Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/dstrupl/vardoger/analyzenpx skills add dstrupl/vardoger --skill analyzegit clone --depth 1 https://github.com/dstrupl/vardogerWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00041 | $0.00943 |
| Opus 5 | $0.00020 | $0.00472 |
| Sonnet 5 | $0.00008 | $0.00189 |
| Haiku 4.5 | $0.00004 | $0.00094 |
Grade A, and why
analyze scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- vardoger-analyze — 94% identical, 16 lines differ
How it starts
The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyze conversation history and generate personalized instructions
Use this skill to read your Claude Code conversation history, extract behavioral patterns, and generate personalized instructions that help the assistant better understand your preferences and working style.
How it works
vardoger prepares your conversation history in batches. You (the assistant) summarize each batch for behavioral signals, then synthesize all summaries into a personalization. vardoger writes the result.
Sandbox note (read before running any command)
vardoger reads and writes files outside the current workspace:
- Reads conversation history from the platform's session directory (e.g.
~/.codex/sessions/,~/.claude/projects/,~/.cursor/projects/,~/.openclaw/agents/). - Writes a checkpoint state file to
~/.vardoger/state.json(created on first run). - Writes the final personalization to the platform's rules file (e.g.
~/.codex/AGENTS.md,~/.claude/rules/vardoger.md,.cursor/rules/vardoger.md,~/.openclaw/skills/vardoger-personalization/SKILL.md).
When the host asks to approve a vardoger command, approve it with 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.
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 conversation 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
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 112 lines · 41 tokens per session scan A f8b6b0f57535
analyze is a skill published in the GitHub repository dstrupl/vardoger (4 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 41 tokens to every session and 943 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…