link
Eliminates context fragmentation across AI agents by centralizing account access, persistent memory, and custom skills.
Lanes Link is a private, self-hostable MCP that connects your accounts, memory and skills to every AI agent you use.
- Ask Claude to retrieve stored credentials and authenticate API calls without hardcoding secrets.
- Generate code that accesses your saved personal context and project memory across AI conversations.
- Automate multi-step workflows by connecting Claude to your existing tools and integrations.
Eliminates context fragmentation across AI agents by centralizing account access, persistent memory, and custom skills. Reduces manual context re-entry, improves agent coordination, and maintains security through self-hosted deployment.
Engineering teams running multiple AI agents internally who need unified state management and consistent tool access across agents.
https://github.com/lanes-sh/link
By lanes-sh
How to Get It
claude plugins install lanes-sh/link
Tip: Paste this into a Claude Code conversation. Verify command matches your Claude Code version.
Auto-generated from the tool's public listing — not hands-on verified. Cross-check against the source repo's README before running.
After installing, paste this into Claude:
Retrieve stored credentials and authenticate API calls without hardcoding secrets
Trust Signals Auto-scanned
Community Pulse Emerging
Discussed on Hacker News
- One endpoint between your AI and all your connections, memory, skills — Hacker News · 14 pts
- A self-hostable MCP for all your connections, memory, skills, and secrets — Hacker News · 6 pts
2 mentions across 1 sources
Reviewer notes
Auto-scanned review. These are observations, not a security certification.
Scored from trust signals (evidence-eval-v1): 34 GitHub stars; contributors unknown; last commit 0d ago; license Apache-2.0.
Things to check
- Scanned, not hands-on tested — this entry was auto-scanned from public metadata (GitHub metrics, license, security flags). No reviewer has run it, and no tool-specific limitations have been documented yet.
How to evaluate tools before deploying →
Data shown here comes from public APIs and automated scanning. Reviewer notes reflect one person's experience. This is not a security certification or legal recommendation. Always evaluate tools according to your own organization's policies.
Evaluation
Scored from trust signals (evidence-eval-v1): 34 GitHub stars; contributors unknown; last commit 0d ago; license Apache-2.0.