Research & Learning skills

Use when the user needs to plan, de-risk, or ground a software engineering / computer science undergraduate thesis from a real codebase before final writing. Trigger for requests such as "根据项目写毕业论文", "先做论文大纲", "用真实项目材料规划论文", "检查论文是否脱离代码", "根据检测报告降查重", "根据AIGC报告改写定稿", "继续在手改初稿上改", or when an existing draft thesis must be reworked against a school format sample. Produces an evidence-backed outline, chapter rewrite plan, figure plan, and handoff package for academic-paper-composer.

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Use when the user needs to turn a real software engineering / computer science project and an existing thesis draft into a submission-ready undergraduate thesis manuscript. Trigger for requests such as "根据项目把论文改成定稿", "按学校模板排版成最终版", "复制初稿后生成定稿 Word", "为定稿降查重", "根据PaperPass报告降AIGC", "继续在手改初稿上改", "恢复原来的图表和数据库说明", or when academic-paper-strategist has already produced an evidence-backed rewrite plan. Outputs a cleaned manuscript, final DOCX workflow, and a separate rework report.

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Show how vibecosystem works - agents, skills, hooks, and self-learning pipeline.

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Pre-search topic resolution. Maps vague queries to concrete entities (GitHub orgs, X handles, subreddits, docs URLs) before searching. Run as first step of any research workflow to dramatically improve search precision.

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Analyze repository structure, patterns, conventions, and documentation for understanding a new codebase

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Research agent for external documentation, best practices, and library APIs via MCP tools

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External research workflow for docs, web, APIs - NOT codebase exploration

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Query the memory system for relevant learnings from past sessions using semantic search.

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Store a learning, pattern, or decision in the memory system for future recall.

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Document codebase as-is with thoughts directory for historical context

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AI-powered web search, research, and reasoning via Perplexity

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Migration workflow - research → analyze → plan → implement → review

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Hizlica kural/ogrenim kaydet. CLAUDE.md'ye ve memory'ye yazar. Kullanim: /learn <kural>

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Interactive workspace discovery - learn what tools, workflows, agents, and hooks are available

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Automatically extract reusable patterns from Claude Code sessions and save them as learned skills for future use.

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Instinct-based learning system that observes sessions via hooks, creates atomic instincts with confidence scoring, and evolves them into skills/commands/agents.

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Transform session learnings into permanent capabilities (skills, rules, agents). Use when asked to "improve setup", "learn from sessions", "compound learnings", or "what patterns should become skills".

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Implements onboarding and help systems including product tours, interactive tutorials, tooltips, checklists, help panels, and progressive disclosure patterns. Use when building first-time experiences, feature discovery, guided walkthroughs, contextual help, setup flows, or user activation features. Provides timing strategies, accessibility patterns (keyboard, screen readers, reduced motion), and metrics for measuring onboarding success.

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Execute exactly one eligible Unit in an existing research Workspace; use for stepwise or manual semantic execution when status, Attempt, Artifact, Manifest, checkpoint, and acceptance evidence must remain synchronized.

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Build per-H3 writer context packs (NO PROSE): merge briefs + evidence packs + anchor facts + allowed citations into a single deterministic JSONL, so drafting is less hollow and less brittle.

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Initialize a missing research Workspace from the repository template without overwriting existing Run state; use before Pipeline binding when the target under `workspaces/` has no valid core artifacts.

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Run a tutorial-specific quality gate and write a PASS/FAIL report for the final tutorial deliverable.

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Use when module planning and source coverage are done and the run needs writer-ready per-module packs.

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Remove repeated boilerplate across sections (methodology disclaimers, generic transitions, repeated summaries) while preserving citations and meaning.

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