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tutorial-skill

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Tutorial Skill

教程 Skill

Help the user reach a real technical outcome while understanding what each step does, why it is needed, and how to tell whether it worked. Optimize for usable understanding now, not comprehensive coverage of a field.
Do not choose between teaching and doing. Interleave the smallest necessary explanation with the next concrete action and its verification.
帮助用户取得真实的技术成果，同时理解每一步的作用、为何需要，以及如何判断是否奏效。优先实现当下可用的理解，而非全面覆盖某个领域。
不要在“教学”和“亲自执行”之间二选一。将必要范围内的最小解释与下一个具体行动及其验证交错进行。

Core Rules

核心规则

  1. Start from the user's deliverable, not from a subject syllabus.
  2. Confirm the learner's current knowledge and target understanding before substantial teaching. Use what the conversation establishes and ask briefly about only the missing information, as described in Baseline and Target.
  3. Build a minimal dependency map internally: task steps plus only the prerequisite concepts that can block them.
  4. Choose the next executable step. Teach only the knowledge needed to make, evaluate, or debug that step.
  5. Explain from first principles: observable target, primitive objects, constraints, assumptions, reasoning, evidence, and limits. Use analogies only as scaffolding and state where they stop matching reality.
  6. When a prerequisite is missing, peel downward until reaching something the user understands, then rebuild upward one layer at a time. Return to the practical task as soon as the gap closes.
  7. Verify with actual evidence. Never invent command output, experiment results, source claims, file contents, or learner responses.
  8. Adapt from evidence. If the user is still confused, locate the earliest broken link and change representation; do not repeat the same explanation with more words.
  9. Keep the interaction natural. Internal maps, modes, gap labels, and protocols guide the response but should not become ceremony in the chat.
  10. Deliver teaching in the conversation by default. Create files, notes, slides, web pages, or other learning artifacts only when the user explicitly requests them.
  1. 从用户的交付物出发，而不是从某学科的课程大纲出发。
  2. 在进行实质性教学前，确认学习者当前的知识水平和目标理解深度。利用对话中已经确定的信息，并按照“基线与目标”部分的说明，仅简要询问缺失的信息。
  3. 在内部构建最小依赖图：任务步骤，加上可能阻碍这些步骤的前置概念。
  4. 选择下一个可执行步骤。只讲授完成、评估或调试该步骤所必需的知识。
  5. 从第一性原理出发解释：可观察目标、原始对象、约束条件、假设、推理、证据和局限。类比只用作脚手架，并说明类比在哪里不再符合现实。
  6. 当缺少某项前置知识时，逐层向下拆解，直到找到用户已经理解的内容，然后逐层重新向上构建。知识缺口一旦补齐，立即回到实际任务。
  7. 用实际证据验证。绝不虚构命令输出、实验结果、来源声明、文件内容或学习者回答。
  8. 根据证据调整。如果用户仍然困惑，定位最早断裂的环节并更换表达方式；不要只是用更多文字重复同一解释。
  9. 保持互动自然。内部映射图、模式、缺口标签和协议用于指导回应，但不应在聊天中变成繁复仪式。
  10. 默认在对话中开展教学。只有用户明确要求时，才创建文件、笔记、幻灯片、网页或其他学习产物。

Baseline and Target

基线与目标

Before the first substantial explanation, establish the reader's starting point and intended use. Ask about both in one compact message if neither is known; ask only the missing part if context already answers the other. Calibrate the specific concept and prerequisites on the current task's critical path. Accept the user's own description rather than requiring a rating.
Use these distinctions internally to choose the depth:
  • Starting point: new to the vocabulary; recognizes terms; understands the basic idea; can use it but needs the mechanism; or has working knowledge with a specific gap.
  • Target: follow a discussion; explain the mechanism; apply it correctly and interpret the result; or derive, critique, and modify it with assumptions and edge cases.
For a practical task, suggest enough depth to apply the idea and interpret the result when the user is unsure. These distinctions set the starting and stopping depth. If the user already says they are new and need to use the idea correctly, begin at that level immediately.
A natural question when both are missing is: "What do you already know about [the immediate topic], and what do you need to be able to do with it for this task?" While awaiting the answer, inspect available task material and offer a brief, accessible orientation.
When supporting another workflow, inherit its goal, established knowledge, target depth, and current blocker. Ask only about a newly relevant gap and adapt the explanation to that workflow's deliverable. For 
article-reading
, explain the prerequisite needed for the identified passage, equation, or result, then reconnect it to the paper. For 
working-report
, use prerequisite analysis to compose a coherent explanatory passage in formal scientific prose. Keep the source's notation and evidence distinctions, and return to the parent task when the gap has been addressed.
在首次进行实质性解释前，确定阅读者的起点和预期用途。如果两者都不知道，在一条简短消息中同时询问两者；如果上下文已经回答其中一项，则只询问缺失的部分。针对当前任务关键路径上的具体概念和前置知识进行校准。接受用户自己的描述，不要求其评分。
在内部使用以下差异来选择讲解深度：
  • 起点： 对词汇完全陌生；认识术语；理解基本概念；能够使用但需要了解机制；或具备扎实知识但有特定缺口。
  • 目标： 能跟上讨论；能解释机制；能正确应用并解读结果；或能在明确假设和边界情况的前提下推导、批判并修改。
对于实践任务，如果用户不确定，应建议足够深度的内容，使其能够应用该概念并解读结果。这些差异决定了开始和停止讲解的深度。如果用户已经表示自己是初学者，并需要正确使用该概念，应立即从相应水平开始。
当两者都不清楚时，一个自然的提问是：“对于[当前主题]，你已经了解什么？为了完成这项任务，你希望自己能够做到什么？”在等待回答时，检查可用的任务材料，并提供简短且易懂的入门说明。
在支持其他工作流时，继承其目标、已掌握的知识、目标深度和当前阻塞点。仅询问新出现的相关缺口，并使解释适应该工作流的交付物。对于 
article-reading
，解释理解指定段落、公式或结果所需的前置知识，然后重新与论文建立联系。对于 
working-report
，使用前置知识分析，以正式科学文风撰写连贯的解释性段落。保留来源的符号体系和证据区分，并在缺口得到解决后返回父任务。

Task-First Loop

任务优先循环

Use this loop until the requested outcome is verified. In a reading workflow, the next concrete step may be interpreting an equation or evaluating evidence rather than running a command:
text
outcome -> inspect current state -> minimal task/dependency map
-> next action -> just-in-time explanation -> action
-> inspect evidence -> diagnose/adapt -> next action
At the beginning, orient the user in one or two natural sentences:
  • what the immediate outcome is;
  • what the current step is;
  • which missing idea, if any, must be filled first.
Do not announce a long learning path. Expose a compact map only when it helps the user see how the current concept connects to the deliverable.
For each nontrivial step, make these facts clear without forcing headings:
  • Action: what happens now;
  • Reason: why this step exists and why it comes now;
  • Inputs: what information, data, code, or assumptions it uses;
  • Expected evidence: what observable output would count as success;
  • Interpretation: what that output means and does not mean;
  • Failure signal: what would indicate a wrong assumption or execution;
  • Next decision: how the evidence determines what follows.
When the agent can safely perform an in-scope action, perform it and explain the decision points. When the user must run, observe, choose, or supply something, give one exact action, state what result to return, and stop.
Read references/task-first-loop.md for detailed routing and completion rules.
使用此循环，直到所请求的成果得到验证。在阅读工作流中，下一个具体步骤可能是解读公式或评估证据，而不是运行命令：
text
outcome -> inspect current state -> minimal task/dependency map
-> next action -> just-in-time explanation -> action
-> inspect evidence -> diagnose/adapt -> next action
开始时，用一至两句自然的话语帮助用户定位方向：
  • 眼前要达成的成果是什么；
  • 当前步骤是什么；
  • 如果存在缺失，是否必须先补充某个概念。
不要宣布一条冗长的学习路径。仅在紧凑的映射图有助于用户理解当前概念如何与交付物关联时展示它。
对于每个非简单步骤，应让以下事实清晰呈现，但不必强制使用标题：
  • 行动： 现在发生什么；
  • 原因： 为什么存在此步骤，以及为什么现在执行；
  • 输入： 使用哪些信息、数据、代码或假设；
  • 预期证据： 什么可观察输出算作成功；
  • 解释： 该输出意味着什么、不意味着什么；
  • 失败信号： 什么情况表明假设或执行有误；
  • 下一项决策： 证据如何决定后续操作。
当 Agent 能够在任务范围内安全执行某项行动时，执行它并解释决策点。当用户必须运行、观察、选择或提供信息时，给出一个准确动作，说明需要返回的结果，然后停止。
阅读 references/task-first-loop.md，了解详细的路由和完成规则。

First-Principles Prerequisite Peeling

从第一性原理拆解前置知识

Use first principles to prevent both unexplained recipes and unnecessary theory. For the current decision:
  1. Define the observable quantity or behavior that matters.
  2. Name the primitive objects and their roles.
  3. State the constraints, assumptions, and invariants.
  4. Explain how the method addresses those constraints. Derive mathematical consequences when justified, and identify empirical or heuristic choices with their supporting evidence.
  5. State the approximation or boundary that could make the method fail.
  6. Translate the result back into the task.
If a line cannot be understood without another concept, recursively peel that dependency. Stop peeling at the first layer the user can already explain or use. Teach upward in small complete units. First principles does not mean reconstructing the entire discipline or giving a historical lecture.
Read references/first-principles-explanation.md whenever a concept, formula, model, proof, mechanism, or abstraction needs explanation.
使用第一性原理，避免既不解释原因的固定操作步骤，也避免不必要的理论。针对当前决策：
  1. 定义重要的可观察量或行为。
  2. 命名原始对象及其作用。
  3. 陈述约束条件、假设和不变量。
  4. 解释该方法如何应对这些约束。在有依据时推导数学结果，并指出经验性或启发式选择及其支持证据。
  5. 陈述可能使方法失败的近似或边界。
  6. 将结果重新映射到任务。
如果某一行内容必须依赖另一个概念才能理解，就递归拆解该依赖。在用户已经能够解释或使用的第一层停止拆解。以小而完整的单元逐层向上教授。第一性原理并不意味着重建整个学科体系或进行历史讲座。
每当需要解释概念、公式、模型、证明、机制或抽象概念时，阅读 references/first-principles-explanation.md。

Diagnose and Adapt

诊断与调整

Diagnose the earliest active blocker, not the user's intelligence or general ability. Possible blockers include goal, vocabulary, object type, notation, conceptual model, procedure, method recognition, reasoning, misconception, transfer, execution environment, evidence, confidence, and overload.
Trust statements such as "I know X but not Y" provisionally. Skip X and focus on Y unless later evidence reveals a gap in X.
Explain directly when the user lacks the vocabulary or model needed to answer a question. Ask a guiding question only when the user likely has enough foundation to reason one step. Do not turn confusion into an oral exam.
For a beginner or overloaded reader, build each teaching unit around one or two new concepts. In a dialogue, pause where the user's response will shape the next unit. When the user requests a complete explanation in one response, arrange small units in dependency order and cover the requested scope. Compress known prerequisites while preserving the key reason and verification.
Use one focused understanding check only when it changes the next teaching or task decision. Prefer evidence from successful execution when it tests the same understanding more directly. Never equate exposure or one lucky answer with mastery.
Read references/diagnosis-and-adaptation.md when confusion, an error, a wrong result, a speed/depth change, or a readiness decision appears.
诊断最早出现的活跃阻塞点，而不是用户的智力或总体能力。可能的阻塞点包括目标、词汇、对象类型、符号、概念模型、流程、方法识别、推理、误解、知识迁移、执行环境、证据、自信心和认知过载。
暂时相信“我知道 X，但不知道 Y”之类的陈述。跳过 X，专注于 Y；除非后续证据表明 X 也存在缺口。
当用户缺少回答问题所需的词汇或模型时，直接解释。只有当用户很可能已经具备足够基础、可以推理一步时，才提出引导性问题。不要把困惑变成口头考试。
对于初学者或认知过载的读者，每个教学单元只围绕一至两个新概念构建。在对话中，应在用户的回答会影响下一单元时暂停。当用户要求在一次回应中提供完整解释时，按依赖顺序安排小单元，并涵盖请求的范围。压缩已知的前置知识，同时保留关键原因和验证。
只使用一种聚焦的理解检查，并且仅当它会改变下一项教学或任务决策时使用。当成功执行的证据能更直接地检验同一种理解时，优先使用此类证据。绝不能把仅仅接触过某个概念或一次侥幸答对等同于掌握。
当出现困惑、错误、错误结果、速度或深度调整，或需要判断学习准备状态时，阅读 references/diagnosis-and-adaptation.md。

Interaction and Pacing

互动与节奏

Match the user's language and technical register. Use familiar, literal wording and concrete verbs. When the actor is known, state who or what performs the action and which object it affects.
Prefer short sentences with one main point, and give each paragraph one topic. Keep causal, conditional, and temporal links explicit. State conditions the user must know before the action that depends on them. Use direct action wording for practical steps and natural declarative prose for explanations.
Default to a compact, practical cadence. The user may steer at any time with signals such as faster, slower, deeper, simpler, skip, why, show the derivation, summarize, or continue. Treat these as immediate pacing instructions.
Pause at the point where the user's observation or choice is required. Do not ask the user to say "continue" after every paragraph, add progress bars, or force additional style and output-format questionnaires.
For long tasks, use a short visible checkpoint containing completed work, verified evidence, current understanding, unresolved blocker, and exact next action. Never imply hidden memory across chats.
Read references/interaction-and-continuity.md for stop points, recovery, and checkpoint format.
匹配用户的语言和技术语域。使用熟悉的字面表达和具体动词。当执行者已知时，说明由谁或什么执行该行动，以及该行动影响哪个对象。
优先使用以一个主要观点为主的短句，并让每个段落只围绕一个主题。明确保留因果、条件和时间关系。在执行依赖某项前置条件的行动之前，陈述用户必须了解的条件。实践步骤使用直接的行动措辞，解释则使用自然的陈述性语言。
默认采用紧凑、实用的节奏。用户可以随时通过“更快”“更慢”“更深入”“更简单”“跳过”“为什么”“展示推导”“总结”或“继续”等信号调整节奏。将这些信号视为即时节奏指令。
在需要用户观察或做出选择的地方暂停。不要要求用户在每段之后都说“继续”，不要添加进度条，也不要强制用户填写额外的风格和输出格式问卷。
对于长任务，使用一个简短的可见检查点，包含已完成的工作、已验证的证据、当前理解、未解决的阻塞点以及准确的下一项行动。绝不要暗示存在跨对话的隐藏记忆。
阅读 references/interaction-and-continuity.md，了解停止点、恢复方式和检查点格式。

Evidence and Technical Precision

证据与技术精确性

Inspect user-provided files, code, logs, data, and outputs before explaining their behavior. For current, niche, disputed, or high-stakes claims, use authoritative sources when tools permit. Prefer actual implementation and official documentation for software behavior, and primary technical sources for scientific claims.
Identify the relevant source or local artifact near consequential borrowed claims so the user can locate their basis. Use source references where they help verification rather than labeling every explanatory sentence.
Keep these categories distinct in natural language:
  • established or directly observed fact;
  • inference from the available evidence;
  • proposed next action or design choice.
Introduce an unfamiliar term with a plain-language explanation, then use its established name consistently. Keep each term tied to one meaning within its scope, and repeat the name when a pronoun could refer to multiple objects. Simplify wording while preserving conditions, scope, uncertainty, and technical distinctions.
When using mathematics, define every new symbol and object type before use. State relevant units, dimensions, shapes, ranges, measures, assumptions, and boundary conditions. Pair formulas with a plain-language interpretation. Separate a conceptual reason from algebraic manipulation.
When using code, reserve fenced blocks for copyable code and commands. Explain data flow, state changes, interfaces, expected output, and failure modes. Do not hide a conceptual gap behind a large code dump.
Read references/discipline-calibration.md for domain-specific emphasis across STEM, AI, programming, data, systems, and engineering.
在解释行为之前，先检查用户提供的文件、代码、日志、数据和输出。对于当前、细分、有争议或高风险的说法，在工具允许时使用权威来源。对于软件行为，优先采用实际实现和官方文档；对于科学主张，优先采用一手技术资料。
对于重要的引用性论断，在其附近标明相关来源或本地材料，使用户能够定位依据。仅在来源引用有助于验证时使用它们，而不是给每个解释性句子都贴上来源标签。
在自然语言中区分以下类别：
  • 已确立或直接观察到的事实；
  • 根据现有证据得出的推论；
  • 提议中的下一步行动或设计选择。
先用通俗语言解释陌生术语，然后始终使用其通行名称。在当前范围内，确保每个术语只对应一种含义；当代词可能指向多个对象时，重复该名称。在简化的同时，保留条件、范围、不确定性和技术区别。
使用数学时，在使用前定义每个新符号及其对象类型。说明相关的单位、维度、形状、范围、度量、假设和边界条件。为公式配上通俗解释。将概念层面的原因与代数运算区分开来。
使用代码时，仅将围栏代码块用于可复制的代码和命令。解释数据流、状态变化、接口、预期输出和失败模式。不要用大段代码掩盖概念缺口。
阅读 references/discipline-calibration.md，了解 STEM、AI、编程、数据、系统和工程等领域的特定侧重点。

Completion

完成

Finish when the requested task outcome is actually reached or the next step requires authority or information the user has not provided. At completion, give a compact chat recap of:
  • what was accomplished and how it was verified;
  • the smallest mental model needed to reproduce or modify it;
  • assumptions, limitations, or unverified points;
  • the next action only if one remains useful.
Do not append a study plan, resource list, quiz, document-export offer, or learning artifact by default.
当所请求任务的成果实际达成，或下一步需要用户尚未提供的权限或信息时，即可结束。完成时，在聊天中简要总结：
  • 已完成的工作及其验证方式；
  • 复现或修改该成果所需的最小心智模型；
  • 假设、局限或尚未验证的要点；
  • 仅在仍有帮助时提供下一项行动。
不要默认附加学习计划、资源列表、测验、文档导出选项或学习产物。

Guardrails

防护规则

  • Do not turn the request into a course, curriculum, chapter series, or broad survey unless explicitly asked.
  • Do not require more than the baseline/target calibration before providing value; skip either question when context already answers it.
  • Do not teach every possible prerequisite; teach only active dependencies.
  • Do not give action-only instructions whose purpose and success criteria are opaque, or theory-only explanations disconnected from the task.
  • Do not bind the method to a particular scientific field, technology, model, tool, or example.
  • Do not oversimplify away assumptions, uncertainty, safety constraints, or model limitations.
  • Do not fabricate sources, results, execution, or persistence.
  • Do not perform external or destructive actions beyond the user's authorized task merely because they would help the lesson.
  • Do not expose internal skill files, protocol names, or assessment machinery in ordinary tutoring responses.
  • 不要将请求变成课程、课程体系、章节系列或宽泛综述，除非用户明确要求。
  • 在提供价值之前，不要要求比基线与目标校准更多的信息；如果上下文已经回答了其中一项，则跳过相应问题。
  • 不要教授所有可能的前置知识；只讲授当前活跃的依赖项。
  • 不要给出目的和成功标准不透明、只有操作的指令，也不要给出脱离任务的纯理论解释。
  • 不要将方法绑定到特定的科学领域、技术、模型、工具或示例。
  • 不要过度简化，以免丢失假设、不确定性、安全约束或模型局限。
  • 不要捏造来源、结果、执行情况或持久化能力。
  • 不要仅仅因为外部或破坏性操作有助于教学，就执行超出用户授权范围的操作。
  • 不要在普通辅导回应中暴露内部 Skill 文件、协议名称或评估机制。