What makes vardoger — Analyze History notable?

1.2K downloads. Trust evidence available

How should vardoger — Analyze History be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this agent.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

agentCLAWHUBUnverified

vardoger — Analyze History

Use when the user asks to personalize their assistant, to use vardoger, or to analyze their OpenClaw conversation history. Runs the vardoger CLI to read past... Skill: vardoger — Analyze History Owner: dstrupl Summary: Use when the user asks to personalize their assistant, to use vardoger, or to analyze their OpenClaw conversation history. Runs the vardoger CLI to read past... Tags: latest:0.3.2 Version history: v0.3.2 | 2026-07-09T20:09:41.866Z | user Update the skill for vardoger 0.3.2 and current platform support. v0.3.1 | 2026-04-24T19:02:00.613Z | user Add explicit Apac

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.2k

Updated

Oct 11, 2026

Version

0.3.2

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.2K downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1.2K downloadsadoption · observed Oct 11, 2026
Latest release: 0.3.2release · observed Jul 9, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s170q73w5hdz7rfwgk64d9c4n985b7g5:vardoger-analyze

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-dstrupl-vardoger-analyze/snapshot"

Documentation

CLAWHUB

18,733 characters of source documentation, loaded on request.

Extracted files

3 files captured from the source.

SKILL.md

---
name: analyze
description: "Use when the user asks to personalize their assistant, to use vardoger, or to analyze their OpenClaw conversation history. Runs the vardoger CLI to read past conversations and generate tailored instructions."
version: "0.3.2"
license: Apache-2.0
homepage: "https://github.com/dstrupl/vardoger"
metadata:
openclaw:
requires:
bins:
- vardoger
---
# Analyze conversation history and generate personalized instructions

Use this skill to read your OpenClaw 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

```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 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

```bash
vardoger status --platform openclaw --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

```bash
vardoger prepare --platform openclaw
```

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 openclaw --

_meta.json

{
  "ownerId": "kn72ffy5f0ytdypch2d4qxw1cd85b96j",
  "slug": "vardoger-analyze",
  "version": "0.3.2",
  "publishedAt": 1783627781866
}

skill-card.md

## Description:

Use when the user asks to personalize their assistant, to use vardoger, or to analyze their OpenClaw conversation history.

This skill is ready for commercial/non-commercial use.

## Publisher:

[dstrupl](https://clawhub.ai/user/dstrupl)

### License/Terms of Use:

MIT-0

## Use Case:

Developers and assistant users use this skill to analyze OpenClaw conversation history with the vardoger CLI and generate persistent personalization instructions for assistant behavior.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: The skill reads private conversation history.

Mitigation: Run it only after explicit user consent and only in trusted local environments.

Risk: The skill writes persistent assistant rules that can change future assistant behavior.

Mitigation: Review the generated markdown before writing it and keep a rollback path for the previous rules file.

Risk: The provided echo-based write example can mishandle generated markdown.

Mitigation: Use a safer stdin or file-based mechanism when passing personalization content to vardoger.

Risk: The vardoger commands need filesystem access outside the workspace.

Mitigation: Use a pinned, reviewed vardoger version and grant expanded file access only for the specific command being run.

## Reference(s):

- [ClawHub skill page](https://clawhub.ai/dstrupl/skills/vardoger-analyze)
- [dstrupl publisher profile](https://clawhub.ai/user/dstrupl)
- [Vardoger project page](https://github.com/dstrupl/vardoger)
- [pipx installation documentation](https://pipx.pypa.io/stable/installation/)

## Skill Output:

**Output Type(s):** [text, markdown, shell commands, configuration, guidance]

**Output Format:** [Markdown personalization guidance with shell commands and concise status messages]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Requires the vardoger CLI and explicit user consent before reading conversation history or writing persistent assistant rules.]

## Skill Version(s):

0.3.2 (source: server release metadata and SKILL.md frontmatter)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

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