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 skills add dstrupl/vardoger --skill vardoger-analyzegit clone --depth 1 https://github.com/dstrupl/vardogerWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/dstrupl/vardoger/vardoger-analyze)<a href="https://agentmods.dev/skills/dstrupl/vardoger/vardoger-analyze"><img src="https://agentmods.dev/badge/skills/dstrupl/vardoger/vardoger-analyze/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/dstrupl/vardoger/vardoger-analyze"><img src="https://agentmods.dev/badge/skills/dstrupl/vardoger/vardoger-analyze.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00043 | $0.00946 |
| Opus 5.5 | $0.00017 | $0.00378 |
| Sonnet 5.5 | $0.00009 | $0.00189 |
| Haiku 4.5 | $0.00004 | $0.00095 |
Grade A, and why
vardoger-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 1mo 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.
This is a copy
94% identical to analyze — 16 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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 Windsurf 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 supported session or export directory (e.g.
~/.codex/sessions/,~/.claude/projects/,~/.cursor/projects/,~/.vardoger/imports/devin/). - 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.mdc,~/.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.
- 1mo ago Changed 62f0dea9341b
- 1mo ago First seen · 112 lines · 43 tokens per session scan A 32ddfd875dd0
vardoger-analyze is a skill published in the GitHub repository dstrupl/vardoger (5 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 43 tokens to every session and 946 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 94% identical to analyze, differing in 16 lines, and is treated as a copy.
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