npx skills add ...
npx skills add github/awesome-copilot --skill vardoger-analyze
npx skills add github/awesome-copilot --skill 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'.
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.
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.
vardoger reads and writes files outside the current workspace:
~/.copilot/session-state/.~/.vardoger/state.json (created on first run).~/.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.
vardoger CLI is installed and fail fast with install guidance if not.vardoger status --platform copilot --json and stop early if the personalization is still fresh.vardoger prepare --platform copilot to learn the number of batches.vardoger prepare --platform copilot --batch <N> and write a concise bullet summary of the behavioral signals.vardoger prepare --platform copilot --synthesize.vardoger write --platform copilot --scope global (or --scope project --project <path>).If the output shows "is_stale": false, tell the user their personalization is up to date and ask if they want to re-run anyway. If stale or never generated, continue with the analysis.
This prints JSON like {"batches": 3, "total_conversations": 29}. Note the number of batches. Tell the user: "Found N conversations in M batches. Analyzing..."
For each batch number from 1 to N, run:
The output contains a summarization prompt followed by conversation data. Read the output carefully and produce a concise bullet-point summary of the behavioral signals you observe in that batch. Keep your summary for later.
Tell the user which batch you are processing: "Analyzing batch 1 of N..."
Repeat for all batches (--batch 2, --batch 3, etc.).
Following the synthesis prompt, combine all your batch summaries into a single personalization. The output should be clean markdown with actionable instructions for an AI assistant.
Pipe your personalization to vardoger:
Replace YOUR_PERSONALIZATION_HERE with the actual personalization markdown you generated. --scope global writes to ~/.copilot/copilot-instructions.md; use --scope project --project <path> to scope the write to a specific repository instead.
Tell the user what was written and where. Mention they can ask you to re-run vardoger any time to update the personalization, and that writes are idempotent (the fenced block is replaced; anything outside it is preserved).
vardoger status --platform copilot --jsonvardoger prepare --platform copilotvardoger prepare --platform copilot --batch 1vardoger prepare --platform copilot --synthesizeecho "YOUR_PERSONALIZATION_HERE" | vardoger write --platform copilot --scope global