New: 97,000+ skills indexed from GitHub repos with 100+ stars · every skill safety-checkedBrowse top skillsTop skills
Esc to closeBrowse all skills
Submit a skillBrowse skills
Submit a skillBrowse skills

Official agent skill

Vardoger Analyze

by github in github/awesome-copilot

A skill your agent uses 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.

OfficialApache-2.0Auto-check passedAgent Workflows

Install Vardoger Analyze

skills CLI
$ npx skills add github/awesome-copilot --skill vardoger-analyze -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install github/awesome-copilot vardoger-analyze --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/github/awesome-copilot.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/vardoger-analyze .claude/skills/vardoger-analyze && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
vardoger-analyze
GitHub stars
40k
Used in
1 other repo
Token cost
~1.4k tokens
SKILL.md length
565 words
Files
1
Skills in repo
417
Repo updated
First seen
Licence
Apache-2.0

At a glance

A skill your agent uses 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.

  • Works in 8 steps: Verify vardoger is installed → Check if a refresh is needed → Get batch metadata → …
  • The user asks to personalize the GitHub Copilot CLI assistant
  • SKILL.md covers How it works, Sandbox note (read before…, Workflow and Steps, plus 1 more section
  • Calls pipx and uvx; reaches pipx.pypa.io and github.com

What it does

Vardoger Analyze is an agent skill from github/awesome-copilot, published by the product's own GitHub organization. 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'…

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Agent Workflows, covering Agent instruction files and Session handoff. The repository describes itself as: Community-contributed instructions, agents, skills, and configurations to help you make the most of GitHub Copilot. The licence is Apache-2.0.

When your agent uses it

  • The user asks to personalize the GitHub Copilot CLI assistant
  • Adapt Copilot to their style
  • Analyze their Copilot CLI conversation history

Example prompts

  • “personalize my copilot”
  • “analyze my copilot history”
  • “tailor copilot to me”
  • “/vardoger-analyze”

Workflow steps

8 steps, taken from the step headings in SKILL.md.

  1. Verify vardoger is installed
  2. Check if a refresh is needed
  3. Get batch metadata
  4. Summarize each batch
  5. Get the synthesis prompt
  6. Synthesize the personalization
  7. Write the result
  8. Report to the user

What it can do on your machine

Read from SKILL.md and the folder at commit 727ff2e. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • pipx
    • uvx

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • pipx.pypa.io
    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Vardoger Analyze loads about 1.4k tokens when it runs. Until then it costs about 173 tokens; SKILL.md has 565 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~173
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from github/awesome-copilot at commit 727ff2e, republished under its Apache-2.0 licence (© github). 565 words, ~1,408 tokens.

Download SKILL.mdSave it as .claude/skills/vardoger-analyze/SKILL.md (or your agent's skills folder).
name
vardoger-analyze
description
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'.
license
Apache-2.0

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.

Show full SKILL.md (239 more words)Show less
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".

© github, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/vardoger-analyze of github/awesome-copilot.

Open the folder on GitHubat commit 727ff2e

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in github/awesome-copilot, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Vardoger Analyze next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Vardoger Analyze compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Vardoger Analyze this skillgithub/awesome-copilot40k1 repos~1.4kAutomated safety check: PassApache-2.0
Harness Engineering10xChengTu/harness-engineering1021 repos~1kAutomated safety check: PassNone
CLAUDE.md PreserveEliaAlberti/cpr-compress-preserve-resume513—~889Automated safety check: PassMIT
Orca CLIstablyai/orca87k2 repos~593Automated safety check: PassMIT
Using Agent Skillsaddyosmani/agent-skills102k4 repos~2.4kAutomated safety check: PassMIT
Claude ReflectBayramAnnakov/claude-reflect1.7k2 repos~627Automated safety check: PassMIT

Similar skills

  • Harness Engineering

    10xChengTu/harness-engineering

    Set up and improve harness engineering (AGENTS.md, docs/, lint rules, eval systems, project-level prompt engineering) for AI-agent-friendly codebases.

    102 GitHub starsUsed in 1 repo~1k tokens
    Agent WorkflowsAuto-check passed

  • CLAUDE.md Preserve

    EliaAlberti/cpr-compress-preserve-resume

    Writes this session's durable learnings into the project's CLAUDE.md and keeps the file short by archiving stale sections.

    513 GitHub stars~889 tokensUpdated 28 days ago
    Agent WorkflowsAuto-check passed

  • Orca CLI

    stablyai/orca

    Operate Orca-managed worktrees, folder contexts, terminals, repos, automations, artifacts, skill sharing, worktree comments, and Orca's embedded browser…

    87k GitHub starsUsed in 2 repos~593 tokens
    Agent WorkflowsAuto-check passed

  • Using Agent Skills

    addyosmani/agent-skills

    Meta-skill for choosing which workflow skill fits the task at hand, plus always-on habits: surface assumptions, stop on confusion, push back, keep it simple and stay in scope.

    102k GitHub starsUsed in 4 repos~2.4k tokens
    Agent WorkflowsAuto-check passed

  • Claude Reflect

    BayramAnnakov/claude-reflect

    Self-learning system that captures corrections during sessions and reminds users to run /reflect to update CLAUDE.md.

    1.7k GitHub starsUsed in 2 repos~627 tokens
    Agent WorkflowsAuto-check passed

  • Neat-Freak Knowledge Closeout

    KKKKhazix/khazix-skills

    Brings project docs, agent rule files, authorized memory and leftover workspace files back in line with what the code and runtime actually do at the end of a work session.

    21k GitHub stars~1.9k tokensUpdated 6 days ago
    Agent WorkflowsAuto-check passed

More from github/awesome-copilot

All 417 skills in this repo

  • Acquire Codebase Knowledge

    github/awesome-copilot

    Official

    Maps an unfamiliar codebase into seven evidence-backed documents in docs/codebase/, using a scan script and templates, for onboarding or architecture write-ups.

    40k GitHub starsUsed in 1 repo~2.3k tokens
    Auto-check passed

  • Azure Architecture Autopilot

    github/awesome-copilot

    Official

    Designs Azure infrastructure from a natural-language description, or diagrams an existing resource group, then refines the design through conversation and deploys it with Bicep.

    40k GitHub starsUsed in 1 repo~1.9k tokens
    Auto-check passed

  • Draw.io Diagram Generator

    github/awesome-copilot

    Official

    Generates, edits and validates draw.io files with correct mxGraph XML, covering flowcharts, architecture, sequence, ER and UML class diagrams.

    40k GitHub starsUsed in 1 repo~4.9k tokens
    Auto-check passed

  • Credit Risk Data Cleaning

    github/awesome-copilot

    Official

    Cleans raw credit data and screens variables before loan modeling, dropping unstable, noisy or redundant features and writing an Excel report of every step.

    40k GitHub starsUsed in 1 repo~1.5k tokens
    Auto-check passed

  • Daily Focus Board

    github/awesome-copilot

    Official

    Builds a warm, browser-based daily focus board the user updates by talking to their agent, with Eisenhower priorities, a brain-dump box and kind not-today carryover.

    40k GitHub stars~3k tokensUpdated today
    Auto-check passed

  • Python Pypi Package Builder

    github/awesome-copilot

    Official

    End-to-end skill for building, testing, linting, versioning, and publishing a production-grade Python library to PyPI.

    40k GitHub starsUsed in 1 repo~4.6k tokens
    Auto-check passed

Categories

Questions about Vardoger Analyze

What does Vardoger Analyze do?

A skill your agent uses 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. Vardoger Analyze is an agent skill from github/awesome-copilot, published by the product's own GitHub organization. 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.

When should I use Vardoger Analyze?

Vardoger Analyze fits situations like: the user asks to personalize the GitHub Copilot CLI assistant; adapt Copilot to their style; analyze their Copilot CLI conversation history.

How do I install Vardoger Analyze in Claude Code?

Run `npx skills add github/awesome-copilot --skill vardoger-analyze -a claude-code`. Or copy the skill folder (skills/vardoger-analyze in github/awesome-copilot) into .claude/skills/vardoger-analyze in your project. Claude Code loads it when a task matches its description.

How do I install Vardoger Analyze in Codex?

Run `npx skills add github/awesome-copilot --skill vardoger-analyze -a codex`. Or copy the skill folder (skills/vardoger-analyze in github/awesome-copilot) into .agents/skills/vardoger-analyze in your project. Codex loads it when a task matches its description.

Can I use Vardoger Analyze in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add github/awesome-copilot --skill vardoger-analyze -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/vardoger-analyze, .gemini/skills/vardoger-analyze, .github/skills/vardoger-analyze and .opencode/skills/vardoger-analyze in your project.

What does Vardoger Analyze need to run?

Going by SKILL.md and its folder, Vardoger Analyze needs the command-line tools its instructions call (pipx and uvx).

Does Vardoger Analyze access the network?

SKILL.md names 2 domains. In commands or code: pipx.pypa.io and github.com; the agent is likely to contact these when it follows the instructions. This is read from the text; nothing was executed.

Is Vardoger Analyze safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Vardoger Analyze use?

Vardoger Analyze is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Vardoger Analyze use?

About 1.4k tokens (SKILL.md is roughly 5.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Vardoger Analyze?

Skills that share tags, products or a category with Vardoger Analyze: Harness Engineering (10xChengTu/harness-engineering, 102 stars), CLAUDE.md Preserve (EliaAlberti/cpr-compress-preserve-resume, 513 stars), Orca CLI (stablyai/orca, 87k stars) and Using Agent Skills (addyosmani/agent-skills, 102k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Vardoger Analyze?

github (a GitHub organization, an official publisher) maintains it in github/awesome-copilot, which has 39,748 GitHub stars. The repository holds 417 skills in this directory. The repository was last updated on October 7, 2026.

Source: github/awesome-copilot on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.