Research & Search

dinglebear-ai Bundle

Stage, commit, and push the current repository changes with an optional version bump and changelog update. Use when the user says "quick push", "push my changes", "commit and push", "ship this", "push to a new branch", or asks to publish the current worktree. Session logging is handled separately by wrap-session so quick-push never mutates the personal knowledge base. Accepts optional `--no-bump` argument to skip the version bump.

dinglebear-ai Bundle

Route session closeout to the correct domain logger based on observed work. Use when the user says "wrap session", "wrap up this session", "log what we did", "close out", or invokes /wrap-session. Classify the full session as coding, homelab maintenance, or both, invoke log-code-session and/or log-homelab-maintenance, cross-link paired artifacts, refresh knowledge indexes, and validate the knowledge base. This skill never commits, pushes, merges, or performs cleanup.

dinglebear-ai Bundle

Record one or more durable architecture or operational decisions as ADRs in the personal knowledge base. Use when the user says "log this decision", "create an ADR", "record why we chose this", "capture these decisions", or when a session produced choices that future agents would otherwise relitigate. Read the ADR contract and related evidence, create the next numbered ADR files, and link superseded decisions. This skill writes decision records only and never implements, commits, or publishes them.

dinglebear-ai Bundle

This skill should be used when the user mentions NotebookLM, says /notebooklm, or asks to create a podcast, generate an audio overview, make a quiz, summarize URLs, add sources to NotebookLM, generate flashcards, create a mind map, make an infographic, or download generated content. Covers full programmatic access to Google NotebookLM including features unavailable in the web UI.

zereight Bundle

Paper reader and idea extraction workflow. Use for research papers, arXiv links, PDFs, 논문 분석, 논문 읽기, paper review.

zereight Skill

Travel research workflow. Use for city research, museums, architecture, itinerary context, 여행 리서치, 도시 공부.

zereight Bundle

Paper lineage and related work tracing workflow. Use for citation trail, prior work, follow-up research, 논문 계보, 관련 연구.

zereight Bundle

Summarize PRs merged in a GitHub repo over the last N hours (default 12), excluding ones you authored or have already touched, and rank what's worth reviewing. Use when user says "morning recap", "what did I miss", "what shipped overnight", or "what went on while I was sleeping".

rongxinzy Bundle

提供高质量的研究写作支持：从资料调研、补充引用，到优化开头、完善结构大纲，并实时提供逐节反馈与打磨建议。当用户提出写作协助请求，如“帮忙调研资料”、“优化开头”、“梳理大纲”，或需要逐节审阅、补充引用、润色文章时触发。

rongxinzy Bundle

协助科研人员进行选题构思、项目规划、问题排查与科研决策，输出具体的研究方案、风险评估矩阵或决策树等文档。当用户提出新研究想法、分享当前项目遇到的卡点、咨询战略性问题，或使用“选题”、“开题”、“项目构思”、“研究方向”、“研究路径”、“风险评估”、“问题排查”、“卡住了”、“下一步怎么走”等关键词时触发。

rongxinzy Bundle

A protocol framework for long-horizon autonomous research tasks. Targets three empirically-observed failure modes — cognitive loops, stalling, runtime fragility — by prescribing state management, stall detection, and watchdog mechanisms. Use when the user asks for academic research, literature surveys, paper writing, or any unattended multi-day research task. Triggers: academic research, 学术研究, literature review, 文献综述, paper writing, 论文写作, ICLR survey, autonomous research.

rongxinzy Bundle

学术论文英文润色助手，按学术写作标准逐段审查语法、用词、语态与逻辑衔接，输出修改建议与润色后的文本。当用户请求论文润色、语法检查、或提交英文论文片段寻求改进，例如说“帮我润色这段英文”、“这段论文语法有没有问题”、“改成学术英语”，或提及paper polishing、academic writing、manuscript editing、SCI润色等关键词时触发。

rongxinzy Bundle

Assists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on each section. Transforms your writing process from solo effort to collaborative partnership.

fudesign2008 Bundle

Shared analysis-stage methodology for PDCA fix workflows: temporary-change permission and rollback gate, red-capable reproduction loop before hypothesis lists (user-pasted evidence excluded), instrumentation-debug with runtime-evidence-debug as default entry, analysis step skeleton (existence → research routing → phenomenon/locate/root-cause/upstream-eval/impact), mandatory analysis gate output block, and debug-verify loop. Parameterizes the post-analysis exit as {next-stage}. Referenced by PDCA hosts via frontmatter dependencies. Triggers — 「分析阶段核心」「分析核心」「临时改动门控」「红环门控」「打点调试门控」「调试验证闭环」「分析门控」 / analysis stage core, red-capable loop gate, temp-change gate, instrumentation debug gate, analysis gate output block, debug-verify loop.

fudesign2008 Bundle

External-research routing for confirmed code problems: triage whether the root cause is internal / external / hybrid, run a known-issue quick search before deep root-causing (platform silent failures, nested host runtimes, no code-level suspects), and evaluate industry-wide hard limits. Delegates all WebSearch discipline to effective-web-research. Referenced by PDCA hosts via frontmatter dependencies; load during the technical-analysis stage.

jaygptpro Skill

Build a complete Amazon keyword set for a product and sort it into a usable map. generates seed keywords, expands by intent, classifies by funnel stage and relevance, and assigns each keyword a placement (title, bullets, backend, PPC). Use when a user asks for keyword research, keyword ideas, what keywords to target, long-tail keywords, search terms to rank for, or how to map keywords to a listing. Trigger phrases: "keyword research", "keyword ideas", "what keywords", "long tail keywords", "keywords for my listing", "keyword map". Works with zero tools. the user describes the product and audience.

jaygptpro Skill

Research and validate an Amazon product opportunity end to end, and evaluate whether the niche around it is winnable. Assesses demand, competition, profit potential, entry barriers, review wall, differentiation room, and seasonality, and returns a go/no-go with the reasoning. Use when a user asks to research a product, find a product to sell, validate a product idea, assess an opportunity, evaluate a niche, find a profitable niche, judge whether a category is worth entering, or compare niches. Trigger phrases: "product research", "find a product to sell", "validate this product", "is this a good product", "product opportunity", "should I sell this", "niche finder", "evaluate this niche", "is this niche worth it", "good niche", "low competition niche", "should I enter". Works with zero tools. the user describes the product and what they can observe.

orkas-ai Bundle

Brand Research

orkas-ai Bundle

Material Organizer

orkas-ai Bundle

deep-research

77cinc Skill

先调研后廷议：自动执行"客观调研 → 主观建议"工作流。 触发：当用户说"调研后再讨论"、"先查资料再开会"、"基于最新数据讨论"时自动触发。 流程： 1. 调用 investigation-first skill 进行多源调研 2. 整理成客观报告 3. 调用 council skill，谋士基于报告讨论 输出：调研报告 + 票拟（谋士建议）

fdiblen Bundle

Covers responding when concealment or misrepresentation is requested: hiding AI usage, making work appear different from reality, backdating or disguising provenance, inflating results or removing traces of how something was made. The skill calls for honesty with concrete reasons and offers honest alternatives that usually satisfy the underlying need. Use PROACTIVELY whenever a request aims to make records, history, authorship or results tell a story different from what happened - including hiding AI assistance, "make it look like", disguising generated content as manual work, or presenting untested claims as verified. Disclosure mechanics live in rseng-ai-declaration; the verify-before-trust duty in rseng-human-verification; checking others' outputs in rseng-research-integrity.

fdiblen Bundle

Covers teaching research software skills while working: turning everyday coding moments into short, learner-centered lessons using Carpentries/CodeRefinery-style pedagogy (objective-led episodes, formative checks, error normalization), and routing learners to canonical training materials. Use when a teachable moment appears during a task (offer a one-line lesson, never lecture), when the user asks to learn a topic, requests an explanation or tutorial, wants training material recommendations, is preparing to teach others, or when a developed project should ship a build story (docs/BUILD-STORY.md) explaining its design, technology and process choices for new developers. For onboarding cohorts see rseng-contributor-onboarding.

fdiblen Bundle

Covers writing readable research code and structuring software projects: naming, formatting, style guides, linters and formatters, pre-commit hooks, modular design, and a conventional directory layout with top-level metadata files. Use when the user asks how to make code readable or clean, pick or enforce a style guide, set up linting/formatting or pre-commit, name variables and functions, organise a repo, or decide where files and data go. For generating a new project from a maintained template see rseng-project-scaffolding; for quantitative complexity and duplication measurement see rseng-software-metrics; for architecture-level structure see rseng-software-design.

fdiblen Bundle

Covers how to apply the FAIR principles - findable, accessible, interoperable, reusable - to research software, and how to assess a project's FAIRness. Use when the user asks how to make software FAIR, wants help with findability, discoverability, or software reuse, mentions metadata, persistent identifiers, DOIs, registries, or software citation in a FAIR context, or asks to run a FAIR self-assessment or checklist on a repository. (Automated FAIR4RS scoring, compliance levels and CI gates with the FAIRGuard tool are rseng-fairguard; FAIR for ML models and datasets is rseng-fair-ml; finding existing software to reuse is rseng-software-reuse.)

fdiblen Bundle

Covers the environmental footprint of research computing: measuring and reporting energy use and carbon emissions of computations (CodeCarbon), reducing them through efficient code, right-sized hardware and carbon-aware scheduling (CATS), the GREENER principles and the Software Carbon Intensity metric. Use when the user asks about the carbon or energy cost of their computations, wants to make workloads greener, mentions sustainability of computing, CodeCarbon, CATS or the Software Carbon Intensity metric. Use PROACTIVELY when planning large training runs, simulations or parameter sweeps - footprint measurement is worthless retrospectively. (Keeping the software project itself alive is rseng-maintenance-sustainability; making code faster is rseng-performance-profiling.)

fdiblen Bundle

Covers capturing and reusing what a project learns: a lessons-learned record fed from debugging sessions, code review findings, failed and successful research approaches, incidents and near-misses; blameless postmortems for the big ones; retrospectives on a cadence; and routing each lesson into the artifact that prevents its repetition (test, doc, checklist, onboarding note). Use PROACTIVELY when a nontrivial bug is fixed, a review uncovers a recurring pattern, an approach is abandoned, or an incident is resolved - and when the user asks to record a lesson, run a retrospective or postmortem, wants a LESSONS or NOTES file, or asks why the same mistake keeps happening.

fdiblen Bundle

Covers defenses against silently wrong research results: validating data at boundaries (schemas, assertions, sanity checks), explicit physical units and quantities in code (pint/astropy-style), disciplined randomness (explicit seeded generators, parallel streams), and fail-loud handling of NaN and missing data. Use PROACTIVELY when code ingests external or instrument data, when values carry physical units, when randomness enters simulations or sampling, or when NaN or missing-data handling is implicit; also when the user mentions data validation, unit errors, seeds or silent bugs, or reviews analysis code whose failure would be invisible. For floating-point behavior and tolerances see rseng-numerical-accuracy; for diagnosing an existing bug see rseng-debugging.

fdiblen Bundle

Covers making research software citable and contributors credited: writing CITATION.cff, describing software with CodeMeta (codemeta.json), minting DOIs and ORCIDs, and tracking contributors of every kind. Use when the user asks how to make software citable, add CITATION.cff or codemeta.json, obtain a DOI, ensure contributors get credit, or mentions CFF, CodeMeta, ORCID, CRediT or persistent identifiers. Use PROACTIVELY when generated code draws on a publication, website or existing code (credit it at the code site and in the references), at release preparation, and when citation files are edited. (Verifying references you cite: rseng-citation-hygiene; versioning schemes and the DOI-minting release: rseng-publishing-releasing.)

fdiblen Bundle

Covers the human's side of AI-assisted research software: strongly urging the user to review generated code and verify results before relying on them, teaching how to review AI-written code effectively (where to look first, what to run, what to spot-check against known answers), and recording review status honestly. Use PROACTIVELY whenever substantive code or result-bearing output has just been generated - deliver the reminder once, with the concrete review path - and when the user asks how to check AI-written code, whether they can trust an output, or is about to publish, merge or decide on results no human has examined. Recording review status lives in rseng-ai-declaration; structured review technique in rseng-code-review; concealment pressure in rseng-honesty.

fdiblen Bundle

Covers floating-point correctness in research code: why 0.1 + 0.2 != 0.3, choosing absolute vs relative tolerances in tests, accumulation error and safe summation, precision choices (float32 vs float64), catastrophic cancellation, NaN and infinity handling, and cross-platform or cross-library result drift. Use PROACTIVELY when floating-point comparisons fail mysteriously, when writing numerical tests or choosing tolerances, when results differ across machines, compilers, BLAS builds or library versions, or when precision or numerical stability questions arise in analysis or simulation code.

fdiblen Bundle

Covers planning research software work: writing and maintaining a Software Management Plan (SMP), and choosing programming languages, tools, and infrastructures for a project. Use when the user wants to write or review an SMP, plan how software will be developed, maintained, shared, and preserved, needs the software sections of a proposal or funder template, wants to decide which language or framework to start a project in (Python, C++, R, Julia, Rust, Fortran, JavaScript), pick a project template or boilerplate, or weigh reuse, sustainability, and funder requirements at the start of a project. (Data management plans, maDMPs, DS-Wizard and DMPonline are rseng-data-management-plans; interactive new-project setup is rseng-project-kickoff; week-to-week task tracking is rseng-project-tracking.)

fdiblen Bundle

Covers starting research software projects from maintained templates and keeping them in sync: choosing a generator (Copier, cookiecutter), scaffolding a Python package, retrofitting template structure, pulling template upgrades into generated projects, and picking a pyproject build backend. Use when the user starts a new research software codebase, asks for a template or boilerplate, wants a src/ layout or pyproject.toml scaffold, mentions copier or cookiecutter, or chooses between setuptools, hatchling, poetry or PDM. For hand-rolled layout see rseng-code-quality; for the management side see rseng-project-kickoff.

fdiblen Bundle

Covers publishing research software through its distribution channels: packaging for and releasing on package indexes (PyPI, conda-forge, CRAN and ecosystem equivalents), registering in research software registries, submitting to software journals (JOSS-style), and choosing the channel mix for a project's audience. Use when the user wants their software installable by others, asks how to publish on PyPI, CRAN or conda-forge, wants the software listed where researchers search, or when a mature project is only obtainable by cloning its repository. (Cutting versioned releases and DOIs: rseng-publishing-releasing; the JOSS review process: rseng-software-peer-review; preservation: rseng-archiving.)

fdiblen Bundle

Covers building and governing a community around research software: CONTRIBUTING guides, codes of conduct, governance models and decision-making, contributor recognition policy, issue and discussion hygiene, and handing over or sharing maintainership. Use when a project wants external contributors, when the user asks for a CONTRIBUTING.md, code of conduct or governance document, when maintainer burnout or bus-factor risks come up, or when a project is moving from single-author to team or community ownership. The contributor funnel and good-first-issue curation live in rseng-contributor-onboarding; health measurement in rseng-community-metrics; support operations in rseng-user-support.

fdiblen Bundle

Covers the release lifecycle of research software: preparing and cutting versioned releases with changelogs, versioning schemes, release automation and minting a DOI per release. Use when the user asks how to tag a v1.0.0 release, write release notes or a changelog, pick a versioning scheme (SemVer or CalVer), automate releases, or mint a release DOI. (Channel craft for PyPI/CRAN/conda and registries is rseng-software-publishing; long-term preservation is rseng-archiving.)