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Anthropic has released 11 open-source plugins that turn Claude into a specialist for roles like sales, legal, finance, and product management. Each plugin bundles skills, slash commands, and connectors to tools like Slack, HubSpot, and Jira, and can be customized to match a company's own workflows.
- Each plugin is file-based (markdown and JSON) with a fixed structure: a manifest, MCP connector config, slash commands for explicit actions, and skills that Claude draws on automatically.
- Eleven plugins cover productivity, sales, customer support, product management, marketing, legal, finance, data, enterprise search, bio-research, and plugin management.
- The plugins are generic starting points meant to be customized by swapping connectors, adding company context, and adjusting workflows, with installation via a Claude Code marketplace command.
This is a repository that provides a single collection of 202 agents, 184 skills, and 105 commands designed to work across seven different coding environments—Claude Code, OpenAI Codex CLI, Cursor, OpenCode, Antigravity CLI, GitHub Copilot, and Pi. Each tool uses a "harness" that loads and runs these components according to its own capabilities, with adapters translating the source Markdown format into whatever format each tool needs.
- The system includes 94 plugins (92 local, 2 external) covering Python, JavaScript, code review, testing, infrastructure, and security work, with installation methods varying by target tool.
- Components are maintained once in Markdown source format, then automatically adapted to each harness's requirements—model aliases, tool permissions, and command formats differ by platform.
- Installation ranges from simple marketplace commands (Claude Code, Cursor) to cloning the repo and running make commands (OpenCode, Antigravity, GitHub Copilot, Pi), with individual skills installable via gh skill or npx.
The 15-page PDF lays out a four-step loop—write, consolidate, recall, apply—to give AI agents a persistent memory. Agents log each attempt, distill lessons into reusable insights, review those insights before new tasks, and skip past dead ends. Anthropic engineers use this approach in Claude Code to boost agent performance.
- A 15-page PDF from a senior Anthropic engineer outlines a four-step memory loop for AI agents: Write, Consolidate, Recall, Apply
- The system distills messy task logs into a handful of reusable lessons rather than replaying full transcripts
- Agents use these distilled lessons to skip previously failed approaches even on new tasks
- Anthropic has already integrated this loop into Claude Code, with setup scripts available to inspect