Quick reference
| Action | Command (typed in Claude session) |
|---|---|
| Create team | create-team <name> |
| Join team | join-team <name> [approver_email] |
| Approve member | approve-join <team> <otp> <email> |
| Push to team | push-rules <team> |
| Sync from team | sync-rules <team> |
The problem at scale
Each coding agent has its own local memory — but none of them talk to each other. When multiple agents run in parallel across your organization, Engineer A’s agent enforces one set of conventions while Engineer B’s enforces another. The context is there, it’s just siloed.The --read-only flag
When you connect with --read-only, the agent can read from the graph but cannot write to it:
- A team lead or platform engineer maintains the canonical Context Graph with all conventions, patterns, and policies
- Individual engineers connect to it in read-only mode
- Every agent session inherits the same standards without anyone being able to accidentally overwrite them
Centralized policy, distributed agents
The Context Graph acts as shared engineering memory. The workflow:Define your standards
A team lead stores conventions in a Context Graph — naming rules, component patterns, architectural decisions. This can be done via the dashboard or automatically through the CLI with
npx rippletide-code.Connect every engineer
Each engineer runs the connect command in read-only mode in their project:Their coding agent now has access to the full set of team standards.
Update once, propagate everywhere
When a convention changes (e.g., “we’re switching from REST to tRPC”), the team lead updates the graph. Every active agent session picks up the change on the next
recall or get_context call. No PRs to update linting configs. No Slack messages hoping everyone reads them.