Skill: bytesagain-lab/ml-visualizer (version 1.0.0)

--- 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** — use `validate` and `profile` to log validation checks and profiling runs, ensuring data integrity before model training 3. **Logging visualization requests** — use `visualize` to record what charts and plots you've generated for model diagnostics (confusion matrices, ROC curves, feature importance) 4. **Managing dataset schemas** — use `schema` to document the structure of your datasets, track schema changes over time, and share definitions with your team 5. **Auditing data operations** — use `search`, `recent`, and `stats` to review your complete data processing history and find specific operations ## Examples ```bash # Ingest a new data source ml-visualizer ingest "Loaded training set from s3://ml-data/train.csv — 50,000 rows, 24 features" # Record a transformation step ml-visualizer transform "Applied StandardScaler to numeric columns, one-hot encoded categoricals" # Log a visualization ml-visualizer visualize "Generated confusion matrix for RandomForest classifier — 94% accuracy" # Define a schema entry ml-visualizer schema "users table: id(int), age(int), income(float), segment(str), churn(bool)" # Search past operations ml-visualizer search "StandardScaler" ``` ## Output All commands print results to stdout. Redirect to a file if needed: ```bash ml-visualizer stats > pipeline-report.txt ml-visualizer export json ``` --- Powered by BytesAgain | bytesagain.com | [REDACTED_SECRET]


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