Practitioner-judgment skill for Claude Code, Codex, and AI agents under ambiguity, trade-offs, and incomplete information.
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Updated
Apr 21, 2026 - Python
Practitioner-judgment skill for Claude Code, Codex, and AI agents under ambiguity, trade-offs, and incomplete information.
Frontend design cognitive engine grounded in Michael Polanyi's tacit knowledge framework. A Claude Code Skill that surfaces expert-level visual judgment.
Obtiene el conocimiento tácito generado al documentar los elementos de trabajo en Azure DevOps dentro de una organización y lo transforma en conocimiento explícito
A cognitive protocol for AI agents. Makes any LLM-based agent think like an experienced practitioner. Works with Claude Code, Codex, Gemini, and any agent framework.
A cognitive base that makes AI agents reason from verified foundations instead of inherited conventions. 让 AI agent 从第一性原理出发推理,而不是继承惯例。
The Polanyi stack for AI agents. Convert tacit knowledge, Polanyis paradox, and expert judgment into reusable agents and skills.
🦅 Decision Guidance Agent Skill — When data runs out, wisdom begins. Built on Kierkegaard's Leap of Faith & Polanyi's Tacit Knowledge.
The team developed an AI-enabled risk management solution that integrates SME heuristics with automated evaluation to improve the quality and actionability of project risk registers. The approach combines Microsoft Power Platform components with LLM-driven analysis to identify weak risks and mitigations, prioritise critical issues, and support c...
Accepted manuscript of 'Interpretive Orchestration' (Strategic Organization, 2026) — a framework for human-AI collaboration in interpretive qualitative research
The team built Jim‑E, an interactive AI‑assisted risk review tool that applies SME heuristics to project risk entries. Using a lightweight Streamlit interface and encoded heuristic rules, the solution helps users identify weak risks and mitigations, capture structured feedback, and generate clear audit‑ready reports.
hack26 is a collaborative hackathon-style event focused on rapidly exploring and prototyping practical data and AI solutions against a defined set of challenges. Teams work within clear challenge boundaries to test ideas, build proof‑of‑concepts and share learning in a short, intensive format.
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