Top OpenClaw Skills for Machine Learning Engineers

Helpful references: Fastio Workspaces, Fastio Collaboration, and Fastio AI.

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Fastio: 50GB free, no CC, 19 MCP tools. Workspaces for datasets, models, RAG queries. Built for openclaw skills machine learning engineers workflows.

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We selected skills by verifying each one against its published ClawHub page. Every skill in this list has a real, accessible page at clawhub.ai. We then evaluated each skill against common ML engineering needs:

• Artifact management: Can the skill help agents store and retrieve datasets, model checkpoints, and experiment outputs?
• Data access patterns: Does the skill support the storage and query patterns common in ML workflows?
• API connectivity: Can it connect agents to external ML platforms and data sources?
• Research access: Can it help agents gather technical papers or documentation?
• Pipeline integration: Does it fit naturally into automated ML pipelines without excessive setup?

We excluded skills where we could not verify a real ClawHub page, regardless of how useful the concept sounded.

Fastio provides 19 MCP tools for agent workspaces covering file storage, sharing, AI-powered document analysis, task management, and approval workflows. For ML engineers, it is the persistent storage layer for datasets, model checkpoints, experiment artifacts, and documentation. The built-in RAG capability means your agent can query a directory of model cards, training logs, or research PDFs using natural language without building a custom retrieval pipeline.

Strengths:

• 19 built-in MCP tools for comprehensive file and workspace management.
• Zero-config RAG: agents query uploaded artifacts and documents using natural language.
• Free agent tier: 50GB storage, 5,000 credits/month, no credit card required.
• Ownership transfer lets agents build experiment workspaces and hand them off to human reviewers.

Limitations:

• File-focused; needs pairing with execution and tracking tools for a complete MLOps pipeline.
• Large model checkpoints beyond the free tier require paid usage-based credits.

Best For: ML engineers deploying agents that need persistent storage for datasets, sharing model artifacts with teammates, and running natural language queries over experiment logs.

Install Command: clawhub install dbalve/fast-io

ClawHub Page: clawhub.ai/dbalve/fast-io

The SQL Toolkit lets agents query, design, migrate, and optimize SQL databases across SQLite, PostgreSQL, and MySQL. ML engineers use SQL databases extensively for storing experiment metadata, hyperparameter logs, evaluation results, and dataset registries. The skill covers complex query writing including window functions and CTEs, as well as migration scripts and EXPLAIN analysis for optimization.

Strengths:

• Complex query writing for experiment result analysis including window functions and aggregations.
• Schema design for experiment tracking databases.
• SQLite for zero-setup local experiment registries during development.
• Migration scripts for evolving schema as experiment tracking needs change.

Limitations:

• Requires sqlite3, psql, or mysql CLI tools to be installed.
• Instruction-only skill; agents write queries but you execute them.

Best For: ML engineers using SQL databases for experiment metadata, model registries, and evaluation result storage.

Install Command: Download from ClawHub (requires sqlite3, psql, or mysql CLI).

ClawHub Page: clawhub.ai/gitgoodordietrying/sql-toolkit

The S3 skill provides guidance on working with S3-compatible object storage: presigned URLs, lifecycle policies, multipart uploads, versioning, CORS configuration, key naming conventions, and cost optimization. It covers AWS S3, Cloudflare R2, Backblaze B2, and MinIO. For ML teams storing large datasets and model artifacts in object storage, this skill helps agents understand and apply the right access patterns and security configurations.

Strengths:

• Covers presigned URL best practices for secure, time-limited artifact sharing.
• Multipart upload strategies for large model checkpoints and dataset files.
• Provider-specific differences across AWS S3, Cloudflare R2, Backblaze B2, and MinIO.
• Lifecycle rules for automatically archiving or deleting old experiment artifacts.

Limitations:

• Guidance skill, not an execution wrapper; agents advise on patterns rather than execute API calls directly.
• Requires pairing with an API execution skill for direct S3 operations.

Best For: ML platform engineers designing storage architecture for datasets and model artifacts across multiple cloud providers.

Install Command: Download from clawhub.ai/ivangdavila/s3.

ClawHub Page: clawhub.ai/ivangdavila/s3

Playwright provides browser automation via MCP. ML engineers use it for scraping research papers from arXiv or Semantic Scholar, extracting benchmark results from leaderboard pages, automating interactions with web-based ML tools and dashboards, and capturing screenshots of visualization outputs for reports.

Strengths:

• Real browser rendering for JavaScript-heavy ML platform dashboards and leaderboards.
• Structured data extraction from rendered pages for benchmark comparison tables.
• Screenshot capture for documentation and visual regression of model output visualizations.
• Multi-step form automation for web-based experiment configuration interfaces.

Limitations:

• Headless by default; headed mode requires display configuration.
• Browser instances consume memory during long automation sessions.

Best For: ML researchers who need agents to gather structured data from web-based sources or automate interactions with browser-based ML tools.

Install Command: npx @playwright/mcp --headless

ClawHub Page: clawhub.ai/ivangdavila/playwright

API Gateway connects your agent to 100+ external services through a single managed OAuth proxy. For ML engineers, relevant integrations include GitHub for experiment repositories, Google Workspace for team documentation and tracking sheets, Notion for model cards and research notes, and Airtable for dataset registries. OAuth token refresh is handled automatically.

Strengths:

• Single consistent interface for 100+ services eliminates per-service wrapper configuration.
• Managed OAuth for Google, Microsoft, GitHub, Notion, Airtable, and more.
• Multi-connection support for routing to different team accounts or environments.

Limitations:

• Requires a Maton API key from maton.ai/settings.
• Acts as a passthrough proxy; underlying service rate limits still apply.

Best For: ML teams whose workflows span multiple external platforms for documentation, tracking, and collaboration.

Install Command: Set MATON_API_KEY environment variable; obtain key at maton.ai/settings.

ClawHub Page: clawhub.ai/byungkyu/api-gateway

Brave Search enables agents to search the web and extract page content as markdown without a browser session. For ML engineers, it supports fast literature lookups, documentation searches for frameworks and libraries, and retrieving benchmark comparisons from web sources. Configurable result counts and optional full-page content extraction make it suitable for both quick lookups and deeper reference gathering.

Strengths:

• Lightweight web search with no browser overhead for fast lookups.
• Optional full page content extraction as markdown for detailed review of specific papers or docs.
• URL content fetching for directly accessing known documentation or paper abstract pages.

Limitations:

• Requires a BRAVE_API_KEY environment variable.
• Not a real-time news feed; reflects Brave's index freshness.

Best For: ML researchers and engineers who need agents to search technical documentation and literature during model development sessions.

Install Command: cd ~/Projects/agent-scripts/skills/brave-search && npm ci

ClawHub Page: clawhub.ai/steipete/brave-search

The GitHub skill uses the gh CLI to manage experiment repositories: open PRs for new model versions, check CI pipeline status for training jobs, inspect failed workflow steps, and query commit history to track configuration changes. For ML teams practicing experiment-as-code workflows, this brings repository operations directly into the agent session.

Strengths:

• Full PR lifecycle for model version management.
• CI status checks for training and evaluation pipelines.
• Advanced API queries with --json and --jq for precise repository data extraction.
• Issue management for tracking experiment tasks and model improvement backlog.

Limitations:

• Requires gh CLI to be installed and authenticated.
• Instruction-only; agents plan git operations, not execute them autonomously.

Best For: ML engineers practicing experiment-as-code and version-controlled model development.

Install Command: Instruction-only; requires gh auth login or GITHUB_TOKEN.

ClawHub Page: clawhub.ai/steipete/github

Filesystem Management provides advanced local file operations for managing dataset directories and model file hierarchies. It supports smart listing with recursive traversal, content search across large directories, batch copy and move with dry-run mode, and directory tree visualization with statistics. For ML engineers working with complex local data pipelines before cloud upload, this replaces manual file management with natural language commands.

Strengths:

• Content search across large dataset directories for locating specific files or patterns.
• Batch operations with dry-run mode to safely preview before executing bulk reorganizations.
• Directory statistics for auditing dataset coverage and file distribution.
• Recursive traversal for navigating deeply nested data pipeline directory structures.

Limitations:

• Local filesystem only; not a cloud storage replacement.
• Requires execution permissions on target directories.

Best For: ML engineers managing large local dataset hierarchies and model checkpoint directories before cloud upload.

Install Command: clawdhub install filesystem

ClawHub Page: clawhub.ai/gtrusler/clawdbot-filesystem

A practical starting point for ML engineers is to pair Fastio with SQL Toolkit. Fastio handles artifact and documentation storage with built-in RAG for natural language queries over experiment logs. SQL Toolkit handles experiment metadata querying against a PostgreSQL or SQLite tracking database.

For research-heavy workflows, add Brave Search for literature lookup and Playwright for gathering structured benchmark data from leaderboard pages.

For teams working across multiple external platforms, API Gateway provides a single authenticated interface to GitHub, Google Workspace, Notion, and other tools in one configuration step.

Define clear tool contracts and fallback behavior in your agent's system prompt so agents fail safely when a dependency is unavailable or a network request times out. This discipline is especially important in long-running ML pipelines where a silent failure can corrupt downstream experiment results.