NYCU-Chung/cc-statusline

Description and use

**Brief Summary**
NYCU‑Chung/cc‑statusline is an open‑source JavaScript dashboard that visualises Claude Code sessions, quota usage, agent activity, MCP health, message history, and other runtime metrics. It provides a ready‑to‑use UI and API/CLI hooks for exposing these signals, making it easy to monitor and debug AI‑assistant deployments.

**Value**

• **Unified observability**: Collates session details, resource quotas, and health checks in a single pane, eliminating the need for ad‑hoc logging or custom dashboards.
• **Standardised integration**: Implements the Model Context Protocol (MCP), enabling any AI assistant that speaks MCP to plug into real‑world tools and data sources without bespoke glue code.
• **Accelerates development cycles**: Teams can instantly see how agents interact with external services, spot bottlenecks, and iterate on prompt or tool‑selection logic.

**Practical Adoption Path**

1. **Add the npm package** to your Claude Code or MCP‑enabled project.
2. **Configure the dashboard** via a small JSON/YAML file (API endpoints, quota limits, health‑check URLs).
3. **Run the built‑in CLI or embed the React component** in your internal dev portal to start streaming status updates.
4. **Optional**: Deploy the provided MCP server as a sidecar for production environments, letting multiple agents share the same statusline instance.

**Production Readiness**

• **Active maintenance**: Last commit 2026‑05‑12, regular releases, and a growing community (245 ★, 29 forks).
• **Ecosystem fit**: Works with the standard MCP, has clear API/SDK/CLI surfaces, and is already used in pilot projects.
• **Risk profile**: No glaring licensing or security issues identified, though a final audit of dependencies and maintainer activity is recommended. Overall, the project is mature enough for a serious pilot or production rollout in AI‑assistant pipelines.

Use in a workflow

NYCU-Chung/cc-statusline helps connect AI assistants to real tools and data through a standard protocol.

• connect AI agents to tools
• ship Model Context Protocol servers
• standardize integrations

Limitations and review notes

High for an OSS candidate: recent activity, adoption, and ecosystem signals are strong enough for a serious pilot.