microsoft-SkillOpt/plugins/copilot/skillopt

SkillOpt — GitHub Copilot integration

Give Copilot (CLI or VS Code) direct access to the SkillOpt research engine via a tiny MCP server. MCP is GitHub's supported way to extend Copilot, so this works across Copilot CLI, VS Code, and other MCP clients with the same server.

SkillOpt is validation-gated, text-space skill optimization: it reflects on rollouts, makes bounded edits to a skill, and keeps a change only if it improves a held-out validation set. This plugin exposes the repo's training and eval entry points (scripts/train.py, scripts/eval_only.py) as Copilot tools.

This is the companion to the SkillOpt-Sleep plugin (../mcp_server.py, sleep_* tools). Sleep evolves a local coding agent from your past sessions; this server drives the research training/eval loops on the benchmark configs in ../../../configs.

What's here

File	Purpose
mcp_server.py	stdlib-only MCP (stdio) server exposing skillopt_* tools
mcp-config.example.json	drop-in MCP server config
copilot-instructions.snippet.md	paste into .github/copilot-instructions.md

Install

Requires Python ≥ 3.10. The MCP server itself is pure stdlib, but the tools it launches need SkillOpt's runtime deps — install the package first:

pip install -e .   # or: pip install -r requirements.txt

1. Register the MCP server. Add the server to your Copilot MCP config (Copilot CLI: ~/.copilot/mcp-config.json; VS Code: your MCP settings). Use mcp-config.example.json as a template — set SKILLOPT_REPO to this repo's path:

   {
     "mcpServers": {
       "skillopt": {
         "command": "python3",
         "args": ["/abs/path/SkillOpt/plugins/copilot/skillopt/mcp_server.py"],
         "env": { "SKILLOPT_REPO": "/abs/path/SkillOpt" }
       }
     }
   }
   

2. (Optional) Tell Copilot about it. Append copilot-instructions.snippet.md to your repo's .github/copilot-instructions.md so Copilot reaches for the tools when the user asks to "optimize a skill" or "train on a benchmark".

Use

Ask Copilot things like "what configs can I run?", "optimize the searchqa skill", or "evaluate this skill on the dataset". Copilot calls the MCP tools: skillopt_list_configs, skillopt_train, skillopt_eval.

Tool	Required args	Notes
skillopt_list_configs	—	Lists configs/**/*.yaml you can pass as config.
skillopt_train	config	Runs a reflective optimization loop. Long-running; spends budget.
skillopt_eval	config, skill	Evaluates one skill markdown file; no training.

Common optional args (both train and eval): env, backend, optimizer_model, target_model, out_root, cfg_options (space-separated KEY=VALUE YAML overrides), and extra_args (raw passthrough flags for the underlying script). skillopt_train also accepts num_epochs, batch_size, seed, and use_gate.

Runs can be very long. The server's subprocess timeout defaults to 6 hours; override it with the SKILLOPT_RUN_TIMEOUT environment variable (seconds).

Verify the server directly (no Copilot needed)

printf '%s\n' \
  '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{}}' \
  '{"jsonrpc":"2.0","id":2,"method":"tools/list"}' \
  '{"jsonrpc":"2.0","id":3,"method":"tools/call","params":{"name":"skillopt_list_configs","arguments":{}}}' \
  | SKILLOPT_REPO="$(pwd)" python3 plugins/copilot/skillopt/mcp_server.py

You should see the server info, the three skillopt_* tools, and the list of benchmark configs.

Notes / status

• MCP is the stable, official Copilot extension surface, so this is portable across Copilot CLI and IDE from one server.
• skillopt_list_configs is filesystem-only and safe to call anytime; skillopt_train / skillopt_eval shell out to the repo scripts and require the SkillOpt runtime deps (and, for real backends, model credentials — see ../../../.env.example).