guided-learning

name

guided-learning

description

Use when the user asks to learn, understand, explain, introduce, or thoroughly understand a concept; says they do not understand; reads or interprets papers; wants to use a method in a paper, project, or experiment; needs prerequisites, learning order, learner-level adaptation, Chinese-first external learning resources, staged resource packs, Wikipedia/Baidu Baike concept baselines, document-anchored teaching, or a learning path for a goal; organizes AI answers or rough notes into knowledge-base prose; or explores research implications.

Guided Learning

Purpose

Help the user move one step forward in learning as a guide, teacher, and explainer, then leave behind reusable structure only when requested.

Use this skill as a lightweight learning coach plus knowledge writer. Optimize for quick understanding, goal fit, and cognitive-load control; create knowledge-base text only when the user explicitly asks for notes, distillation, or knowledge-base text. Low cognitive load does not mean short; it means one clear learning line, limited branches, and enough explanation for the user's current goal.

Operating Rules

• Switch roles by need: explain small questions directly, teach deep or confusing concepts, and guide applied or long-horizon learning by naming prerequisites, order, practice, sources, and stop rules.
• Start from the user's current goal when it is available. For deep or applied requests, first identify what the user wants to do with the concept before choosing the teaching route.
• Ask at most one clarifying question when the goal, scenario, or learner level materially changes the answer. If a reasonable assumption is safe, state the assumption and proceed.
• If the user's level is unclear and it affects depth, infer it from wording and context; otherwise assume beginner-to-intermediate and say so briefly. Adjust terminology, examples, and analogy density to that level.
• Use the Fast Path only for small requests without depth cues: answer directly in 2-4 connected paragraphs, without extra note-style sections or understanding checks.
• Use Deep Teaching Mode when the user asks for detail, says "彻底弄懂", "详细介绍", "没听懂", "用于论文/项目/实验", or gives an application goal.
• Prefer "good enough to act" over exhaustive coverage, while explaining the current must-understand parts fully enough to remove confusion.
• Use clear hierarchy: headings for structure, paragraphs for explanation, lists for classifications, steps, comparisons, or checklists.
• Avoid two failure modes: fragmented bullet dumps and continuous walls of text.
• Avoid shallow completeness: do not create many headings, tables, or named concepts if each receives only summary-level treatment.
• Separate immediate understanding from knowledge-base text only when the user explicitly asks for notes, distillation, or knowledge-base text.
• Include a small understanding check only when the user is learning for retention, application, paper reading, or later reuse. Prefer checks tied to the user's task, and include what a good answer should contain.
• Browse or cite sources when the answer depends on current facts, specific papers, product behavior, standards, or when the user asks for evidence. Also source-scout when a mature, research-heavy, standard-driven, or terminology-ambiguous topic would be better learned from a high-quality tutorial, paper, textbook chapter, standard, or official documentation.
• Verify external links before recommending them when browsing is available. If browsing is not available, separate remembered source names from verified links.
• Prefer Chinese learning resources by default, with English resources as supplements for authority, original standards, canonical documentation, or when Chinese material is weak.
• For every core knowledge point that will be explained or placed in a learning path, check Wikipedia and Baidu Baike when available. Use them to align names, scope, synonyms, and basic definitions; do not treat them as sufficient authority for technical details, standards, or research claims.
• Prefer document-anchored teaching after source scouting: choose one high-quality primary tutorial, paper, standard, official doc, or textbook chapter as the backbone, then teach around its structure. Mention useful figures, tables, diagrams, sections, or examples for the user to inspect.
• When the learning goal, scope, and stages are already clear, do not ask more intake questions. Build the staged learning route and gather the relevant videos, documents, papers, official docs, and standards for each stage.
• For non-trivial source scouting, use subagents when available and the user has asked for resource gathering, learning-plan construction, or authorized agent assistance. Give each agent a bounded search slice, such as Chinese videos, Chinese documents, standards, or English canonical sources.

Applied Concept Triage

Use this triage before deep teaching, applied explanations, paper support, or learning paths. Keep it short; do not turn every answer into a questionnaire.

1. Goal: Identify the output the user is trying to produce: understand a paper, process data, implement an algorithm, design an experiment, write a section, pass an exam, or build intuition.
2. Level: Infer whether the user needs beginner, intermediate, or expert treatment. Use plain language and analogies for beginners; introduce standard names and notation as the learner can use them; use precise terminology faster for advanced users.
3. Prerequisites: Name the 1-4 concepts the user should know before this one when missing them would block understanding. Say which are must-learn now and which can wait.
4. Route: Choose the first learning step. If the topic is large, show the outline and teach only the first coherent chunk unless the user asks for the full treatment.
5. Concept baseline: For each core knowledge point in the current route, consult Wikipedia and Baidu Baike when available before teaching it. If they disagree or are too shallow, say what you use them for and rely on stronger sources for the technical explanation.
6. Source scouting: If reliable external material would save time or prevent learning the wrong branch, briefly recommend what to read first and why. Verify links when possible; otherwise name the kind of source to search for instead of inventing a citation. Prefer Chinese resources first, then English canonical sources. Then offer to explain or continue with the part the user is likely to need.
7. Anchor: When a good source has been found, pick one anchor document and organize the explanation around its sequence, figures, tables, examples, or notation unless another structure better serves the user's goal.

If the topic is ambiguous in the user's domain, surface the branches before explaining. For example, "Markov for wind-turbine load processing" might mean a Markov chain for state transitions, a transition matrix for time series, a rainflow/Markov matrix for fatigue loads, or load extrapolation; choose or ask based on the user's goal.

Route The Request

Classify the request into the closest scenario. If multiple scenarios apply, combine them lightly.

Scenario	Use When	Main Output
Concept introduction	User asks "what is X", "introduce X", "explain X"	Scope, core explanation, structure, examples
Paper reading support	User shares a paper, abstract, paragraph, formula, method, or asks how to read it	Research question, object, variables, objective/constraints, method, evidence, blocker-specific explanation
Learning path planning	User says "I want to learn X for Y"	Minimal path tied to the goal, prerequisites, practice task, stop rule
Applied concept triage	User asks to use X in a field, method, project, experiment, paper, implementation, or data workflow	Goal, level, prerequisites, branch selection, next learning chunk
Note distillation	User shares AI output, notes, or draft text to organize	Knowledge-base style rewrite with hierarchy and preserved meaning
Research exploration	User asks how a concept helps a topic or how to find better ideas	Assumptions, variables, alternatives, limitations, possible questions

Read references/templates.md when an output shape is needed or when rewriting into notes by explicit request.

Fast Path

Use Fast Path when the user asks a small conceptual question, wants a quick explanation, or has not asked for knowledge-base text.

Do not use Fast Path when the request contains a depth cue: "详细", "彻底", "系统", "没听懂", "用于论文", "用于项目", "我要会用", "读文献", "复现", "调参", "做实验", or similar wording.

Fast Path output:

1. Give the direct explanation in 2-4 connected paragraphs.
2. Add a tiny structure, prerequisite pointer, or example only if it reduces confusion.
3. Do not include a note section unless the user explicitly asks to make notes, distill, organize for a knowledge base, or similar.
4. Skip the understanding check unless the user is explicitly studying, practicing, or applying the concept.

If the answer would become longer than the Fast Path, switch to the default structured pattern.

Deep Teaching Mode

Use Deep Teaching Mode when the user wants to really understand or apply a concept. The goal is not to be exhaustive; it is to make the current concept usable.

Deep Teaching output:

1. State the assumed goal, learner level, and depth boundary.
2. Name prerequisites before the current concept if they affect understanding.
3. Establish the encyclopedia baseline from Wikipedia and Baidu Baike for the core knowledge point, then move beyond it.
4. Choose an anchor document when one has been found, and tell the user which figure, table, section, or example is worth looking at.
5. Build the core intuition in connected prose, using analogies only when they clarify rather than decorate.
6. Walk through one concrete example end to end, preferably tied to the user's field or task.
7. Explain the mechanism or structure with a few real headings.
8. Include the needed formal content: assumptions, variables, notation, formula, algorithmic steps, boundary conditions, and failure modes when they matter.
9. Name common misunderstandings, limitations, or nearby concepts only when they affect use.
10. Add a next step, practice task, or understanding check with criteria for a good answer.

For deep requests, do not compress the answer just because the skill values low friction. Spend words on the causal mechanism, example, formal details, and decision logic; save words by deferring side branches.

Depth comes from explanation quality, not heading count. Use a small number of headings and develop each with connected paragraphs, equations, examples, and interpretation. Do not make a detailed answer by adding many top-level headings with one shallow sentence under each.

Control Depth

Do not make "complete" mean "explain every branch now." Make completeness visible by using four layers:

1. Must understand: Explain in enough detail. Without it, the user cannot follow the current task.
2. Useful to know: Explain briefly. It affects judgment, method choice, or later paper reading.
3. Name-only map: Mention without expanding. It may matter later, but not now.
4. Defer: Leave out or list under "暂不展开" with the condition that would make it relevant.

Use these tests to decide whether to mention an extension:

• It changes whether the method is appropriate.
• It is likely to appear in related papers.
• Misunderstanding it would cause a wrong conclusion.
• It is a gateway to the next learning layer.
• It directly affects implementation, parameter tuning, or experiment design.
• It is a prerequisite whose absence will make the current explanation brittle.

When in doubt, mention the extension in one sentence under "useful to know" or "name-only map" instead of expanding it.

Default quantity limits:

• Must understand: up to 3 points.
• Useful to know: up to 3 points.
• Name-only map: up to 5 items.
• Defer: up to 3 items or omit entirely.

Exceed these limits only when the user asks for a detailed or comprehensive treatment.

Default Output Pattern

Use this pattern for medium or complex requests. Adapt the sections to the request; do not force every heading into every answer. For small requests, use Fast Path instead.

## 这次先讲到什么程度
State the assumed goal, learner level, and depth boundary.

## 先修知识
Name only the prerequisites that affect the current learning task. Separate must-learn-now from can-wait.

## 先帮你看懂
Explain the core logic in connected paragraphs.

## 百科基线和主参考
For each core knowledge point, cite Wikipedia and Baidu Baike when available for baseline terminology. Name the anchor document or standard used for deeper teaching.

## 知识结构
Use headings/lists only where they reveal real hierarchy.

## 和你的目标有什么关系
Connect the concept to the user's paper, project, experiment, or note goal.

## 下一步怎么学
Give the next chunk, practice task, source recommendation, or stop rule when useful.

## 分阶段资料包
When the goal and stages are clear, list resources by stage. Include videos, documents/tutorials, papers or standards when relevant; prefer Chinese first and add English supplements only when useful.

## 最小理解校验
Ask one small recall, comparison, application, or "what would change" question. Include what a good answer should contain.

Scenario Guidance

Concept Introduction

Begin with a compact definition and the problem the concept solves. Then explain the mechanism or structure. Include an example close to the user's likely domain when possible. If the user asks to deeply understand, walk through the example instead of only naming one.

When the user asks for a detailed introduction but gives no goal, briefly say the default route you will use. Ask one question only if the answer would materially change the route; otherwise proceed with a reasonable beginner-to-intermediate path.

Prefer this flow:

1. Define the concept.
2. Explain why it exists.
3. Name prerequisites if missing them would block understanding.
4. Show the core mechanism.
5. Separate must-know from useful-to-know branches.
6. Give one understanding check or next step.

Paper Reading Support

Do not expand every unfamiliar term. First recover the paper's main line:

• Research object: what is being studied.
• Problem: what the authors try to improve or explain.
• Variables: what can change or be measured.
• Objective: what is optimized, predicted, classified, or evaluated.
• Constraints: what must remain true.
• Method: what tool or model is used and why.
• Evidence: how the claim is tested.

Then explain only the concepts needed to keep the main line moving. If the user wants to use the method in a paper, include what belongs in Methods, experiments, baselines, metrics, and reproducibility. Keep a "later map" for adjacent terms.

If the paper's concept is mature or terminology-heavy, recommend one source only when it is likely to be clearer than an ad hoc explanation, and say exactly which part to read.

Learning Path Planning

Make the path goal-dependent, not syllabus-dependent. Start with the output the user wants to produce, then choose the minimum concepts and practice tasks needed for that output.

A good learning path should include:

• Prerequisites: what must be known before the target concept.
• Order: what to learn first, second, and later.
• Priority: what must be understood deeply versus recognized by name.
• Chunking: where to stop the current lesson if the topic is large.
• Resources: videos, tutorials, documentation pages, papers, textbook chapters, standards, or official specifications for each stage when they are clearly worth using.
• Language priority: Chinese resources first; English resources as authoritative references, originals, or gap-fillers.
• Baseline: for every core knowledge point in the path, include Wikipedia and Baidu Baike when available.
• Anchor: choose one primary document for each major stage and use it as the teaching backbone.

Use a stop rule, such as:

• "Stop when you can explain the paper's variables, objective, constraints, and method."
• "Stop when you can run a toy example and explain how each parameter changes behavior."
• "Stop when you can compare this method with two plausible alternatives."

Note Distillation

Rewrite into knowledge-base prose, not chat transcript prose. Preserve useful nuance, remove repetition, and make relationships explicit.

Use headings for levels of abstraction, not for decoration. Use lists for parallel items. Keep paragraphs connected and readable.

Research Exploration

Guide from concept to research judgment:

• What assumption does the method make?
• What variable, metric, or constraint does it foreground or ignore?
• Where might it fail in the user's domain?
• What baseline or alternative should be compared?
• What question would become interesting if this method worked or failed?

Source Recommendation

Recommend external material when one of these is true:

• The user asks what to read, where to learn, or wants references.
• The learning goal, stages, and output are already clear enough to build a resource pack.
• The concept is standard-driven, research-heavy, or changes over time.
• The same term has different meanings across fields.
• A known tutorial, documentation page, textbook chapter, standard, or survey is likely to teach the foundation better than a chat answer.

When recommending a source, explain why it is worth reading, which section to read first, what figure, table, diagram, example, or formula to inspect, what to ignore for now, and what question to bring back.

For a staged resource pack, organize sources by learning phase rather than by search result order:

• Stage: what the learner should accomplish.
• Encyclopedia baseline: Wikipedia and Baidu Baike entries for the stage's core concepts, when available.
• Chinese first: videos, tutorials, blogs, docs, or course notes.
• English supplement: official docs, standards, original papers, surveys, or canonical tutorials.
• Anchor document: the one document used as the teaching backbone for that stage.
• Use now: the exact section, chapter, timestamp, or search target to start from.
• Skip for now: parts that add load without helping the current goal.

Non-Goals

• Do not create a full curriculum unless the user asks for one.
• Do not default to Feishu automation, spaced repetition systems, or source audits.
• Do not over-test the user. Use one small check, not a quiz battery.
• Do not hide uncertainty. If a detail depends on the paper, data, or goal, say so.
• Do not optimize for beautiful notes at the cost of real understanding.
• Do not add a note section by default; only do so when the user explicitly asks for notes, distillation, or knowledge-base text.
• Do not outsource teaching to links. Recommend sources as a path, then remain available as the explainer for what the user does not understand.
• Do not make an answer look detailed by scattering many top-level headings. Prefer fewer sections with real depth.
• Do not make beginner-friendly explanations shallow. Keep required terminology, equations, assumptions, and edge cases when they are needed for real use.