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Equip AI agents with mental models for better analysis and decision-making.
The material indicates a prompt-only, open-source MIT skill with no required secrets, no declared remote endpoints, and no stated local execution or data access capabilities, so overall risk is low. The main uncertainty is the lack of README/maintenance detail; supply-chain transparency is generally good but it should still be managed like any third-party prompt asset.
The material explicitly states that no keys or environment variables are required; there is no description of token requests, credential storage, or credential forwarding, so credential exposure risk is low.
No remote endpoints are declared, and the system flags it as prompt-only; the material does not indicate any external service connections or user data egress.
As a skill flagged as prompt-only, the material does not show any ability to spawn local processes, run scripts, or invoke system-level capabilities.
No access to the filesystem, databases, clipboard, or other local/cloud resources is declared; based on the available material, it does not appear to have active read/write data capabilities.
The GitHub source is open to audit, MIT-licensed, and has some community adoption (149 stars); while maintenance status is unknown and the missing README reduces visibility, there are no signs of closed-source distribution, suspicious delivery, or other concrete red flags.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "thinking-partner" yet — see the docs or source repo.
Act as my thinking partner. Use suitable mental models to analyze the key decisions in migrating a monolith to microservices, detect the problem orientation, and break it down by risks, benefits, dependencies, and implementation steps.
A structured analysis with the problem frame, trade-offs, migration path, and recommended priorities.
As a thinking partner, help me evaluate whether to prioritize onboarding features or advanced reporting. Compare user value, implementation cost, opportunity cost, and risks using multiple mental models, then recommend a direction.
A clear decision analysis covering comparison dimensions, rationale for the conclusion, and next-step recommendations.
Please act as a thinking partner to analyze how to verify whether a user behavior change was caused by a new feature. Identify suitable cognitive operations and reasoning frameworks to help form a more rigorous research plan.
A more rigorous research plan with hypotheses, validation methods, confounders, and evidence requirements.
Improve complex reasoning with multi-agent debate, bias detection, and structured thinking.
Set up and use a self-hosted AI companion for chat and assistance.
Learn provider-neutral agent design best practices across coding agent environments.
Curate and distill agent skills about people, relationships, memorial scenes, and methods.
Match tasks to skills, track performance, detect gaps, and discover new skills.
Brainstorm with multiple LLMs before presenting an implementation plan to users.