Extending RaiSE

RaiSE is designed to be extended. There are four extension points, each serving a different purpose.

Adapters

Adapters connect RaiSE to external services — project management tools, documentation platforms, and knowledge graph backends. They follow Python Protocol contracts with entry point discovery.

RaiSE ships with adapters for Jira and Confluence. You can add your own for any service.

Entry point groups:

rai.adapters.pm — project management (issues, sprints, backlog)
rai.docs.targets — documentation publishing (pages, search)
rai.governance.schemas — governance artifact type definitions
rai.governance.parsers — governance artifact parsers
rai.graph.backends — knowledge graph storage

Create a custom adapter

Skills

Skills are structured instructions that guide your AI assistant through repeatable workflows. They are SKILL.md files with YAML frontmatter and step-by-step sections.

RaiSE ships with lifecycle skills (session, epic, story) and utility skills (debug, research). You can create project-specific skills for your team’s workflows.

Create a custom skill

MCP Servers

MCP (Model Context Protocol) servers give your AI assistant access to external tools — documentation lookups, security scanners, repository operations. RaiSE manages MCP server registration and health checking.

Lifecycle Hooks

Hooks are shell commands that run on Claude Code events — before tool use, after tool use, or before context compaction. They enable side effects like journal logging without blocking the main workflow.

Wire a lifecycle hook

Validation

After extending RaiSE, verify your setup:

# Check adapter registration and Protocol compliance
rai adapter check

# Validate a declarative adapter config
rai adapter validate .raise/adapters/my-adapter.yaml

# Check skill structure
rai skill validate my-skill

# Verify MCP server health
rai mcp health my-server