What makes Ml Visualizer notable?

1.1K downloads. Trust evidence available

How should Ml Visualizer 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

Ml Visualizer

Visual analysis and diagnostic tools to help machine learning model selection. ml-visualizer, python, anaconda, estimator, machine-learning, matplotlib. Skill: Ml Visualizer Owner: bytesagain-lab Summary: Visual analysis and diagnostic tools to help machine learning model selection. ml-visualizer, python, anaconda, estimator, machine-learning, matplotlib. Tags: latest:1.0.0 Version history: v1.0.0 | 2026-03-20T01:19:05.301Z | user publish v1.0.0 Archive index: Archive v1.0.0: 4 files, 5589 bytes Files: scripts/script.sh (11397b), skill-card.md (2085b), SKILL.md (4450

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.1k

Updated

Oct 11, 2026

Version

1.0.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1.1K 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: 1.1K downloadsadoption · observed Oct 11, 2026
Latest release: 1.0.0release · observed Mar 20, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s174pq5f3gzez3838vt2642fmx848f1d:ml-visualizer

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-bytesagain-lab-ml-visualizer/snapshot"

Documentation

CLAWHUB

7,170 characters of source documentation, loaded on request.

Extracted files

3 files captured from the source.

SKILL.md

---
version: "1.0.0"
name: Yellowbrick
description: "Visual analysis and diagnostic tools to help machine learning model selection. ml-visualizer, python, anaconda, estimator, machine-learning, matplotlib."
---

# ML Visualizer

A data toolkit for ingesting, transforming, querying, and visualizing machine learning datasets. Manage your entire data pipeline — from raw ingestion through profiling and validation — all from the command line.

## Commands

| Command | Description |
|---------|-------------|
| `ml-visualizer ingest <input>` | Ingest raw data or record a data source entry |
| `ml-visualizer transform <input>` | Log a data transformation step or operation |
| `ml-visualizer query <input>` | Record a query against your dataset |
| `ml-visualizer filter <input>` | Log a filter operation applied to data |
| `ml-visualizer aggregate <input>` | Record an aggregation or rollup operation |
| `ml-visualizer visualize <input>` | Log a visualization request or chart specification |
| `ml-visualizer export <input>` | Record an export operation or export all data |
| `ml-visualizer sample <input>` | Log a data sampling operation |
| `ml-visualizer schema <input>` | Record or describe a data schema |
| `ml-visualizer validate <input>` | Log a data validation check |
| `ml-visualizer pipeline <input>` | Record a full pipeline definition or step |
| `ml-visualizer profile <input>` | Log a data profiling run |
| `ml-visualizer stats` | Show summary statistics across all entry types |
| `ml-visualizer export <fmt>` | Export all data (formats: `json`, `csv`, `txt`) |
| `ml-visualizer search <term>` | Search across all entries by keyword |
| `ml-visualizer recent` | Show the 20 most recent activity log entries |
| `ml-visualizer status` | Health check — version, disk usage, last activity |
| `ml-visualizer help` | Show the built-in help message |
| `ml-visualizer version` | Print the current version (v2.0.0) |

Each data command (ingest, transform, query, etc.) works in two modes:
- **Without arguments** — displays the 20 most recent entries of that type
- **With arguments** — saves the input as a new timestamped entry

## Data Storage

All data is stored as plain-text log files in `~/.local/share/ml-visualizer/`:

- Each command type gets its own log file (e.g., `ingest.log`, `transform.log`, `visualize.log`)
- Entries are stored in `timestamp|value` format for easy parsing
- A unified `history.log` tracks all activity across command types
- Export to JSON, CSV, or TXT at any time with the `export` command

Set the `ML_VISUALIZER_DIR` environment variable to override the default data directory.

## Requirements

- Bash 4.0+ (uses `set -euo pipefail`)
- Standard Unix utilities: `date`, `wc`, `du`, `tail`, `grep`, `sed`, `cat`
- No external dependencies or API keys required

## When to Use

1. **Building a data pipeline journal** — use `ingest`, `transform`, and `pipeline` to document each step of your ML data preparation workflow
2. **Tracking data quality

_meta.json

{
  "ownerId": "kn76vqrf94wk924mddj8fp4p5x8497tb",
  "slug": "ml-visualizer",
  "version": "1.0.0",
  "publishedAt": 1773969545301
}

skill-card.md

## Description:

A command-line activity journal for recording and exporting machine learning data pipeline, schema, validation, profiling, and visualization notes.

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

## Publisher:

[bytesagain-lab](https://clawhub.ai/user/bytesagain-lab)

### License/Terms of Use:

MIT-0

## Use Case:

Developers and engineers can use this skill to log ML data preparation, validation, profiling, and visualization activity from the command line. It is best treated as a local workflow journal rather than a real visualization or model-selection engine.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: The release evidence flags this skill as suspicious because it is presented as an ML visualization or model-selection tool while behaving as a local plaintext logging tool.

Mitigation: Review the artifact before installing and set user expectations that it records notes and command activity rather than generating model diagnostics.

Risk: Users may enter secrets, customer data, private dataset paths, proprietary schema details, credentials, or sensitive model notes into logs stored in plaintext.

Mitigation: Avoid entering sensitive information and redirect ML_VISUALIZER_DIR to an approved local location with appropriate access controls when logging is necessary.

## Reference(s):


## Skill Output:

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

**Output Format:** [Plain text command output with optional JSON, CSV, or TXT exports]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [Writes plaintext logs under ~/.local/share/ml-visualizer by default; ML_VISUALIZER_DIR can override the data directory.]

## Skill Version(s):

1.0.0 (source: release evidence 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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