Knowledge Learning Article
Use this skill only after source-gated-evidence-layer has produced a current
gate_receipt.json, analysis_receipt.json, and audited analysis pack.
- Read
references/analysis-protocol.md before interpreting source artifacts.
- Read
references/artifact-schema.md before writing learning enrichment or changing data fields.
- Validate upstream provenance. Never treat file existence as freshness.
- Inspect semantic inventory completeness before enrichment. Block when source claims are absent. Block empty, unanchored, or heuristic concept/example/argument inventories unless an explicit evidence-bound source reanalysis is prepared.
- If reanalysis is required, read
references/evidence-reanalysis-contract.md, return to the admitted normalized source, and validate every Source row against real IDs, ranges, verbatim excerpts, and support rationales. Reanalysis is never permission to fill empty fields from general knowledge.
- Keep the source's original order separate from the recommended learning order.
- Organize the article by concepts, relationships, examples, and learning dependencies. Keep timestamps only as evidence locators.
- Create
15_learning/learning_enrichment.json for standard or deep work. Ground every Source statement in upstream evidence; label reconstructed relationships, prerequisites, learning order, and advice as Inference or Extension, and disclose that learning-design boundary near the article opening.
- Run the complete pipeline:
python scripts/learning_pipeline_runner.py --project-root <project> --learning-goal "<goal>" --audience "<audience>" --learner-level <level> --final-language zh-CN --depth standard
- Read
references/article-template.md when revising the generated candidate.
- Read
references/quality-gates.md before delivery. Deliver only when
20_document/learning_article_receipt.json is current and
learning_quality_gate.json.approved_for_learning_article is true.
The deterministic baseline may be used for brief output only when the upstream
semantic inventory passes completeness checks. For standard or deep output,
enrich the audited source structure first so the result contains
real concept relationships, prerequisites, transfer patterns, priorities, and a
task-shaped learning path.
Do not acquire data, repair source status, invent missing claims or source
sequence, append a full transcript to the article, or route degraded/secondary-
only material into a normal learning article. Source sequence may be
semantically re-segmented only inside declared evidence-bound reanalysis.
1---2name: knowledge-learning-article3description: Transform a current provenance-checked source_analysis_pack or video_analysis_pack into a knowledge map, argument graph, prerequisite map, learning path, and evidence-grounded article optimized for personal learning. Use when the user asks what is worth learning from acquired videos, transcripts, articles, repositories, or other source-gated material; wants systematic understanding instead of timestamp summaries; asks how to learn the material; requests a learning article, concept map, prerequisite order, transferable methods, or study path; or needs explicit evidence-bound reanalysis of an incomplete semantic inventory without inventing Source content.4---56# Knowledge Learning Article78Use this skill only after `source-gated-evidence-layer` has produced a current9`gate_receipt.json`, `analysis_receipt.json`, and audited analysis pack.10111. Read `references/analysis-protocol.md` before interpreting source artifacts.122. Read `references/artifact-schema.md` before writing learning enrichment or changing data fields.133. Validate upstream provenance. Never treat file existence as freshness.144. Inspect semantic inventory completeness before enrichment. Block when source claims are absent. Block empty, unanchored, or heuristic concept/example/argument inventories unless an explicit evidence-bound source reanalysis is prepared.155. If reanalysis is required, read `references/evidence-reanalysis-contract.md`, return to the admitted normalized source, and validate every Source row against real IDs, ranges, verbatim excerpts, and support rationales. Reanalysis is never permission to fill empty fields from general knowledge.166. Keep the source's original order separate from the recommended learning order.177. Organize the article by concepts, relationships, examples, and learning dependencies. Keep timestamps only as evidence locators.188. Create `15_learning/learning_enrichment.json` for `standard` or `deep` work. Ground every Source statement in upstream evidence; label reconstructed relationships, prerequisites, learning order, and advice as Inference or Extension, and disclose that learning-design boundary near the article opening.199. Run the complete pipeline:2021```powershell22python scripts/learning_pipeline_runner.py --project-root <project> --learning-goal "<goal>" --audience "<audience>" --learner-level <level> --final-language zh-CN --depth standard23```242510. Read `references/article-template.md` when revising the generated candidate.2611. Read `references/quality-gates.md` before delivery. Deliver only when27 `20_document/learning_article_receipt.json` is current and28 `learning_quality_gate.json.approved_for_learning_article` is true.2930The deterministic baseline may be used for `brief` output only when the upstream31semantic inventory passes completeness checks. For `standard` or `deep` output,32enrich the audited source structure first so the result contains33real concept relationships, prerequisites, transfer patterns, priorities, and a34task-shaped learning path.3536Do not acquire data, repair source status, invent missing claims or source37sequence, append a full transcript to the article, or route degraded/secondary-38only material into a normal learning article. Source sequence may be39semantically re-segmented only inside declared evidence-bound reanalysis.
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