1,577 件 ・ 関連度順
概要と使いどころ
Comprehensive product launch planning including GTM strategy, launch checklists, stakeholder communication, beta testing plans, and post-launch analysis. Execute successful product launches with coordinated teams.
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。
A vibrant claymation-meets-data interface for Clay.com (GTM data-orchestration platform). Anchors on white canvas with dark-navy primary CTAs, custom rounded display type, and saturated single-color feature cards — hot pink, deep teal, lavender, peach, ochre — that punctuate long-scroll explainer pages. Brand voltage comes from 3D-rendered claymation illustrations (mountains, characters, mascots) used as full-bleed hero artifacts and the bright multi-color card surfaces showing product UI fragments.
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。
Analyze competitors with strengths, weaknesses, and differentiation opportunities. Identifies direct competitors and maps the competitive landscape. Use when doing competitive research, preparing a competitive brief, or finding differentiation opportunities.
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。
Break down full ICLR reviewer responses into structured rebuttal units. Use when input contains reviewer summary/presentation/contribution/strength/weakness/question text and the goal is to split weaknesses/questions into granular R-style response items while preserving original wording for quoted 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。
Break down full ARR (ACL Rolling Review) reviewer responses into structured rebuttal units. Use when input contains ARR reviewer fields (Paper Summary, Strengths, Weaknesses, Comments/Suggestions) and numeric scores (Confidence, Soundness, Excitement, Overall Assessment, Reproducibility). Splits weaknesses and comments/suggestions into granular response items while preserving original wording for quoted 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。
Break down full ICML reviewer responses into structured rebuttal units. Use when input contains reviewer summary/presentation/contribution/strength/weakness/question text and the goal is to split weaknesses/questions into granular R-style response items while preserving original wording for quoted 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。
Break down full NeurIPS reviewer responses into structured rebuttal units. Use when input contains NeurIPS reviewer fields (Summary, Strengths and Weaknesses, Questions, Limitations) and numeric scores (Rating, Confidence, Quality, Clarity, Significance, Originality). Splits questions and limitations into granular response items while preserving original wording for quoted 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。
Real-world Active Directory environment constraints that silently break attacks when ignored: NTLM disabled (Kerberos fallback), AES-only KDCs (RC4 blocked by GPO), LDAP signing and channel binding requiring LDAPS on 636, Kerberos clock skew (KRB_AP_ERR_SKEW), SPNs that must be FQDNs never short names or IPs (the misleading invalidCredentials), Protected Users / LAPS / gMSA / MachineAccountQuota, LDAP 1000-object paging, and VPN latency. Use this whenever a bind, TGT request, LDAP query, or SMB negotiation fails in a way that looks like bad credentials but is really hardening, or before running any AD tooling against a domain you have not fingerprinted. Standard-tooling knowledge (netexec, impacket, certipy, bloodyAD, kerbrute), no vendor-specific 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。
Think and work like an expert Microbial Ecologist. Use when a task calls for Microbial Ecologist judgment. Reasons from Vellend assembly (selection, dispersal, drift, diversification), compositional stats (ANCOM-BC2, MaAsLin2, Aitchison), and SILVA/GTDB/EMP workflows; treats kitome contamination, GCN bias, pseudoreplication, SparCC-as-interaction, and PICRUSt2-as-measured-function as first-class failure modes.
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。
Think and work like an expert Microbiologist. Use when a task calls for Microbiologist judgment. Reasons from culturability limits, CFU/MPN enumeration, selective media, DADA2/QIIME2 16S ASVs (SILVA/GTDB), and shotgun metagenomics (Kraken2, MetaPhlAn, HUMAnN); treats plate-count anomaly, compositional stats pitfalls, kit contamination, and index hopping as first-class failure modes.
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。
Think and work like an expert Glycobiologist. Use when a task calls for Glycobiologist judgment. Reasons from N-/O-glycan biosynthesis, O-GlcNAc cycling (OGT/OGA), LC-MS glycomics, exoglycosidase sequencing, and lectin microarrays; uses GlyTouCan/SNFG/MIRAGE, pGlyco/GlycoWorkbench, and treats PNGase F limits, isomer collapse, and ER-stress high-mannose as first-class failure modes.
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。
Think and work like an expert Geophysicist. Use when a task calls for Geophysicist judgment. Reasons from wavelength and skin-depth limits through earthquake catalogs (USGS ComCat, ObsPy/FDSN), Bouguer/IGRF gravity-magnetics, MT distortion and dimensionality, and SEG-Y reflection processing (NMO, migration) while treating statics, galvanic distortion, velocity ambiguity, and header mis-mapping as first-class failure modes.
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。
Think and work like an expert Environmental Microbiologist. Use when a task calls for Environmental Microbiologist judgment. Reasons from spatial patchiness, redox thermodynamic ceilings, and process-over-taxonomy guild function through DADA2/QIIME2 amplicons keyed to SILVA/GTDB/PR2/UNITE, metaSPAdes/MetaBAT2 MAGs vetted by CheckM/GUNC, and SIP/qSIP rate assays paired with IC/GC/ICP-MS chemistry, while treating extraction-batch effects, relic and extracellular DNA, primer-window bias, and core pseudoreplication as first-class failure modes.
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。
Think and work like an expert Composites Engineer. Use when a task calls for Composites Engineer judgment. Reasons from CLT/ABD laminate mechanics, Halpin–Tsai micromechanics, and CMH-17/NCAMP allowables through autoclave/OOA/RTM process control, ASTM D30 mechanical qualification, ultrasonic C-scan and CAI damage tolerance while treating fiber waviness, void content, under-cure, and quasi-isotropic strength traps as first-class failure modes.
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。
Think and work like an expert Computer Vision Scientist. Use when a task calls for Computer Vision Scientist judgment. Reasons from image formation, projective geometry (pinhole intrinsics and extrinsics, epipolar/PnP/bundle adjustment, similarity-scale ambiguity), and COCO/LVIS AP mechanics through DINOv3/SigLIP 2 foundation baselines, RF-DETR/YOLO26/SAM 3 models, COLMAP 4/GLOMAP and VGGT geometry, pycocotools/TrackEval/BOP evaluation, and CVPR reporting and EU AI Act limits while treating train–test and pretraining leakage, AP evaluation-setting gaming, preprocessing mismatches (EXIF, BGR, aliased resizing), label noise, and camera-convention and scale errors as first-class failure modes.
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。
Think and work like an expert Antenna Engineer. Use when a task calls for Antenna Engineer judgment. Reasons from gain–directivity–efficiency, Chu–Harrington bandwidth limits, and array factor through HFSS/CST/FEKO synthesis, IEEE 149-2021 NF/FF/CATR metrology, CTIA TRP/TIS/ECC OTA, and Friis link budgets while treating ground-plane truncation, active impedance in arrays, range ripple, and S₁₁≠pattern conflation as first-class failure modes.
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。
Think and work like an expert Algorithms Researcher. Use when a task calls for Algorithms Researcher judgment. Reasons from separating problem, model, and cost model (comparison, word-RAM, arithmetic, online) through exchange/matroid greedy proofs, subproblem-DAG dynamic programming, max-flow min-cut and Goemans–Williamson primal-dual rounding, Karp–Rabin fingerprinting, competitive ratio and Yao's principle, PTAS/FPTAS (Williamson–Shmoys), and Instance Space Analysis over DIMACS10/MIPLIB 2017/SuiteSparse while treating amortized-versus-average-case conflation, unproven greedy killed by a 4-node counterexample, Monte Carlo without a false-match probability, DIMACS10 suite overfitting, and 'linear time' hiding word-size tricks over bit-length L as first-class failure modes.
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。
Think and work like an expert Atomic, Molecular & Optical Physicist. Use when a task calls for Atomic, Molecular & Optical Physicist judgment. Reasons from Rabi/OBE dynamics, laser-cooling hierarchy, optical lattices and Hubbard U/J, and magic-wavelength clock budgets through NIST ASD, ARC, QuTiP/PyLCP, and in-situ lattice-depth calibration while treating power-based depth guesses, polarization impurities, BBR shifts, and imaging-heating conflation as first-class failure modes.
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。
Weekly product-level commercial tracking — TRx/NRx with current/4-wk/12-wk share and YoY, launch curves overlaid with same-class analogs plus a consensus-implied trajectory, LOE/biosimilar erosion series, GTN/net-price stress brackets, and per-row data-caveat discipline. Use to track any launched drug, biologic, device, or vaccine against its class and consensus.
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 full-length audiobooks and long-form narration with generative voice tools. Use when the task is to turn a manuscript or long text into hours of spoken audio: preparing the manuscript for narration (front/back matter, footnotes, tables, dialogue), casting a single narrator or full cast, controlling pronunciation and voice consistency across a whole book, running a proofing/QC listen, meeting a retailer's technical delivery specs (RMS, peak, noise floor, room tone, chapterized files, metadata), and choosing a distribution route under each platform's current AI-narration policy. Not for short TTS clips, single-line voiceover, podcast production, or captioning existing video.
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。
Apply umap-learn in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses umap-learn or its strengths fit the task.
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。
Apply vaex in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses vaex or its strengths fit the task.
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。
Apply validate data in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses validate data or its strengths fit the task.
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。
Apply seaborn in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses seaborn or its strengths fit the task.
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。