学术
学术写作、研究与引用
literature-search
lingzhi227
Search academic literature using Semantic Scholar, arXiv, and OpenAlex APIs. Returns structured JSONL with title, authors, year, venue, abstract, citations, and BibTeX. Use when the user needs to find papers, check related work, or build a bibliography.
deep-research
lingzhi227
Conduct systematic academic literature reviews in 6 phases, producing structured notes, a curated paper database, and a synthesized final report. Output is organized by phase for clarity.
paper-assembly
lingzhi227
Orchestrate the full paper pipeline end-to-end. Manage state propagation between phases (literature → plan → code → experiments → figures → tables → writing → review), support checkpointing and resumption. Use for assembling a complete paper from components.
figure-generation
lingzhi227
Generate publication-quality scientific figures using matplotlib/seaborn with a three-phase pipeline (query expansion, code generation with execution, VLM visual feedback). Handles bar charts, line plots, heatmaps, training curves, ablation plots, and more. Use when the user needs figures, plots, or visualizations for a paper.
atomic-decomposition
lingzhi227
Decompose research ideas into atomic, self-contained concepts with bidirectional math-code mapping. For each concept, extract the math formula from papers and find code implementations. Use for complex system papers requiring formal grounding.
deep-research
youlianvr
"Run a disciplined, multi-source research investigation for a high-stakes question or decision — fan-out web search across many channels, parallel batched searches, source triangulation (each claim backed by ≥3 independent sources), an adversarial review pass, and every source saved to its own file with verbatim quotes for reuse. Use when a low-quality answer is expensive: strategy work, comparing N products/methods/markets, validating a hypothesis with external data, or mapping how a field works. NOT for quick fact-checks (answer directly), structured 12-dimension competitor scoring (use competitive-teardown), or fast topic overviews where the decision risk is low (use the research router instead)."
aggg2-persona
youlianvr
Use for ANY communication with the user — how to speak and respond: when to explain simply and step by step, when briefly, how to react to a difficult situation, how to verify your own answers, how to reduce the interlocutor's load, how not to pass guesses off as facts. Covers: tone and style (calm, human, no sugarcoating), adaptation to the interlocutor (pace, level, mood), "explain step by step" mode, how to explain complex things (audience → layers → analogy → comprehension check), visual presentation (tables, hierarchy, highlighting, diagrams), importance of no hallucinations (fact checking, "verify" instead of guessing), testing answers through a beginner's eyes, answer structure (result first, next step last). Not for techniques (web-research-camoufox, db-first-search) — this skill is about HOW to speak.
ln-200-scope-decomposer
levnikolaevich
"Orchestrates full decomposition (scope → Epics → Stories → RICE prioritization) by delegating ln-210 → ln-220 → ln-230. Sequential processing. Epic 0 for Infrastructure."
ln-230-story-prioritizer
levnikolaevich
RICE prioritization per Story with market research. Generates consolidated prioritization table in docs/market/[epic-slug]/prioritization.md. L2 worker called after ln-220.
ln-001-standards-researcher
levnikolaevich
Research standards/patterns via MCP Ref. Generates Standards Research for Story Technical Notes subsection. Reusable worker.
novelty-assessment
lingzhi227
Assess research idea novelty through systematic literature search. Multi-round search-evaluate loops with harsh critic persona. Binary novel/not-novel decision with justification. Use before committing to a research direction.
report-writing
vstorm-co
Guidelines for writing well-structured, cited research reports
diagram-design
vstorm-co
Best practices for creating research diagrams with Excalidraw MCP tools
ln-520-test-planner
levnikolaevich
"Orchestrates test planning pipeline (research → manual → auto tests). Coordinates ln-521, ln-522, ln-523. Invoked by ln-500-story-quality-gate."
brainstorming-ideas
alexei-led
Turn ideas into designs through collaborative dialogue. Use when user wants to brainstorm, design features, explore approaches, or think through implementation before coding.
ln-222-story-replanner
levnikolaevich
Replans Stories when Epic requirements change. Compares IDEAL vs existing, categorizes operations (KEEP/UPDATE/OBSOLETE/CREATE), executes in Linear.
ln-220-story-coordinator
levnikolaevich
CREATE/REPLAN Stories for Epic (5-10 Stories). Delegates ln-001-standards-researcher for standards research. Decompose-First Pattern. Auto-discovers team/Epic.
smart-search
AIsa-team
'Intelligent hybrid search combining web and academic sources via AIsa Smart Search endpoint. Best when you need both web and scholarly results. Use when: the user needs web search, research, source discovery, or content extraction.'
notion-research-documentation
ComposioHQ
Research across Notion and synthesize into structured documentation; use when gathering info from multiple Notion sources to produce briefs, comparisons, or reports with citations.
paper-interpreter
chujianyun
论文解读助手。适用于用户发送 arXiv 论文链接，并希望下载论文、解读论文、生成读书笔记、做论文拆解或输出详细报告时使用。会在工作目录创建论文文件夹、下载 PDF 与 TeX Source（如有）、生成中文 Markdown 报告。默认先交付初稿，不自动复查；如果用户明确同意，再安排后续复查。不适用于只要简短推荐语的情况。
aiox-pm
SynkraAI
Product Manager (Morgan). Use for PRD creation (greenfield and brownfield), epic creation and management, product strategy and vision, feature prioritization (MoSCoW, RICE), roa...
paper-writing-section
lingzhi227
Write a specific section of an academic paper (Abstract, Introduction, Background, Related Work, Methods, Experiments, Results, Discussion/Conclusion) with section-specific guidance and two-pass refinement. Use when the user wants to write, draft, or improve a paper section.
paper-revision
lingzhi227
Revise papers based on reviewer feedback. Map reviewer concerns to specific sections, apply targeted edits, run additional experiments if needed, and verify improvements. Use after receiving peer review with revision requests.
survey-generation
lingzhi227
Generate complete academic survey papers using multi-LLM parallel outline generation, RAG-based subsection writing, citation validation, and local coherence enhancement. Based on AutoSurvey pipeline. Use for writing comprehensive literature surveys.