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Manage your entire data pipeline — from raw ingestion through profiling and validation — all from the command line.\n\n## Commands\n\n| Command | Description |\n|---------|-------------|\n| `ml-visualizer ingest <input>` | Ingest raw data or record a data source entry |\n| `ml-visualizer transform <input>` | Log a data transformation step or operation |\n| `ml-visualizer query <input>` | Record a query against your dataset |\n| `ml-visualizer filter <input>` | Log a filter operation applied to data |\n| `ml-visualizer aggregate <input>` | Record an aggregation or rollup operation |\n| `ml-visualizer visualize <input>` | Log a visualization request or chart specification |\n| `ml-visualizer export <input>` | Record an export operation or export all data |\n| `ml-visualizer sample <input>` | Log a data sampling operation |\n| `ml-visualizer schema <input>` | Record or describe a data schema |\n| `ml-visualizer validate <input>` | Log a data validation check |\n| `ml-visualizer pipeline <input>` | Record a full pipeline definition or step |\n| `ml-visualizer profile <input>` | Log a data profiling run |\n| `ml-visualizer stats` | Show summary statistics across all entry types |\n| `ml-visualizer export <fmt>` | Export all data (formats: `json`, `csv`, `txt`) |\n| `ml-visualizer search <term>` | Search across all entries by keyword |\n| `ml-visualizer recent` | Show the 20 most recent activity log entries |\n| `ml-visualizer status` | Health check — version, disk usage, last activity |\n| `ml-visualizer help` | Show the built-in help message |\n| `ml-visualizer version` | Print the current version (v2.0.0) |\n\nEach data command (ingest, transform, query, etc.) works in two modes:\n- **Without arguments** — displays the 20 most recent entries of that type\n- **With arguments** — saves the input as a new timestamped entry\n\n## Data Storage\n\nAll data is stored as plain-text log files in `~/.local/share/ml-visualizer/`:\n\n- Each command type gets its own log file (e.g., `ingest.log`, `transform.log`, `visualize.log`)\n- Entries are stored in `timestamp|value` format for easy parsing\n- A unified `history.log` tracks all activity across command types\n- Export to JSON, CSV, or TXT at any time with the `export` command\n\nSet the `ML_VISUALIZER_DIR` environment variable to override the default data directory.\n\n## Requirements\n\n- Bash 4.0+ (uses `set -euo pipefail`)\n- Standard Unix utilities: `date`, `wc`, `du`, `tail`, `grep`, `sed`, `cat`\n- No external dependencies or API keys required\n\n## When to Use\n\n1. **Building a data pipeline journal** — use `ingest`, `transform`, and `pipeline` to document each step of your ML data preparation workflow\n2. **Tracking data quality** — use `validate` and `profile` to log validation checks and profiling runs, ensuring data integrity before model training\n3. **Logging visualization requests** — use `visualize` to record what charts and plots you've generated for model diagnostics (confusion matrices, ROC curves, feature importance)\n4. **Managing dataset schemas** — use `schema` to document the structure of your datasets, track schema changes over time, and share definitions with your team\n5. **Auditing data operations** — use `search`, `recent`, and `stats` to review your complete data processing history and find specific operations\n\n## Examples\n\n```bash\n# Ingest a new data source\nml-visualizer ingest \"Loaded training set from s3://ml-data/train.csv — 50,000 rows, 24 features\"\n\n# Record a transformation step\nml-visualizer transform \"Applied StandardScaler to numeric columns, one-hot encoded categoricals\"\n\n# Log a visualization\nml-visualizer visualize \"Generated confusion matrix for RandomForest classifier — 94% accuracy\"\n\n# Define a schema entry\nml-visualizer schema \"users table: id(int), age(int), income(float), segment(str), churn(bool)\"\n\n# Search past operations\nml-visualizer search \"StandardScaler\"\n```\n\n## Output\n\nAll commands print results to stdout. Redirect to a file if needed:\n\n```bash\nml-visualizer stats > pipeline-report.txt\nml-visualizer export json\n```\n\n---\n\nPowered by BytesAgain | bytesagain.com | hello@bytesagain.com\n\nFile v1.0.0:_meta.json\n\n{\n  \"ownerId\": \"kn76vqrf94wk924mddj8fp4p5x8497tb\",\n  \"slug\": \"ml-visualizer\",\n  \"version\": \"1.0.0\",\n  \"publishedAt\": 1773969545301\n}\n\nFile v1.0.0:skill-card.md\n\n## Description:\n\nA command-line activity journal for recording and exporting machine learning data pipeline, schema, validation, profiling, and visualization notes.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[bytesagain-lab](https://clawhub.ai/user/bytesagain-lab)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers 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.\n\n### Deployment Geography for Use:\n\nGlobal\n\n## Known Risks and Mitigations:\n\nRisk: 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.\n\nMitigation: Review the artifact before installing and set user expectations that it records notes and command activity rather than generating model diagnostics.\n\nRisk: Users may enter secrets, customer data, private dataset paths, proprietary schema details, credentials, or sensitive model notes into logs stored in plaintext.\n\nMitigation: Avoid entering sensitive information and redirect ML_VISUALIZER_DIR to an approved local location with appropriate access controls when logging is necessary.\n\n## Reference(s):\n\n\n## Skill Output:\n\n**Output Type(s):** [text, shell commands, configuration, guidance]\n\n**Output Format:** [Plain text command output with optional JSON, CSV, or TXT exports]\n\n**Output Parameters:** [1D]\n\n**Other Properties Related to Output:** [Writes plaintext logs under ~/.local/share/ml-visualizer by default; ML_VISUALIZER_DIR can override the data directory.]\n\n## Skill Version(s):\n\n1.0.0 (source: release evidence and SKILL.md frontmatter)\n\n## Ethical Considerations:\n\nUsers 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.","readmeExcerpt":"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. 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Manage your entire data pipeline — from raw ingestion through profiling and validation — all from the command line.\n\n## Commands\n\n| Command | Description |\n|---------|-------------|\n| `ml-visualizer ingest <input>` | Ingest raw data or record a data source entry |\n| `ml-visualizer transform <input>` | Log a data transformation step or operation |\n| `ml-visualizer query <input>` | Record a query against your dataset |\n| `ml-visualizer filter <input>` | Log a filter operation applied to data |\n| `ml-visualizer aggregate <input>` | Record an aggregation or rollup operation |\n| `ml-visualizer visualize <input>` | Log a visualization request or chart specification |\n| `ml-visualizer export <input>` | Record an export operation or export all data |\n| `ml-visualizer sample <input>` | Log a data sampling operation |\n| `ml-visualizer schema <input>` | Record or describe a data schema |\n| `ml-visualizer validate <input>` | Log a data validation check |\n| `ml-visualizer pipeline <input>` | Record a full pipeline definition or step |\n| `ml-visualizer profile <input>` | Log a data profiling run |\n| `ml-visualizer stats` | Show summary statistics across all entry types |\n| `ml-visualizer export <fmt>` | Export all data (formats: `json`, `csv`, `txt`) |\n| `ml-visualizer search <term>` | Search across all entries by keyword |\n| `ml-visualizer recent` | Show the 20 most recent activity log entries |\n| `ml-visualizer status` | Health check — version, disk usage, last activity |\n| `ml-visualizer help` | Show the built-in help message |\n| `ml-visualizer version` | Print the current version (v2.0.0) |\n\nEach data command (ingest, transform, query, etc.) works in two modes:\n- **Without arguments** — displays the 20 most recent entries of that type\n- **With arguments** — saves the input as a new timestamped entry\n\n## Data Storage\n\nAll data is stored as plain-text log files in `~/.local/share/ml-visualizer/`:\n\n- Each command type gets its own log file (e.g., `ingest.log`, `transform.log`, `visualize.log`)\n- Entries are stored in `timestamp|value` format for easy parsing\n- A unified `history.log` tracks all activity across command types\n- Export to JSON, CSV, or TXT at any time with the `export` command\n\nSet the `ML_VISUALIZER_DIR` environment variable to override the default data directory.\n\n## Requirements\n\n- Bash 4.0+ (uses `set -euo pipefail`)\n- Standard Unix utilities: `date`, `wc`, `du`, `tail`, `grep`, `sed`, `cat`\n- No external dependencies or API keys required\n\n## When to Use\n\n1. **Building a data pipeline journal** — use `ingest`, `transform`, and `pipeline` to document each step of your ML data preparation workflow\n2. **Tracking data quality"},{"path":"_meta.json","content":"{\n  \"ownerId\": \"kn76vqrf94wk924mddj8fp4p5x8497tb\",\n  \"slug\": \"ml-visualizer\",\n  \"version\": \"1.0.0\",\n  \"publishedAt\": 1773969545301\n}"},{"path":"skill-card.md","content":"## Description:\n\nA command-line activity journal for recording and exporting machine learning data pipeline, schema, validation, profiling, and visualization notes.\n\nThis skill is ready for commercial/non-commercial use.\n\n## Publisher:\n\n[bytesagain-lab](https://clawhub.ai/user/bytesagain-lab)\n\n### License/Terms of Use:\n\nMIT-0\n\n## Use Case:\n\nDevelopers and engineers can use this skill to log ML data preparation, validation, profiling, and visualization activity from the command line. 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