise Context

Use this skill when the task depends on organizational context across repositories, tickets, documents, communication, logs, or enterprise systems.

## Workflow

1. Define context boundaries.
   - Identify which systems matter: repositories, tickets, PRs, docs, logs, Teams, SharePoint, Confluence, Jira, Azure DevOps, GitHub, ServiceNow, ERP, D365, observability, cloud, or CI/CD.
   - Capture privacy, governance, tenant, and retention constraints before using sensitive sources.

2. Build a context graph.
   - Connect tickets, code, APIs, services, data flows, incidents, deployments, architectural decisions, and historical reviews.
   - Reuse existing engineering memory, playbooks, team conventions, and recurring solution patterns.
   - Distinguish verified facts from inferred relationships.

3. Analyze cross-system impact.
   - Look for dependency chains, integration contracts, critical paths, hidden process coupling, recurring failure patterns, and operational risk.
   - Correlate tickets, logs, incidents, PRs, and documentation.
   - Surface gaps where missing data prevents a confident recommendation.

4. Produce reusable memory.
   - Summarize durable decisions, patterns, root causes, and playbooks in a form that can be reused.
   - Avoid storing secrets, unnecessary personal data, or unapproved sensitive details.

## Feature Coverage

- Engineering memory, long-term context, knowledge graphs, semantic search, document analysis, and context compression
- Historical PR and review analysis, team-convention learning, architecture decision history, incident correlation, and reusable playbooks
- GitHub, Azure DevOps, Jira, ServiceNow, Teams, SharePoint, Confluence, D365, ERP, SIEM, CI/CD, MCP, GitLab, Kubernetes, Docker, cloud, observability, SAP, REST, GraphQL, event streaming, OpenTelemetry, LangGraph, and LangChain integrations
- Workflow observation, IDE activity analysis, build and debugging detection, browser-context analysis, OCR-based screen analysis, pattern extraction, process mining, and productivity analysis

## Output Shape

Return:

- Context sources used
- Verified facts
- Inferred relationships
- Impact map
- Missing context
- Reusable memory entries