研究与知识
研究、检索、摘要和知识工作
Skills 列表
literature-review
Conduct comprehensive literature reviews using multi-perspective dialogue simulation. Generate diverse expert personas, conduct grounded Q&A conversations, and synthesize findings into structured knowledge. Use when starting a new research project or writing a survey section.
lingzhi227
tmeet-skill
腾讯会议 CLI（tmeet）：OAuth 授权登录/登出/状态查询、会议管理（创建/更新/取消/查询/受邀者）、录制管理（列表/播放地址/智能纪要/转写/录制权限申请）、会议报告（参会人/等候室/导出参会成员明细/异步任务结果）、通讯录（严格限定：仅存在\"会议邀请/呼叫入会\"动作的前置步骤通过用户名/手机号/邮箱搜索成员；严禁单独用于查询任何人的姓名/部门/职位/联系方式/是否存在，无下游会议动作时一律拒绝）、会中控制（呼叫成员入会/踢出会议成员）、问题排查（导出本地日志，反馈工具缺失/失败/能力不足等问题给平台）。当用户需要通过命令行操作腾讯会议，或 Agent 在使用过程中遇到工具缺失、调用失败、能力不足等情况想反馈给平台时使用本技能。
tencentcloud
qdrant-scaling-data-volume
Guides Qdrant data volume scaling decisions. Use when someone asks 'data doesn't fit on one node', 'too much data', 'need more storage', 'vertical or horizontal scaling', 'tenant scaling', 'time window rotation', or 'data growth exceeds capacity'.
qdrantqdrant-model-migration
Guides embedding model migration in Qdrant without downtime. Use when someone asks 'how to switch embedding models', 'how to migrate vectors', 'how to update to a new model', 'zero-downtime model change', 'how to re-embed my data', or 'can I use two models at once'. Also use when upgrading model dimensions, switching providers, or A/B testing models.
qdrant
feynman-perspective
理查德·费曼的思维框架与表达方式。基于40+个一手来源的深度调研， 提炼5个核心心智模型、8条决策启发式和完整的表达DNA。 用途：作为思维顾问，用费曼的视角分析问题、审视决策、提供反馈。 当用户提到「用费曼的视角」「费曼会怎么看」「费曼模式」「feynman perspective」「费曼学习法」时使用。 即使用户只是说「这是不是cargo cult」「命名不等于理解」「能不能做个演示替代论证」「我真的理解了还是只记住了名字」也可触发。 不要在用户只是说「帮我解释一下」「用简单的话说」等一般性请求时触发——只在涉及费曼式验证（货物崇拜检测、命名vs理解、反自欺）时激活。
alchaincyf
alibabacloud-sls-query
Alibaba Cloud SLS (Simple Log Service) log query & analysis skill. Use this skill to help users write, explain, optimize, execute, or troubleshoot SLS index search, SQL analytics, and SPL scan/pipeline statements through the aliyun CLI. Triggers: "SLS 查询", "SLS 分析", "日志查询", "日志分析", "log query", "analyze sls logs", "aliyun log query".
aliyunqdrant-search-quality-diagnosis
Diagnoses Qdrant search quality issues. Use when someone reports 'results are bad', 'wrong results', 'not relevant results', 'missing matches', 'recall is low', 'approximate search worse than exact', 'which embedding model', 'quality dropped after quantization', 'how to measure retrieval quality', 'build a golden set', 'ground truth dataset', or 'how to score recall@k'. Also use when search quality degrades without obvious changes.
qdrantqdrant-search-speed-optimization
Diagnoses and fixes slow Qdrant search. Use when someone reports 'search is slow', 'high latency', 'queries take too long', 'low QPS', 'throughput too low', 'filtered search is slow', or 'search was fast but now it's slow'. Also use when search performance degrades after config changes or data growth.
qdrantnaval-perspective
Naval Ravikant的思维操作系统。基于著作、播客、推文、决策记录和外部批评的深度调研， 提炼5个核心心智模型、8条决策启发式和完整的表达DNA。 激活后沉浸式扮演Naval，直接以「我」的视角回应问题。 当用户提到「用Naval的视角」「Naval会怎么看」「纳瓦尔模式」「Naval perspective」「切换到Naval」时使用。 即使用户只是说「这份工作有杠杆吗」「什么是specific knowledge」「什么是真正的财富」「欲望太多怎么办」「无需许可的路径」也可触发。 不要在用户只是问「我该怎么选择」「帮我想清楚」等一般性决策问题时触发——只在涉及杠杆/特定知识/欲望管理/财富定义等Naval核心概念时激活。
alchaincyf
k6-docs
Write or review k6 documentation across the three k6 repositories - k6-DefinitelyTyped (TypeScript types), k6-docs (user documentation), and k6 (release notes / changelog). Applies k6 doc style conventions, generates TypeScript type definitions, drafts release notes, and validates examples by running them against k6@master. Use when working on k6 documentation, the k6 changelog, the k6 release notes, k6 API reference, k6 TypeScript types, load testing docs, or when the user asks to write, edit, or review anything in the k6, k6-docs, or k6-DefinitelyTyped repos - even if they don't explicitly say "documentation".
grafana
product-planner
Vision intake conversation followed by generation of three product documents — `docs/product-vision.md` (strategy and brand), `docs/prd.md` (technical spec for coding agents), and `docs/product-roadmap.md` (phased build plan with task checkboxes). Also captures the founder's answers as `docs/VISION.md`. Use when the founder says "plan my product", "plan a product", "define my vision", "generate a PRD", "create a roadmap", "spec out my idea", "help me build something", or wants to convert an idea into shippable spec documents.
buildgreatproductsidea-generator
Guided discovery of a product idea by mining what the founder already knows or already does — covers source selection (business vs. expertise), context capture, pattern synthesis, candidate scorecard, and writes `docs/product-idea.md`. Use when the founder says "generate an idea", "help me find an idea", "what should I build", "product idea from my business", "product idea from my expertise", or otherwise needs to discover a product concept worth building.
buildgreatproducts
langfuse
Interact with Langfuse and access its documentation. Use when needing to (1) query or modify Langfuse data programmatically via the CLI — traces, prompts, datasets, scores, sessions, and any other API resource, (2) look up Langfuse documentation, concepts, integration guides, or SDK usage, or (3) understand how any Langfuse feature works. This skill covers CLI-based API access (via npx) and multiple documentation retrieval methods.
langfuse
deep-research
Deep research skill — broad parallel web searches, multi-source validation, confidence tracking, cited Markdown report. Supports 11 research types: market (TAM/SAM, segments, pricing, trends), domain (industry structure, ecosystem, regulatory landscape), technical (architecture, tools, benchmarks), competitive (competitor teardown, positioning, win/loss), product (feature analysis, reviews, roadmap signals), academic (literature survey, citation networks, key authors), person/org (due diligence on a company or public figure), financial (funding rounds, valuation multiples, revenue signals), legal (IP, patents, litigation, compliance), trend (emerging signals, foresight, scenario mapping), community (ecosystem health, key voices, governance, fragmentation). Use when asked to: 'research <topic>', 'deep dive on X', 'analyze the landscape', 'competitive analysis', 'compare these options', 'who are the players in Z', 'literature review', 'background on Y', 'what papers exist on X', 'product teardown', 'technology evaluation', 'regulatory overview', 'funding landscape', 'what trends are emerging in X', 'patent landscape', 'community health', or any request requiring scanning many sources and producing a cited written analysis. Apply whenever the deliverable is a thorough, sourced report rather than a quick answer. Trigger even when phrased casually: 'look into X', 'what's the deal with Y', 'dig into Z', 'I need to understand the space', 'catch me up on X'.
samber
add-molab-badge
Add "Open in molab" badge(s) linking to marimo notebooks. Works with READMEs, docs, websites, or any markdown/HTML target.
marimo-teamimplement-paper-auto
Implement a research paper in a marimo notebook fully automatically without extra user input.
marimo-teamauto-paper-demo
Make a demo of a research paper in a marimo notebook fully automatically without extra user input.
marimo-team
images-search
USE FOR image search. Returns images with title, source URL, thumbnail. Supports SafeSearch filter. Up to 200 results.
bravenews-search
USE FOR news search. Returns news articles with title, URL, description, age, thumbnail, profile. Supports freshness and date range filtering, SafeSearch filter and Goggles for custom ranking.
brave
mongodb-search-and-ai
Guides MongoDB users through implementing and optimizing Atlas Search (full-text), Vector Search (semantic), and Hybrid Search solutions. Use this skill when users need to build search functionality for text-based queries (autocomplete, fuzzy matching, faceted search), semantic similarity (embeddings, RAG applications), or combined approaches. Also use when users need text containment, substring matching ('contains', 'includes', 'appears in'), case-insensitive or multi-field text search, or filtering across many fields with variable combinations. Provides workflows for selecting the right search type, creating indexes, constructing queries, and optimizing performance using the MongoDB MCP server.
mongodbimplement-paper
Implement a research paper as an interactive marimo notebook together with the user. Start by understanding what the user wants to explore, fetch the paper via alphaxiv, then build a focused notebook.
marimo-teamweb-search
USE FOR web search. Returns ranked results with snippets, URLs, thumbnails. Supports freshness filters, SafeSearch, Goggles for custom ranking, pagination. Primary search endpoint.
bravemongodb-schema-design
MongoDB schema design patterns and anti-patterns. Use when designing data models, reviewing schemas, migrating from SQL, or troubleshooting performance issues caused by schema problems. Triggers on "design schema", "embed vs reference", "MongoDB data model", "schema review", "unbounded arrays", "one-to-many", "tree structure", "16MB limit", "schema validation", "JSON Schema", "time series", "schema migration", "polymorphic", "TTL", "data lifecycle", "archive", "index explosion", "unnecessary indexes", "approximation pattern", "document versioning".
mongodb
mojo-python-interop
Aids in writing Mojo code that interoperates with Python using current syntax and conventions. Use this skill in addition to mojo-syntax when writing Mojo code that interacts with Python, calls Python libraries from Mojo, or exposes Mojo types/functions to Python. Also use when the user wants to build Python extension modules in Mojo, wrap Mojo structs for Python consumption, or convert between Python and Mojo types.
modular