863 件 ・ 関連度順
概要と使いどころ
Generates Chinese/Japanese speech with StepFun's Contextual TTS — default stepaudio-2.5-tts, stepaudio-3-tts for whisper/inline-() prosody. Replaces step-tts-2's voice_label with natural-language instruction. Use for emotional/prosody-controlled synthesis, batch voice lines, migrating from step-tts-2, or cloned voices (2.5/step-tts-2, not v3). Triggers on 阶跃 TTS, 语音合成, 配音. Not for transcription (use stepfun-asr).
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。
Build a tiered classroom behavior intervention plan (BIP) for a K-12 student, grounded in the function of the behavior. Use when asked to plan a behavior intervention, address a disruptive or off-task pattern, write a BIP, or set up positive behavior supports. Produces a function hypothesis, prevention/antecedent strategies, teaching of a replacement behavior, a response plan for when it happens, and a simple data-tracking method — positive and skill-building, not punitive.
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。
Design statistically rigorous A/B tests for product features, UI changes, onboarding flows, and pricing experiments. Use when asked to set up an experiment, design an A/B test, calculate sample size, or interpret test results. Produces a complete test plan with hypothesis, variant definitions, sample size, duration estimate, guardrail metrics, and a results interpretation guide.
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。
Help users convert massive volumes of qualitative and quantitative feedback into high-confidence product decisions through rigorous synthesis and internal immersion.
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 the user asks about content in their OpenKB knowledge base — research topics, concepts compiled from their documents, cross-document synthesis — or mentions `openkb`, an `.openkb/` directory, or a `wiki/` tree generated by openkb. The user may invoke you from any working directory; the active KB resolves via `openkb status`. Do NOT use for arbitrary Markdown directories, Obsidian vaults, or documentation sites not built by openkb.
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。
Scholarly research companion using Knows sidecar spec (.knows.yaml). Generates, validates, reviews, queries, and compares structured research-paper sidecars, and fetches them from knows.academy. Use for academic literature search, survey synthesis, paper authoring assistance, and peer review with token-efficient claim/evidence/relation access.
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。
Architecture specialist for software/system design, module and service boundaries, tradeoff analysis, and stakeholder synthesis. Uses context-aware methods such as diagnostic routing, design-twice comparison, ATAM-style risk analysis, CBAM-style prioritization, and ADR-style decision records.
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。
Search academic literature and generate, validate, or compare Knows paper sidecars. Use for claim/evidence analysis and literature 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。
Produce an experiment blueprint from a research hypothesis
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 fast, progressive experiments to map tunable parameters and their effects. Use when optimizing code, prompts, configurations, or other artifacts against a goal or benchmark, or when a long research run needs a planned hypothesis queue, focused trials, and durable findings.
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 only after explicit Codex `$superloopy:superloopy-research` or Claude Code `/superloopy:superloopy-research` invocation, a research task started with a leading `loopy` or `루피` (such as `loopy research`), or an active Superloopy loop explicitly routing a research deliverable here. Evidence-backed Superloopy research orchestration with automatic advisory usage targets, parallel read-only lanes, per-retrieval verdicts, graded and dated sources, empirical verification, a priced claim ledger, cited synthesis, and optional reports. Do not activate from research, investigate, look-up, summarize, deep-dive, report, or similar vocabulary alone, in any language; ordinary questions, debugging, and implementation context-gathering stay with their primary workflows.
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。
Rules as hypotheses: falsifiable tests, confidence updates, graduate or kill. Triggers: experiment, hypothesis, prove, test rule, validate methodology, 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。
Generate meaning-carrying editorial data-illustrations in the monotykamary / Linear aesthetic (near-black grayscale, Inter display + mono labels, hairline framed figures) with a single coral accent. This is a GENERATIVE GUIDE, not a template gallery: it teaches the "claim -> geometry" method so any session can invent the RIGHT figure for what a post actually argues, then render it as a self-contained, theme-aware, reduced-motion-safe HTML/SVG figure. Use when writing/illustrating a blog post, essay, spec, or slide and you want a chart, diagram, or illustration that carries a thesis (not decoration) in the house style. Triggers: "make a chart/diagram/illustration for this", "visualize this argument", "monotykamary/Linear style figure", "editorial graphic".
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 targeting Trends in Food Science & Technology or deciding whether a food-science review manuscript fits this venue. Encodes the journal's review-only fit, the critical-synthesis and comprehensiveness bar, structure expectations, official-submission re-check, and desk-reject heuristics.
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 targeting the Bulletin of the American Meteorological Society (BAMS) or deciding whether a broad-interest atmospheric/oceanic/hydrologic-sciences contribution fits this venue. Encodes the journal's review-and-community positioning, the broad-audience-significance bar, synthesis and field-campaign expectations, AMS house style, official-submission re-check, and misfit heuristics.
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 targeting Annual Review of Plant Biology or deciding whether a plant-biology review proposal fits this invited-review venue. Encodes the journal's fit, the authoritative-synthesis and invited-article bar, evidence and balance expectations, house style, official-submission re-check, and desk-reject heuristics.
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 writing the short proposal that gates an Academy of Management Annals (Annals) review — the ≤5-page, four-heading proposal submitted under the twice-yearly deadlines before any full review is invited. Builds the proposal; it does not gather the literature (amann-literature-synthesis) or run the portal preflight (amann-submission).
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 judging whether a management/organization literature is an Academy of Management Annals (Annals)-scale review topic — mature, important, fragmented, and integrable into a new framework — rather than a narrow review. Decides fit and frames the review's animating problem; it does not gather the literature (amann-literature-synthesis) or write the proposal (amann-proposal-framing).
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 building exhibits for an Academy of Management Annals (Annals) review — the signature framework figure, who-studied-what synthesis tables, and search/coverage exhibits that summarize across a literature. Designs review exhibits; it does not produce original-estimate regression tables (an Annals review reports no new data of its own).
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 building the novel integrative framework that an Academy of Management Annals (Annals) review must deliver — the theory-advancing "new way of seeing the field" that is the acceptance bar, not a catalog of papers. Designs the spine; it does not gather the literature (amann-literature-synthesis) or appraise evidence balance (amann-evidence-standards).
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。
Prepare for investor calls by pulling upcoming meetings from Google Calendar, deeply researching each investor and their firm (website scraping, portfolio analysis, thesis extraction), checking for competitor conflicts, and outputting an honest prep sheet with compatibility assessments. Use when asked to prep for investor meetings, fundraising calls, VC meetings, or demo day.
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 VCs, angels, and investors - portfolio, thesis, contact info
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Detects cancer and infers tissue-of-origin from cfDNA methylation by choosing conversion chemistry (bisulfite vs EM-seq vs TAPS vs cfMeDIP), calling read-level methylation haplotypes rather than averaged beta values, and deconvolving a hematopoietic-dominated cfDNA mixture against a methylation atlas via NNLS/quadratic programming. Encodes the GRAIL/CCGA thesis that thousands of tissue-specific markers make methylation outperform sparse mutations for multi-cancer early detection (MCED) and localization, and that single concordantly-methylated fragments give ppm-level sensitivity. Uses MethylDackel for extraction (mbias-then-extract), MEDIPS/QSEA for enrichment data, scipy.optimize.nnls for deconvolution. Use when building an MCED or methylation-MRD assay, picking a conversion chemistry for low-input plasma, or deconvolving tissue-of-origin from cfDNA.
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。
Designs and analyzes pooled prime-editor (PE) screens for installing precise genetic variants without bystander confounding. Covers pegRNA design with PRIDICT and PRIDICT2 for predicting per-pegRNA editing efficiency, pegRNA architecture (spacer + scaffold + PBS + RTT), PE2/PE3/PE3b/PEmax variants, MOSAIC in situ saturation mutagenesis, the PRIME pooled-screen methodology (Ren 2023; ~3,699 ClinVar variant screens), chromatin context as a major locus-level determinant of PE efficiency, scaffold-incorporation and indel byproduct quantification with CRISPResso2, and the cross-modal validation strategy of PE + base-editor screens for variant function. Use when designing a pegRNA library for variant installation, choosing between BE and PE for a specific edit, predicting pegRNA efficiency before library synthesis, analyzing PE screen output, distinguishing intended-edit from scaffold-incorporation, or scaling PE screens to thousands of variants.
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。