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概要と使いどころ
Reconcile new evidence into existing ChatGPT Pages or a Space when the user requests upkeep. For a direct text correction, use write-page. Do not use for ordinary chat drafts, writing, or advice, or treat a selected Page as a request to save. Schedule future upkeep only when requested.
日本語の概要は準備中です。原文の説明を表示しています。
Use when authoring a non-trivial Workflow for research, review, migration, or other multi-agent work, especially when the task needs multiple evidence sources, verification, or synthesis.
日本語の概要は準備中です。原文の説明を表示しています。
Diagnose Muse Code product/runtime issues from installed binary evidence. Use ONLY when the user explicitly invokes the doctor skill, asks to debug/troubleshoot Muse Code itself, asks what happened earlier in the current Muse Code session, or explicitly selects an earlier Muse Code session. Do NOT use for ordinary repository code failures or history, benchmark tasks, implementation debugging, build/test hangs, or third-party project issues.
日本語の概要は準備中です。原文の説明を表示しています。
Check deployment evidence and recovery conditions.
日本語の概要は準備中です。原文の説明を表示しています。
Run the local test evidence check
日本語の概要は準備中です。原文の説明を表示しています。
Review database migration files when a change adds or modifies paths under migrations/. Use it before merge to collect forward, rollback, locking, and data-safety evidence.
日本語の概要は準備中です。原文の説明を表示しています。
Focused interactive tutor for the Agent Skills Engineering path in AI Engineering from Scratch. Start or resume this route when a learner wants to create, discover, invoke, secure, evaluate, package, or port Agent Skills. Teaches one lesson per invocation and records evidence in AGENT-SKILLS-LEARNING.md.
日本語の概要は準備中です。原文の説明を表示しています。
Focused interactive tutor for the Model Context Protocol (MCP) path in AI Engineering from Scratch. Start or resume this route when a learner wants to build, secure, debug, verify, or operate MCP clients, servers, transports, gateways, registries, or conformance gates. Teaches one lesson per invocation and records wire evidence in MCP-LEARNING.md.
日本語の概要は準備中です。原文の説明を表示しています。
When the user wants to write, rewrite, or improve marketing copy for any page, including homepage, landing pages, pricing pages, feature pages, about pages, or product pages. Also use when the user says "write copy for," "improve this copy," "rewrite this page," "marketing copy," "headline help," "CTA copy," "value proposition," "tagline," "hero section copy," "this copy is weak," "this sounds like AI," "AI slop," "make it sound human," or "help me describe my product." Use this whenever someone is working on website text that needs to persuade or convert. Drafts never use AI tells like "it's not X, it's Y" reveals, "no X, no Y, no Z" lists, or sentences that trail into extra comma clauses. For email copy, see emails. For popup copy, see popups. For editing existing copy, see copy-editing. For the offer underneath the copy (bonuses, guarantees, value framing), see offers. Claims must be supportable, and a declined claim names the evidence that would unlock it.
日本語の概要は準備中です。原文の説明を表示しています。
Review a finished epic folder in the ticket tree against the evidence it left behind — the epic file, each ticket plan, diffs, commits — and produce a retrospective with sourced findings, action items, and an acceptance decision. Use when the user says "run a retrospective" or "lets retro the epic [epic]". Supports -H/--headless
日本語の概要は準備中です。原文の説明を表示しています。
Universal deep research agent team. 13-agent pipeline for rigorous academic research on any topic. 8 modes: full research, quick brief, paper review, lit-review, fact-check, three-way literature scan, Socratic guided research dialogue, and systematic review with optional meta-analysis. Covers research question formulation, Socratic mentoring, methodology design, systematic literature search, source verification, cross-source synthesis, risk of bias assessment, meta-analysis, APA 7.0 report compilation, editorial review, devil's advocate challenges, ethics review, and post-research literature monitoring. Triggers on: research, deep research, literature review, systematic review, meta-analysis, PRISMA, evidence synthesis, fact-check, WHY HOW WHAT papers, 3W literature scan, guide my research, help me think through, 研究, 深度研究, 文獻回顧, 文獻探討, 系統性回顧, 後設分析, 事實查核, 三段式文獻掃描, 引導我的研究, 幫我釐清, 幫我想想, 我不確定要研究什麼, 研究方向, 研究主題, 심층 연구, 문헌 조사, 체계적 문헌고찰, 메타분석, 사실 확인, 연구 방향을 잡아줘, 연구 주제 정하는 것을 도와줘, revisión de literatura, metaanálisis
日本語の概要は準備中です。原文の説明を表示しています。
Supports structured what-if scenario analysis for research planning, experimental contingencies, and scientific project decisions. Explores favorable, reference, adverse, wild-card, contrarian, and second-order scenarios with explicit assumptions, evidence, and decision triggers. Use to stress-test a research plan under uncertainty; scenario narratives do not estimate causal effects or calibrated forecast probabilities.
日本語の概要は準備中です。原文の説明を表示しています。
Facilitates evidence-aware scientific ideation with independent generation, structured discussion, explicit assumptions, transparent evaluation, adversarial review, and decision logs. Use for early-stage research brainstorming or prioritizing candidate directions; hand off empirical validation, study design, ethics or regulatory review, and clinical questions to appropriate experts or skills.
日本語の概要は準備中です。原文の説明を表示しています。
Drafts, revises, and audits scientific manuscripts or reports with explicit evidence provenance, reporting-guideline coverage, authorship accountability, confidentiality controls, and local consistency checks. Use for manuscript sections, references, declarations, tables, figures, or submission preparation when scientific accuracy and traceability matter.
日本語の概要は準備中です。原文の説明を表示しています。
Uses Parallel CLI for web search, URL extraction, deep research, structured data enrichment, entity discovery, and recurring web monitoring. Best for requests that explicitly need current web evidence, academic-source discovery, repeated entity lookups, exhaustive reports, or ongoing change tracking.
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Provides qualitative-first, evidence-traceable developmental review of scholarly works and audit low-stakes research-assessment rubrics with optional local quality controls. Never use for ranking people or consequential decisions.
日本語の概要は準備中です。原文の説明を表示しています。
Conducts systematic, scoping, and narrative literature reviews using PubMed, arXiv, bioRxiv, Semantic Scholar, and other appropriate sources. Use for research synthesis, reproducible literature searches, screening, citation checking, or preparing Markdown and PDF reviews. Tracks search coverage, records versus studies, and evidence limitations; supports meta-analysis planning but does not supply a meta-analysis engine.
日本語の概要は準備中です。原文の説明を表示しています。
Converts neuroscience acquisition data to Neurodata Without Borders files with NeuroConv and PyNWB, preserves metadata and timebases, checks evidence-based clock alignment, and produces schema validation, NWB Inspector findings and round-trip checks. Use for NWB conversion and synchronization of planar single-channel two-photon TIFF imaging plus timestamped behavioral position CSV; this skill does not perform spike sorting or claim tested support for arbitrary acquisition formats.
日本語の概要は準備中です。原文の説明を表示しています。
Formulates evidence-bounded scientific questions, candidate hypotheses, rival explanations, causal or associational claims, discriminating predictions, measurements, and preregistration-ready analysis plans. Used when turning observations or preliminary findings into transparent, testable research plans without treating hypotheses as facts.
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Searches and reads biomedical papers, FDA/PMDA/EMA documents, clinical trials, and protein records with the GXL Paperclip CLI and Python SDK. Supports source-scoped search, full-text grep, metadata SQL, map/reduce extraction, figure analysis, optional repositories and claim verification, and line-pinned citations. Use when a task names GXL paperclip, asks to install or authenticate it, or requests literature retrieval and evidence extraction through Paperclip.
日本語の概要は準備中です。原文の説明を表示しています。
Analyzes and engineers protein glycosylation by scanning canonical N-glycosylation sequons, describing S/T-rich regions, checking curated glycan evidence, and preparing NetNGlyc, NetOGlyc and GlycoSHIELD workflows. Use for glycoprotein engineering, antibody Fc glycosylation, glycan shielding, and site-specific glycoproteomics interpretation.
日本語の概要は準備中です。原文の説明を表示しています。
Prepares and validates research-only clinical decision-support evaluation, evidence-profile, cohort, survival, biomarker/model, privacy, and governance artifacts. Supports aggregate or synthetic research documentation and traceability, excluding patient care and live clinical operation.
日本語の概要は準備中です。原文の説明を表示しています。
Applies the DHDNA framework as an exploratory rubric for reasoning and writing patterns in supplied text. Used for explicit requests for DHDNA, cognitive-style reflection, a thinking-pattern profile, or comparisons of textual reasoning. Scores describe evidence in the sample, not validated psychological traits or personal identity.
日本語の概要は準備中です。原文の説明を表示しています。
Applies Arbor Hypothesis Tree Refinement to research artifacts with repeatable evaluators, including model training, agent harnesses, data synthesis and benchmark optimization. Uses persistent hypotheses, isolated experiments, evidence propagation and held-out candidate comparison for multi-experiment research runs. Includes a standard-library state manager and guidance for the RUC-NLPIR Arbor CLI.
日本語の概要は準備中です。原文の説明を表示しています。