vardoger-analyze

by github

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… npx skills add https://github.com/github/awesome-copilot --skill vardoger-analyze Download ZIPGitHub36.2k

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.

Workflow

• Verify the vardoger CLI is installed and fail fast with install guidance if not.

• Check staleness with vardoger status --platform copilot --json and stop early if the personalization is still fresh.

• Get batch metadata with vardoger prepare --platform copilot to learn the number of batches.

• For each batch, run vardoger prepare --platform copilot --batch <N> and write a concise bullet summary of the behavioral signals.

• Get the synthesis prompt with vardoger prepare --platform copilot --synthesize.

• Synthesize all batch summaries into a single personalization following the synthesis prompt.

• Write the result by piping the personalization into vardoger write --platform copilot --scope global (or --scope project --project <path>).

• 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 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 instructions for an AI assistant.

### 7. Write the result

 Pipe your personalization to `vardoger`:

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".