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Connects experiment platforms and adds local tools for analysis and skill debugging.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "ab-platform-mcp-local" yet — see the docs or source repo.
Read the latest A/B experiment data from the upstream platform, summarize key metric changes, identify the significantly improved or declined variants, and provide five optimization suggestions in English.
A concise experiment report with metric comparisons, anomalies, and actionable optimization recommendations.
Inspect this skill’s call chain and error logs in the experiment platform, identify the failure cause, and provide fix steps plus recommended test cases.
A debugging report identifying the root cause, with fix steps and validation suggestions.
Fetch all experiment variants from this week from the upstream platform, compare them by conversion, retention, and error rate, and output a conclusion table with the variant most worth iterating on.
A multi-variant comparison showing strengths, weaknesses, and the recommended iteration direction.
Connect AI agents to control local JupyterLab for coding and analysis.
Build local MCP servers and AI tools from plain-language descriptions.
Securely equips AI agents with executable tools for commands, search, and file operations.
Helps LLM agents discover, register, and run local and remote MCP tools.
Build, debug, and manage software tasks with natural language across LLMs.
Analyze and fix test failures with plain-language guidance in IDE and Slack.