「research」の検索結果

Researches state-specific charitable solicitation filing requirements keyed to gross revenue, producing a citation-backed state-by-state compliance matrix of audit thresholds, financial-statement tiers, filing fees, and due dates. Use when building charity compliance matrices, researching nonprofit filing thresholds, charitable solicitation registration renewals, comparing state audit requirements for 501(c)(3) organizations, or when the user mentions audit threshold, review threshold, compilation requirement, charity filing deadline, or multi-state nonprofit compliance.

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。

AI-powered SEO operations: keyword intelligence, competitor gap analysis, Google Search Console optimization, and trend detection. Use when asked to research keywords, analyze competitor content gaps, audit GSC performance, or detect trending topics. For growth experiments see growth-engine; for content optimization see autoresearch.

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。

Scientific research engine with adversarial review, tree search, and serendipity detection. Use when: exploring hypotheses, validating findings against literature, running computational experiments with quality gates, or hunting for unexpected discoveries. Do NOT use for simple Q&A, code editing, or non-research tasks.

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。

Scientific research engine for hypothesis testing, literature gap analysis, experimental validation, and data-driven discovery. Enforces adversarial review (Reviewer 2), 32 quality gates, tree search over hypotheses, confounder harness for quantitative claims, and serendipity detection. TRIGGER when: user asks to analyze scientific data, test hypotheses, validate findings, search for research gaps, design experiments, or investigate results. DO NOT TRIGGER when: pure code review, documentation writing, devops tasks, general conversation, or non-scientific data transformation.

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。

Automate SEO research with Ahrefs -- analyze backlink profiles, research keywords, track domain metrics history, audit organic rankings, and perform batch URL analysis through the Composio Ahrefs integration.

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。

Web search and research using Perplexity AI. Use when user says "search", "find", "look up", "ask", "research", or "what's the latest" for generic queries. NOT for library/framework docs (use Context7) or workspace questions.

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。

Collaborative writing partner that assists with research, outlining, drafting, citations, and iterative feedback. Helps writers create high-quality content while maintaining their unique voice and style.

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。

Handle ethical requirements, IRB approval, trial registration, and pre-analysis plans for RCTs. Use when user mentions: research ethics, IRB, informed consent, trial registration, pre-analysis plan, PAP, data security, vulnerable populations, research transparency.

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。

Systematic literature review methodology for social science research. Use when conducting systematic reviews, research synthesis, meta-analyses, scoping reviews, or comprehensive literature searches across economics, political science, sociology, education, public health, and development. Produces structured notebook cells with search strategy, evidence table, thematic synthesis, and gap analysis.

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。

Scope, preregister, execute, audit, normalize, and publish GEODE evaluations with research-question, reproduction, attempt-lineage, trajectory, verifier-receipt, and immutable artifact contracts. Use for benchmark runs or comparisons, eval-artifact and cache-accounting audits, GPT or subscription result normalization, research question or hypothesis framing, retry diagnosis, trajectory publication, DPO or reward-data readiness reviews, and requests involving MCPMark, tau2, BFCL, Agent-World, HAL, Terminal-Bench, or Toolathlon.

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。

Evidence-first multi-step research with bounded parallel collection, contradiction checks, and cited 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。

Trigger when: (1) the user mentions "manim", "scene", "video", "EN/KO render", "GEODE_HERO_LANG", "1080p60", or (2) editing files under `scripts/visualizations/` ending in `.py`, or (3) the user asks to render, re-render, or extend any of the four validated scenes (`geode_hero.py`, `autoresearch_filewalk.py`, `autoresearch_compare.py`, `critical_floor.py`). GEODE Manim Scene authoring standard. Locks the patterns the four validated scenes already share — Anthropic-style palette, Helvetica Neue + Pretendard font pairing with weight=NORMAL, EN/KO multilingual lang via `GEODE_HERO_LANG` env, `_dashed_arrow_with_head` stage-coupled tinting, `verify_hero_layout.py` ratchet — so the next scene does not re-discover the kerning / padding / transition regressions catalogued in [[viz-frame-audit]].

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。

Reference when designing autonomous agent systems or deciding agent freedom/safety/context management/collaboration infrastructure. 10 design principles distilled from Karpathy autoresearch (autonomous ML experiment loop) + AgentHub (agent-native Git DAG). Triggered by "autoresearch", "agenthub", "ratchet" ("래칫"), "context budget", "dumb platform", "program.md", "overnight", "autonomous experiment" ("자율 실험"), "branchless", "single-file constraint" ("단일 파일 제약"), "fixed time budget" ("고정 시간 예산") keywords.

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。

Long-running design anthropologist that builds comprehensive visual databases from 500-1000 real-world examples, extracting color palettes, typography patterns, layout systems, and interaction design across any domain (portfolios, e-commerce, SaaS, adult content, technical showcases). This skill should be used when users need exhaustive design research, pattern recognition across large example sets, or systematic visual analysis for competitive positioning.

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。

Deep multi-source technical research with citations — tech evaluation, ecosystem/standards comparison. NOT for API docs (mk:docs-finder), codebase discovery (mk:scout), or project Q&A (mk:ask-me).

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。

Fetch external sources (web / arXiv / GitHub) and turn them into scanner-gated wiki CANDIDATES — never canonical pages. Every fetched byte is DATA; url-guarded, size-capped, redirect-re-validated, injection-scanned, and secret-scrubbed before a candidate is even created. Requires network. NOT for capturing local knowledge (see mk:wiki); NOT for one-shot page-to-markdown (see mk:web-to-markdown).

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。

Deep spec analysis on a Confluence page. Reads page (+ children) via the confluence-as wrapper, produces a structured Spec Research Report with requirements, acceptance criteria, gaps, ambiguities, and suggested user stories for human review. Read-only — does NOT create Jira tickets. Triggers: 'analyze spec for PAGE-ID', 'extract requirements from confluence page', 'spec research on URL'. NOT for raw page CRUD (mk:confluence-page); NOT for ticket complexity scoring (mk:planning-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。

Research-validated planning — research → plan → verify cycle for high-confidence implementation plans

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。

Finish an authorized security research task with a durable solved, abstained, or blocked outcome. Use when evidence is sufficient, a stop condition is reached, a tool budget is nearly exhausted, or the agent must commit its best supported answer.

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。

Plan vulnerability research using conventional bug-hunting and pentest stage names, specialty playbooks, and a research-hypothesis-experiment loop. Use when designing a bug bounty workflow, selecting tools and strategies, building a decision tree, or turning a target/scope into a staged hunting plan.

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。

Autonomous experiment loop that optimizes any file by a measurable metric. Inspired by Karpathy's autoresearch. The agent edits a target file, runs a fixed evaluation, keeps improvements (git commit), discards failures (git reset), and loops indefinitely. Use when: user wants to optimize code speed, reduce bundle/image size, improve test pass rate, optimize prompts, improve content quality (headlines, copy, CTR), or run any measurable improvement loop. Requires: a target file, an evaluation command that outputs a metric, and a git repo.

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。

App Store Optimization (ASO) toolkit for researching keywords, analyzing competitor rankings, generating metadata suggestions, and improving app visibility on Apple App Store and Google Play Store. Use when the user asks about ASO, app store rankings, app metadata, app titles and descriptions, app store listings, app visibility, or mobile app marketing on iOS or Android. Supports keyword research and scoring, competitor keyword analysis, metadata optimization, A/B test planning, launch checklists, and tracking ranking changes.

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。

Automate SEO research with Ahrefs -- analyze backlink profiles, research keywords, track domain metrics history, audit organic rankings, and perform batch URL analysis through the Composio Ahrefs integration.

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 this skill to drive a research question (RQ) end-to-end: validate the RQ README, generate a fill batch-plan, start the Docker batch in the background, monitor progress, run aggregation, and propose findings updates. Trigger when the user says "RQ-N voranbringen", "run-rq", "fill RQ-N", "run RQ-N", "Forschungsfrage N starten", or names a specific RQ-N directory in research/.

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。