# Peter Zhou Knowledge Learning > Create reusable graph-grounded system knowledge lessons for recurring weak topics. Use for learning-unit selection, agent teaching drafts, controlled explanatory animation, linked practice, and archived HTML courseware. - Skill: `szsip239/peter-zhou-knowledge-learning` (Agent Skill) - Install (CLI): `npx skillmds@latest add szsip239/peter-zhou-knowledge-learning` - Raw SKILL.md: https://api.skillmd.com/api/skills/szsip239/peter-zhou-knowledge-learning/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Web & Frontend - Author: szsip239 (https://skillmd.com/u/szsip239) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/szsip239/peter-zhou-knowledge-learning --- # Knowledge Learning Use this subskill when the user asks to learn a knowledge point, a weak tag surfaced by overview, or a larger system topic such as "指数方程中的分类讨论" rather than one surface wrong-question tag. Core contract: - Use `scripts/workflow.py start --kind knowledge_courseware` after graph selection and verified practice are ready. The workflow stops only for `graph_teaching_draft.v1`, then scripts build and validate the courseware. - Normalize subject and knowledge tag. - Check `scripts/learning_profile.py show --data-dir --json` and use grade/stage, explanation preference, difficulty preference, goals, and weak subjects when present. For deep modules, `build-context` reads `LearningProfile` automatically when available and marks the Phase 1 contract as `source="inferred"` when it is missing; do not block module generation solely because the profile has not been created yet. - For deep learning, first select a bounded graph learning unit. The graph owns the topic boundary, prerequisite order, target skills, misconceptions, and verified exercises. - If a broad tag would pull unrelated evidence into a system topic, pass curated `--wrong-question-id ` values so the module is grounded in the exact wrong questions the agent selected. - Render the archival continuous deep module with `scripts/knowledge_learning.py render-page --data-dir --context --json` when a long reference page is useful. - Use `scripts/knowledge_learning.py build-courseware` as the only default student-courseware entry. It builds the context, renders HTML, and runs semantic validation in one command. Do not separately call `render-courseware` as a shortcut. - Start every source-backed junior-high module with one deterministic preflight: `scripts/knowledge_graph.py prepare-courseware-inputs --data-dir --subject --topic --json`. It audits packaged sources, validates the normalized seed, resolves or bootstraps the topic package, builds/reuses the topic slice and verified exercise pool, and returns exactly one structured `next_action`. Do not manually replay its internal commands during the normal path. - Keep recognition-time `knowledge_tags[]` as lightweight display/aggregation fields. Use optional `knowledge_refs[]` only after a reviewed topic overlay maps a wrong question to stable graph nodes. Every ref carries `topic_id`, `graph_version`, `node_id`, `role` (`primary`, `secondary`, or `prerequisite`), and a short rationale. - Follow the preflight `next_action` literally. When it requests `knowledge_courseware_preparation_choice.v1`, choose one listed coherent learning unit and one or more listed, relevant wrong-question ids, write only that choice file, then invoke the returned `resume_argv`. The resumed command validates the choice, creates the bounded graph selection and three verified practice tasks, and returns the exact `knowledge_courseware` workflow start command. - If preflight returns `bootstrap_required`, the running Agent writes only the requested `knowledge_topic_overlay.v1` candidate to `output_ref`, using the supplied graph and mistake-bank refs, then invokes `resume_argv`. The script validates and archives it as `agent_generated`; packaged topics remain `packaged_reviewed`. Final selection and courseware metadata preserve `asset_origin`, `verification_status`, and `source_refs`. - Keep `audit-sources`, `validate-seed`, `resolve-topic-package`, `build-topic-slice`, `select-subgraph`, exercise-pool commands, and `archive-agent-topic` as script-owned diagnostics/repair primitives. They are not the normal Agent orchestration path. Never let the Agent hand-edit mistake-bank arrays or student-facing grading keys. - In rendered practice, keep the correct answer collapsed. `显示答案` stays disabled until the student enters and submits that task's answer; submission locks the answer field, then enables the separate answer toggle. Downloaded submission JSON continues to carry the student's answer for later Agent grading. - After the topic package is resolved, the running agent creates only `graph_teaching_draft.v1` for the courseware stage. The draft contains one entry, exactly three mechanism cards, one summary, and semantic animation specs. Every mechanism card must cite selected node ids. The agent may write friendly explanations, analogies, and minimal examples, but it must not alter the resolved knowledge scope, prerequisite path, linked evidence, or practice set. - Build graph-backed courseware with `build-courseware --knowledge-selection --knowledge-practice --teaching-draft `. The script owns evidence quality filtering, context assembly, `graph_lesson_blueprint.v1`, rendering, metadata, and final validation. - `render-courseware` rejects any context without `graph_lesson_blueprint.v1`. Do not retry through the historical topic-name/TeachAny renderer. `render-page` remains the archival continuous-page path for historical contexts. - If preflight resolution finds no topic package, do not call lower-level commands manually. Complete its one explicit bootstrap task and resume the same preflight. The candidate must use `asset_origin="agent_generated"` and `verification_status="agent_verified"`, include provenance plus exactly three verified prerequisite/core/transfer exercises, and must never enter the Skill's packaged overlay directory or claim `packaged_reviewed`. - One eligible wrong question is enough to ground a deep module; three wrong questions are not required. The separate requirement is exactly three verified practice roles. If the agent cannot verify the candidate, any exercise, or a coherent learning unit, mark the uncertainty outside the archive and stop. Do not bypass the archive validator or claim that courseware was completed. - Agent topic archives record `generated_by`, `generated_at`, `base_graph_version`, `source_refs`, `content_hash`, and `validation_report`. Same-hash regeneration is reused. Different content requires explicit `--refresh`. If a packaged topic later appears, it takes precedence while the Agent archive remains a distinguishable audit asset. - Wrong-question evidence is script-filtered before it can influence the lesson or mastery links. Records with total `confidence < 0.70`, or without visible question, student answer, or mistake analysis, are listed under `excluded_wrong_question_evidence` and are not linked to the generated courseware. - Content target: 55% graph/verified exercises, 25% current-model teaching translation, 15% student mistake evidence, and 5% fixed template copy. Previously frozen Agent prose may contribute at most 5% and cannot be the source of a knowledge claim. - A deep module is not a wrapper around one tag and is not a wrong-question explanation. Wrong questions select emphasis; they do not define the entire lesson sequence. - Wrong questions are supporting evidence for why the topic matters. They should not be the main teaching path, the only worked example, or the default Xiaohei prompt input. - Use the same pixel/Minecraft visual language and wide-screen useful canvas as the current Peter Zhou pages. The default student HTML is exactly six cards: entry/diagnosis with visible eligible mistake evidence, three graph-grounded mechanisms, three linked exercises, and one exam-check summary. - Do not add animation to entry, practice, or summary cards. Each mechanism card has one teaching animation with a single stated goal. - Animation defaults to paused. It provides previous, next, optional auto-play, and replay controls. Steps persist earlier mathematical states, highlight only the change, and finish with a concrete student check. Decorative card motion does not satisfy the contract. - Use a subject visual adapter only when it reveals a mechanism: algebra area/formula morph, geometry construction, physics state/force evolution, or another causal change. Prefer a static figure when time does not matter. - Reuse the adapted `xiaohei-illustration/` child skill for default knowledge-module comics: `concept_anchor`, `solution_path`, and `trap_guardrail`. - For each default comic, build the prompt with `scripts/knowledge_learning.py build-xiaohei-prompt --data-dir --context --purpose --output --json`, generate a real original Xiaohei bitmap when image generation is available, copy it under `data/knowledge-modules///assets/`, then attach it with `scripts/knowledge_learning.py attach-xiaohei-image --data-dir --context --purpose --image-ref --json`. - Re-render the module after attaching images. If image generation is unavailable or a generated image fails QA, keep the shot list and saved prompt without blocking the learning artifact. - Treat overview `knowledge_page_recommendations[]` as lightweight suggestions. If the recommendation is `status="recommended"`, ask for user confirmation before generating a page. If it is `status="archived"`, reuse the existing `html_ref`. - Use `scripts/knowledge_overview.py knowledge-page` only for the existing lightweight subject/tag KnowledgePage path. - Store/reuse lightweight page metadata through `data/knowledge-pages/index.json`. - Use `--refresh` only when an archived page should be regenerated after template/content improvements or user-requested correction. - Generate local HTML focused on the minimum graph-selected first principles, mechanism animations, examples, common errors, three linked exercises, and a reusable final check. Extra illustrations or games are optional assets and must not expand the default six-card path unless the user requests them. - Do not create learning-session or completion-tracking tables. Distinction: - `ExplanationArtifact`: teaches one wrong question. - `KnowledgePage`: lightweight reusable page for one normalized weak tag. - `KnowledgeModuleContext`: archived system-topic evidence plus a validated graph lesson blueprint generated from multiple wrong-question signals.