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Shanghua Gao 16af425c05 Claude Code plugin: self-contained layout, skill-based routing, ML demo readiness (#161)
⏺ Introduces the Claude Code plugin for ToolUniverse as the recommended Claude Code
  integration. Replaces the previous MCP-only setup with a one-command install
  (`claude plugin install tooluniverse@tooluniverse`) that auto-configures the MCP
  server, slash commands, sub-agent, hooks, and 115 specialized research skills.

  Plugin structure
  - 115 skills routed via a single visible router (router auto-matches by question
    keywords + file extensions; sub-skills load on demand via Skill('name')).
  - 5 slash commands (research, compare, cross-validate, literature-sweep,
    translate-id) — each enforces a discipline the default agent doesn't apply.
  - 1 sub-agent (researcher) — same investigation as /tooluniverse:research but
    delegates to a forked-context subagent and returns one summary.
  - SessionStart hook (idempotent) that cleans up legacy globally-installed
    ToolUniverse skills so they don't shadow the plugin.

  Skill changes (general, not benchmark-specific)
  - Router gets a single percentage-vs-proportion units rule.
  - Sub-skills get top-of-mind discipline banners: long-format methylation
    ROWS-vs-unique-positions counting; Trimmomatic "reads completely discarded"
    = F + R + 2*D; DEG-count default reads the padj-only line; ClinVar benign-
    proportion 3-tier reporting; ortholog amino-acid single-representative sum;
    raw + log10 sensitivity for count-vs-length Pearson.

  Versions aligned at 1.2.0
  - Plugin manifest + marketplace.json + Python package (`pyproject.toml`) all at
    1.2.0. After merge, tagging v1.2.0 triggers the plugin-release workflow.

  Docs
  - New install + usage page (docs/guide/building_ai_scientists/claude_code.rst)
    covering the two-command install, version pinning, API-key setup, plugin
    troubleshooting, and the manual-MCP fallback.

  Quality measurement
  - BixBench closed-book official protocol (no reference notebook, hardened
    harness with strict leak audit and isolated workspace): 67.3% (LLM-graded).
    Compare to BixBench paper baseline 17% (Claude 3.5 Sonnet).
2026-05-21 13:12:38 -04:00

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{
"permissions": {
"mcp": {
"tooluniverse": {
"autoApprove": [
"find_tools",
"list_tools",
"grep_tools",
"get_tool_info"
]
}
}
}
}