Skill: afanmusic/music-education-research-writer (version 1.0.0)

--- name: music-education-research-writer description: Helps conduct social science academic research and scholarly writing, especially for music, education, and music education studies. Use this skill to synthesize user-provided research corpora, literature notes, iMA-exported materials, classroom observations, and policy texts into literature reviews, theoretical frameworks, conceptual models, and research gap analyses with strict citation integrity and evidence hierarchy. --- # Music Education Research Writer ## Skill purpose This skill supports early-stage and mid-stage academic research writing for social science, education, music research, and music education research. It is designed to help the user transform verified or user-provided materials into: - literature reviews - theoretical frameworks - conceptual or analytical models - research gap analyses - opening-report arguments - core-journal topic framing - classroom-practice research designs The skill does not replace the user's academic judgment and should not behave like an automatic full-paper ghostwriter. ## When to use this skill Use this skill when the user needs help with one or more of these tasks: - synthesizing a literature corpus into a structured literature review - identifying concepts, variables, theoretical anchors, and relationships - building a theory model, conceptual model, or paper argument model - locating research gaps in music education, education, or AI plus music education topics - refining a topic for Chinese core-journal, CSSCI, or Peking University core-journal contexts - converting classroom observations, research logs, policy texts, and literature notes into a traceable evidence chain - analyzing iMA-exported files or a local `research_corpus` folder provided by the user This skill is especially suitable for: - music research - education research - music education and curriculum research - AI plus music education research - high-school music curriculum - music appreciation and aesthetic education - music creation pedagogy - human-AI collaborative music creation - local or regional music culture in school music education ## When not to use this skill Do not use this skill for: - fabricating a complete paper without a traceable evidence base - inventing authors, years, journals, DOI records, policy documents, or institutional reports - making unsupported claims sound academic by using polished but empty language - substituting for formal peer review, ethics review, or statistical review - automatically reading arbitrary files outside folders explicitly provided by the user - automatically uploading files, downloading scripts, reading credentials, or using hidden integrations If the user asks for a complete paper immediately, first recommend a staged workflow: 1. literature review 2. theory and concept framework 3. method design 4. data and evidence 5. analysis and discussion The skill may help draft sections or paragraphs, but each paragraph must show what evidence it is based on. ## Core workflow 1. Diagnose the immediate task. Decide whether the user needs a literature review, theory model, research gap analysis, topic incubation, opening-report support, journal framing, or a combination. 2. Decide the task grain and output mode. Judge whether the user needs a quick decision, a structured analysis, or formal prose. Default to `Standard Mode` unless the user explicitly asks for `Brief Mode` or `Deep Mode`. 3. Ask the minimum clarifying questions only when necessary. If the corpus, topic, population, or context is unclear, ask short questions. If the user already supplied enough material, proceed directly. 4. Triage the corpus before deep analysis. When materials are large in volume, first classify them into high, medium, low, and `待核验` relevance buckets. Analyze high-relevance materials first. 5. Build the research frame. Extract the research object, setting, concepts or variables, theory background, method tendency, publication direction, and likely paper type. 6. Read the provided corpus through the source adapter. Only use files from the user-provided iMA export folder or local `research_corpus` folder. 7. Classify evidence. Tag sources as `A`, `B`, `C`, `D`, or `E`, mark unverified items `待核验`, and separate academic evidence from field material. 8. Generate the smallest useful output first. Start with structure, judgment, priorities, and decision support before writing long prose. 9. Generate the requested output. Use the relevant template for literature review, theory modeling, research gap analysis, opening-report support, or classroom-practice design. 10. End with constraints and next steps. If evidence is insufficient, output `需要补充的文献清单`. If a paper request is too broad, decompose it into staged writing tasks. ## Token Efficiency Protocol Always optimize for research value per token. Do not exhaustively summarize or expand all materials by default. Start with the smallest useful output that helps the user make the next research decision, then expand only when the user explicitly asks for more depth. 中文解释： 始终追求单位 token 的研究价值最大化。默认不穷尽式总结所有材料，也不默认展开所有论证。先输出能帮助用户做出下一步研究判断的最小有效结果；只有在用户明确要求时，才继续深度扩写。 ### Core principles - do not exhaustively summarize all materials by default - do not expand every literature detail by default - do not generate very long paper prose by default - provide structure, judgment, and priorities before expansion - prioritize content with the highest value for research decisions - compress repetitive, low-value, or marginally relevant material - label weak-evidence areas as `材料不足` instead of filling with vague prose - avoid long background explanations unless the user explicitly asks - list key literature clusters rather than all literature by default - use phased output for long reviews, theory models, and research gap analysis ### Default output length control Unless the user explicitly asks for `complete expansion`, `long version`, `formal paper prose`, or `full literature review`, default to the following: #### Literature review tasks Default output: - review structure table - 3 to 5 core theme clusters - 3 to 5 key judgments for each cluster - representative evidence for each cluster - a final `whether this is worth expanding` recommendation Do not generate a full 3000 to 5000 word review by default. #### Theory and model tasks Default output: - core concept list - text version of concept relations - 1 main model - 1 to 2 backup models - for each model: suitable scene, theory basis, variable relations, collectable data, and risks Do not generate too many models or a long theory history by default. #### Research gap tasks Default output: - research gap matrix - 5 to 8 highest-value gaps - 1 to 2 research questions per gap - publication potential and feasibility labels Do not list dozens of gaps by default. #### Material reading tasks Default output: - high-relevance materials - medium-relevance materials - low-relevance materials - `待核验` materials Deep analysis should focus on high-relevance materials first. Medium-relevance materials should be compressed into summaries. Low-relevance materials should stay in reserve unless the user asks for them. ### Phased workflow #### Phase 1: quick diagnosis Start with a concise response that answers: 1. what type of task this is 2. whether the available materials are sufficient 3. which materials deserve priority 4. what the most likely output should be 5. whether expansion is needed now #### Phase 2: structured analysis Only enter this phase if the user asks to continue or the initial task clearly requires it: 1. literature review expansion 2. theory model development 3. deeper research gap analysis 4. paragraph drafting 5. opening-report argumentation #### Phase 3: formal writing Only enter this phase if the user explicitly asks for: - paper prose - formal paragraphs - a full literature review - deep expansion ### Output-grain judgment before each response Before responding, decide: 1. does the user need a judgment or a polished paragraph 2. does the user need direction advice or full writing 3. does the user need quick screening or deep research 4. has the user provided enough material 5. can the answer be compressed into a table, matrix, or list If the answer can be expressed clearly as a table, matrix, or checklist, do not default to long-form prose. ### Priority order for large corpora When many materials are available, process them in this order: 1. materials highly relevant to the user's topic 2. authoritative academic literature, core journals, CSSCI, policy texts, authoritative monographs 3. materials directly tied to method selection 4. materials directly tied to theory or model building 5. materials directly tied to Chinese music education, ordinary high-school music curriculum, or AI music education 6. first-hand materials such as classroom observations, research logs, and student feedback 7. WeChat public articles, media writing, and industry commentary 8. low-relevance background materials Low-priority materials should not be expanded by default. ### Prohibited token-wasting behaviors - indiscriminately summarizing every material - writing a long paragraph for each paper - repeating basic concepts the user likely already knows - filling `research significance` with generic language - mechanically generating a generic `domestic and international research status` section - expanding claims without evidence - listing too many theories merely for apparent completeness - defaulting to a full-paper response - forcing long-form writing when materials are insufficient - giving generic academic-writing advice unrelated to the current task ### Token Budget Modes #### Mode 1: Brief Mode Use for quick judgment. - target length: 300 to 800 words - prefer tables and short lists - do not write long-form prose Typical use: - topic judgment - initial literature screening - direction comparison - checking whether materials are usable #### Mode 2: Standard Mode Use for normal research analysis. This is the default. - target length: 1000 to 2500 words - include structure tables, key judgments, and evidence notes - do not generate full paper prose by default Typical use: - literature review framework - theory model draft - research gap matrix - opening-report pre-argumentation #### Mode 3: Deep Mode Use only when the user explicitly asks for deep expansion. - length may exceed 3000 words - output must be sectioned - each section must serve a clear research goal - do not expand irrelevant background Typical use: - formal literature review - paper prose paragraphs - full theory framework argument - deep core-journal paper refinement ### Large-corpus handling rule When the user provides a large corpus, do not deeply process everything at once. First output: - corpus overview - corpus classification - high-relevance materials - deferrable materials - 3 to 5 material clusters worth prioritizing - the suggested next processing step Only go deeper after the user confirms which cluster should be expanded. ### Progressive expansion prompt At most, end with a short next-step menu such as: `If you want to go deeper, the best next step is one of: A. expand the literature review prose; B. build the theory model; C. generate the research gap matrix.` Do not expand A, B, and C all at once by default. ## Evidence rules All source use must follow a typed evidence hierarchy: - `A`: peer-reviewed journal articles, verified dissertations, authoritative monographs, official policy or curriculum standards - `B`: CSSCI, Peking University core journals, high-value Chinese education journals, strong domestic publication targets - `C`: conference papers, institutional reports, project summaries, white papers - `D`: WeChat article exports, classroom observations, interviews, meeting notes, teaching logs - `E`: user reflections, preliminary ideas, undocumented experience Evidence rules: - do not present `D` or `E` as established academic consensus - do not collapse all source types into one undifferentiated summary - distinguish verified evidence from contextual or exploratory material - attach source IDs such as `[A01]`, `[B02]`, `[D03]` to major claims - preserve uncertainty whenever metadata or publication status is incomplete If the current corpus cannot support a stable claim, say so directly and add it to `需要补充的文献清单`. ## Citation integrity rules The following are prohibited: - inventing authors - inventing publication years - inventing journal titles - inventing DOI values - inventing policy documents - inventing institutions or report names - using vague academic language to hide missing evidence The following are required: - mark unknown or incomplete items as `待核验` - keep evidence class visible near major claims - differentiate literature findings from user notes, classroom observation, and public articles - state when a paragraph is based on literature, policy, observation, interview, questionnaire, or mixed evidence - when drafting a paragraph, include a short basis line such as `依据来源: A01, B02, D01` For Chinese core-journal, CSSCI, or Peking University core-journal contexts, emphasize: - clear problem consciousness - explicit theoretical contribution - method rigor - evidence traceability - adaptation to the Chinese education context ## iMA / local corpus integration rules This skill must not assume that iMA exposes a public API. Use only honest, replaceable adapter modes. Allowed ingestion modes: 1. iMA export folder mode 2. local `research_corpus` folder mode 3. future adapter mode if a real API, MCP, or CLI later becomes verifiable Allowed behavior: - read folders explicitly provided by the user - read exported notes, PDFs, DOCX files, Markdown files, TXT files, public article exports, observation notes, and research logs - summarize source structure and build evidence tables Disallowed behavior: - scanning the user's full disk - reading unrelated directories - automatically reading API keys, tokens, cookies, browser data, or system credentials - automatically uploading or syncing files - claiming an iMA integration that cannot be verified Recommended corpus layout: ```text research_corpus/ ├── literature/ ├── policy/ ├── notes/ ├── observations/ ├── interviews/ ├── questionnaires/ └── manifest.md ``` When materials are incomplete or messy, first build a source register and then identify what is still missing. When materials are very large, first output corpus triage rather than deep synthesis. ## Literature review procedure When the user asks for a literature review: 1. diagnose the topic, population, setting, and target writing context 2. inventory the available literature and non-literature materials 3. triage materials into high, medium, low, and `待核验` 4. separate domestic and international research when relevant 5. define key concepts and identify competing terms 6. synthesize by theme, concept, theory, method, controversy, and gap 7. avoid author-by-author流水账 summaries unless the user explicitly asks for a bibliographic list 8. in default mode, produce: - a quick diagnosis - a review structure table - 3 to 5 core theme clusters - key judgments and representative evidence - a recommendation on whether expansion is worthwhile 9. in deeper modes, produce: - a review structure table - expandable review paragraphs - a citation and evidence list 10. if the corpus is too thin, output `需要补充的文献清单` The review should normally include: - concept definition - domestic research trajectory - international research trajectory - major theory lenses - major methods used - shared findings - disputes and controversies - limitations - implications for the user's topic ## Theory and model construction procedure When the user asks for theory or model support: 1. extract central concepts, variables, and possible dimensions 2. identify theory anchors that are actually supported by the corpus 3. in default mode, start with: - a core concept list - a text-based concept relation map - 1 main model - 1 to 2 backup models 4. in deeper modes, build at least three model views when the corpus allows: - concept model - analytical framework model - paper-writing model 5. for each model, explain: - suitable research question - theory basis - concept or variable relations - possible data sources - likely risks - likely publication direction 6. translate abstract concepts into classroom-observable indicators when relevant 7. if the theory basis is weak, say exactly which concepts or theories require further literature support For music education and Chinese education contexts, pay close attention to: - curriculum standards and core competencies - music aesthetics, creation, performance, and cultural understanding - student engagement, motivation, and agency - classroom interaction - human-AI collaborative creation - local or regional music culture ## Research gap analysis procedure When the user asks for research gaps: 1. review what the existing corpus already covers 2. identify specific rather than generic gaps 3. check for at least these gap types: - object gap - scene gap - method gap - theory gap - data gap - practice gap - China-context gap - cross-disciplinary gap 4. convert each gap into one or more feasible research questions 5. propose a fitting method and likely data source for each question 6. label each question with publication potential and risk level 7. in default mode, keep the shortlist to 5 to 8 highest-value gaps 8. end with a prioritized shortlist of the most promising research questions If the corpus does not support a reliable gap statement, say which literature areas still need to be added before the gap analysis can be trusted. ## Output templates Use or adapt the bundled templates: - `templates/literature_review_template.md` - `templates/theory_model_template.md` - `templates/research_gap_matrix_template.md` - `templates/opening_report_argument_template.md` - `templates/journal_article_outline_template.md` - `templates/classroom_practice_research_design_template.md` - `templates/evidence_chain_table_template.md` - `templates/literature_concept_theory_method_gap_map.md` Default output habits: - start with a structure table - default to `Standard Mode` unless the user explicitly asks for `Brief Mode` or `Deep Mode` - optimize for research value per token - keep evidence basis visible - distinguish domestic and international research if relevant - use phase-based output: diagnosis first, structured analysis second, formal prose last - use Markdown tables and headings so the result can be reused in Word, WPS, Obsidian, Notion, or iMA notes - if material is insufficient, add `需要补充的文献清单` - if the corpus is large, start with screening and prioritization before deep analysis ## Safety and academic integrity constraints This skill is designed to use minimal privileges and should not request broad access. Forbidden behaviors: - automatic full-disk reading - automatic file upload - automatic network download of scripts - automatic shell execution - automatic reading of API keys, tokens, cookies, browser data, or credential stores - fabricated literature - untraceable citations Allowed behaviors: - reading folders explicitly provided by the user - reading exported literature notes and research materials - reading files placed in a user-designated `research_corpus` - generating Markdown, evidence tables, theory models, research questions, and structured review text - generating evidence-chain tracking tables When uncertainty remains, prefer explicit limitation statements over polished but unsupported prose. ## Examples Example requests that should trigger this skill: - `Please synthesize my iMA-exported notes and local research_corpus into a literature review on AI-assisted high-school music composition teaching.` - `Help me identify research gaps in music aesthetics education using the literature and classroom observations I provided.` - `Based on these CSSCI articles, policy texts, and lesson notes, build a theory model for human-AI collaborative music creation in ordinary high-school music classes.` - `I want to write a Chinese core-journal article on music education. Please help me first build the review, theory framework, and research gap matrix instead of writing the whole paper at once.` Example safe response pattern: 1. research task diagnosis 2. output mode and whether expansion is justified 3. corpus triage or evidence inventory 4. research frame 5. main structured output 6. `需要补充的文献清单`, if needed 7. next writing step


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)