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

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

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

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:

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

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 in