Results for “principle-extraction”

Extract the core product philosophy, strategic posture, and design principles from a raw concept or founder brief. Use when the team needs a crisp product promise, guiding principles, product point of view, or a philosophy statement before scope and UX planning.

Strip a problem to its irreducible fundamental truths and rebuild the solution from the ground up — free from analogy, convention, and inherited assumptions. Load when the user feels constrained by how something has always been done, when existing solutions feel expensive or inefficient for no good reason, when the user asks to think from first principles, challenge the fundamentals, or rebuild this from scratch. Also triggers on "why does it have to work this way", "what are the actual constraints here", "ignore what everyone else does", or when deep-thinking diagnoses a convention-break frame. Based on Aristotle's first principles method, popularised by Musk and Feynman. Produces genuinely novel solutions by eliminating convention.

First-principles assumption auditor. Classifies each hidden assumption (fact / convention / belief / interest-driven), ranks by fragility × impact, and rebuilds conclusions from verified premises. Bilingual: auto-detects Chinese or English.

First-principles assumption auditor. Classifies each hidden assumption (fact / convention / belief / interest-driven), ranks by fragility × impact, and rebuilds conclusions from verified premises. Bilingual: auto-detects Chinese or English.

First-principles assumption auditor. Classifies each hidden assumption (fact / convention / belief / interest-driven), ranks by fragility × impact, and rebuilds conclusions from verified premises. Bilingual: auto-detects Chinese or English.

Audits hidden assumptions in decisions by classifying them into four types, ranking by fragility and impact, and rebuilding conclusions from verified premises. Auto-detects Chinese or English.

First-principles assumption auditor. Classifies each hidden assumption (fact / convention / belief / interest-driven), ranks by fragility × impact, and rebuilds conclusions from verified premises. Bilingual: auto-detects Chinese or English.

Identifies recurring patterns across three or more domains to extract universal principles and apply them to new contexts.

Distill learnings from a completed repo analysis into reusable knowledge base entries in ~/.copilot/context/. Use after finishing a repo-analysis to make the learnings persistent and searchable.

First-principles assumption auditor. Classifies each hidden assumption (fact / convention / belief / interest-driven), ranks by fragility × impact, and rebuilds conclusions from verified premises. Bilingual: auto-detects Chinese or English.

First-principles assumption auditor. Classifies each hidden assumption (fact / convention / belief / interest-driven), ranks by fragility × impact, and rebuilds conclusions from verified premises. Bilingual: auto-detects Chinese or English.

First-principles assumption auditor. Classifies each hidden assumption (fact / convention / belief / interest-driven), ranks by fragility × impact, and rebuilds conclusions from verified premises. Bilingual: auto-detects Chinese or English.

将一篇理论建模论文摄取为理论研究 wiki：论文卡片 + 假设/原语 + 命题/定理 + 解概念 + 可检验推论，并与实证层在同一张 graph 上接桥

First-principles assumption auditor. Classifies each hidden assumption (fact / convention / belief / interest-driven), ranks by fragility × impact, and rebuilds conclusions from verified premises. Bilingual: auto-detects Chinese or English.

Audits hidden assumptions in decisions by classifying them, ranking by fragility and impact, and rebuilding conclusions from verified premises. Auto-detects Chinese or English input.

First-principles assumption auditor. Classifies each hidden assumption (fact / convention / belief / interest-driven), ranks by fragility × impact, and rebuilds conclusions from verified premises. Bilingual: auto-detects Chinese or English.

Identifies recurring patterns across three or more domains to extract universal principles and apply them to new problems.

Extract employee, employer, earnings, deductions, tax, and net pay from Spanish or Latin American payroll slips.

First-principles assumption auditor. Classifies each hidden assumption (fact / convention / belief / interest-driven), ranks by fragility × impact, and rebuilds conclusions from verified premises. Bi

First-principles assumption auditor. Classifies each hidden assumption (fact / convention / belief / interest-driven), ranks by fragility × impact, and rebuilds conclusions from verified premises. Bilingual: auto-detects Chinese or English.

First-principles assumption auditor. Classifies each hidden assumption (fact / convention / belief / interest-driven), ranks by fragility × impact, and rebuilds conclusions from verified premises. Bilingual: auto-detects Chinese or English.

将一篇经管实证论文摄取为实证研究 wiki：论文卡片 + 变量 + 数据 + 模型 + 机制 + 识别 + 稳健性 + 异质性 + 表格线索

Audits hidden assumptions in decisions or beliefs by classifying them into fact, convention, belief, or interest-driven, ranking by fragility and impact, then rebuilding conclusions from verified premises. Supports English and Chinese.

Extract parties, obligations, restrictions, permitted disclosures, and expiry dates from non-disclosure agreements.

Extract clean article content — title, author, date, and body text — from PDFs, Word docs, and web pages.

Extract structured fields from modello f24 documents.

融合潜能蒸馏与规则抽象能力，从规则维度提取和转化潜能，构建基于规则的潜能发展框架。

Auto-extract reusable patterns from the current session into skills or memory

融合分类系统与潜能蒸馏能力，从复杂现象中提取本质并进行结构化分类，促进跨领域知识整合与创新。

Find a unifying insight that eliminates multiple components, collapsing complexity by abstracting common patterns.

Extract structured fields from pit 37 documents.

从 PAPER_PLAN 起草 LaTeX 论文：逐 section 从 wiki 取材撰写 + 生成 figures/tables + BibTeX 验证 + de-AI polish

Extract structured fields from modello 730 documents.

Extract structured knowledge from source material. Comprehensive extraction is the default — every insight that serves the domain gets extracted. For domain-relevant sources, skip rate must be below 10%. Zero extraction from a domain-relevant source is a BUG. Triggers on "/reduce", "/reduce [file]", "extract insights", "mine this", "process this".

Use when extracting structured information from text - named entity recognition, relation extraction, coreference resolution, knowledge graph construction, and information extraction pipelinesUse when ", " mentioned.