Agent skill · software engineering · github
vardoger-analyze
Use when the user asks to personalize the GitHub Copilot CLI assistant, adapt Copilot to their style, use vardoger, or analyze their Copilot CLI conversation history. Reads the local session directory at `~/.copilot/session-state/`, extracts recurring preferences and conventions, and writes a fenced personalization block into `~/.copilot/copilot-instructions.md`. Runs entirely on the user's machine via the local `vardoger` CLI (`pipx install vardoger`); no network calls and no uploads. Triggers: 'personalize my copilot', 'analyze my copilot history', 'tailor copilot to me', 'run vardoger', 'update my copilot instructions from history', 'make copilot learn my style'.
Why this skill is useful
Adds a complete executable workflow for personalizing GitHub Copilot using the vardoger CLI, which includes specific commands and file handling not commonly known.
What it needs
Requires pipx installed locally. About 3k tokens when loaded. Last updated 2026-08-07. 37,534 stars on the source repository.
What this skill does
Analyze Copilot CLI history and generate personalized instructions Drive the local vardoger CLI to read the user's GitHub Copilot CLI conversation history, extract behavioral patterns, and write a personalization block into ~/.copilot/copilot-instructions.md. How it works vardoger prepares the history in batches. You (the assistant) summarize each batch for behavioral signals, then synthesize all summaries into a final personalization. vardoger writes the result, fenced by <!-- vardoger:start --> / <!-- vardoger:end --> markers so any hand-authored rules in the same file are preserved. Sandbox note (read before running any command) vardoger reads and writes files outside the current workspace: Reads Copilot CLI history from ~/.copilot/session-state/. Writes a checkpoint state file to ~/.vardoger/state.json (created on first run). Writes the final personalization to ~/.copilot/copilot-instructions.md. When the host asks to approve a vardoger command, grant it write access beyond the workspace. Otherwise the first vardoger prepare call will fail with PermissionError: ... ~/.vardoger/state.tmp because the sandbox blocks writes outside the current working directory. Workflow 1. Verify the vardoger CLI is installed and fail fast with install guidance if not. 2. Check staleness with vardoger status --platform copilot --json and stop early if the personalization is still fresh. 3. Get batch metadata with vardoger prepare --platform copilot to learn the number of batches. 4. For each batch, run vardoger prepare --platform copilot --batch <N> and write a concise bullet summary of the behavioral signals. 5. Get the synthesis prompt with vardoger prepare --platform copilot --synthesize. 6. Synthesize all batch summaries into a single personalization following the synthesis prompt. 7. Write the result by piping the personalization into vardoger write --platform copilot --scope global (or --scope project --project <path>). 8. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills github/vardoger-analyze