vardoger-analyze

# 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. Report back to the user what was written, where, and that the write is idempotent. ## Steps ### 1. Verify vardoger is installed ```bash if ! command -v vardoger >/dev/null 2>&1; then cat <<'INSTALL_EOF' vardoger CLI is not installed. This skill calls the `vardoger` CLI to read your Copilot CLI history and write a personalization file, so the CLI must be on PATH. Install options: # Recommended: pipx install vardoger # Or run without installing: uvx vardoger --help If you do not have pipx, see https://pipx.pypa.io/stable/installation/. Project page: https://github.com/dstrupl/vardoger After installing, re-run the personalization request. INSTALL_EOF exit 1 fi ``` ### 2. Check if a refresh is needed ```bash vardoger status --platform copilot --json ``` 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. ### 3. Get batch metadata ```bash vardoger prepare --platform copilot ``` This prints JSON like `{"batches": 3, "total_conversations": 29}`. Note the number of batches. Tell the user: "Found N conversations in M batches. Analyzing..." ### 4. Summarize each batch For each batch number from 1 to N, run: ```bash vardoger prepare --platform copilot --batch 1 ``` 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.). ### 5. Get the synthesis prompt ```bash vardoger prepare --platform copilot --synthesize ``` ### 6. Synthesize the personalization 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. ### 7. Write the result Pipe your personalization to `vardoger`: ```bash echo "YOUR_PERSONALIZATION_HERE" | vardoger write --platform copilot --scope global ``` 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. ### 8. Report to the user 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). ## When to use - When the user asks to personalize their Copilot CLI assistant. - When the user asks to analyze their Copilot CLI conversation history. - When the user mentions "vardoger".