366 件 ・ 関連度順
概要と使いどころ
Assesses platform investment opportunities with industry positioning, management quality, organic growth potential, and add-on runway. Use when evaluating platform deals, screening PE investments, or analyzing industry leaders.
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Evaluates bolt-on acquisitions for existing platforms with strategic fit, synergy quantification, and return contribution analysis. Use when screening add-on targets, building platform acquisition strategies, or modeling tuck-in economics.
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。
Evaluates distressed debt opportunities with recovery analysis, liquidity assessment, and potential restructuring outcomes. Use when screening distressed situations, analyzing stressed credits, or evaluating workout scenarios.
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。
Grade a trading card using PSA, BGS, or CGC standards. Covers observation-first assessment (adapted from meditate's unbiased observation), centering measurement, surface analysis, edge and corner evaluation, and final grade assignment with confidence interval. Supports Pokemon, MTG, Flesh and Blood, and Kayou cards. Use when evaluating a card before professional grading submission, pre-screening a collection for high-grade candidates, settling condition disputes between buyers and sellers, or estimating the grade-dependent value spread for a card.
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Map the intellectual property landscape for a technology domain or product area. Covers patent cluster analysis, white space identification, competitor IP portfolio assessment, freedom-to-operate preliminary screening, and strategic IP positioning recommendations. Use before starting R&D in a new technology area, when evaluating market entry against incumbents with strong patent portfolios, preparing for investment due diligence, informing a patent filing strategy, or assessing freedom-to-operate risk for a new product.
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Appraise gemstone value using the four Cs (color, clarity, cut, carat), origin assessment, treatment detection, and market factor analysis. Advisory educational guidance only — not a certified appraisal. Use when understanding factors that determine a gemstone's value, pre-screening stones before a professional appraisal, evaluating whether a seller's asking price is reasonable, learning gemstone grading methodology, or understanding how treatment status affects value.
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Medicinal chemistry filters for compound triage. Apply drug-likeness rules (Lipinski rule of five, Veber, Oprea, CNS, lead-like, rule of three), structural alert catalogs (PAINS a/b/c, NIBR screening-deck severity, Brenk, BMS, Glaxo, Dundee, ChEMBL common alerts), ZINC-15 percentile complexity metrics (Bertz, SAscore, QED, Whitlock, Barone), chemical-group detection, Lilly demerits, and the medchem query language (MATCHRULE, HASALERT, HASPROP, HASGROUP) for filtering a library at scale. Also trigger on medchem, `import medchem as mc`, RuleFilters, NIBRFilters, CommonAlertsFilters, NamedCatalogs, QueryFilter, PAINS filtering, or structural alerts.
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。
How to use the Adaptyv Bio Foundry API and Python SDK for protein experiment design, submission, and results retrieval. Use this skill whenever the user mentions Adaptyv, Foundry API, protein binding assays, protein screening experiments, BLI/SPR assays, thermostability assays, or wants to submit protein sequences for experimental characterization. Also trigger when code imports `adaptyv`, `adaptyv_sdk`, or `FoundryClient`, or references `foundry-api-public.adaptyvbio.com`.
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Molecular featurization hub with one consistent interface over 100+ featurizers. Fingerprints (ECFP/Morgan, MACCS, atom pair, topological torsion, Avalon, RDKit, ERG), RDKit and Mordred descriptor sets, pharmacophore and 3D shape descriptors, scaffold keys, and pretrained embeddings (ChemBERTa, ChemGPT, MolT5, GIN, Graphormer) through a common transformer API with caching and parallelism. Use this skill to convert SMILES into model-ready feature matrices for QSAR, virtual screening, and molecular ML, and to choose between featurizer families. Also trigger on molfeat, MoleculeTransformer, FPVecTransformer, PretrainedHFTransformer, molfeat model store, or featurizer selection.
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Rowan is a cloud-native molecular modeling and medicinal-chemistry workflow platform with a Python API. Use for pKa and macropKa prediction, conformer and tautomer ensembles, docking and analogue docking, protein-ligand cofolding, MSA generation, molecular dynamics, permeability, descriptor workflows, and related small-molecule or protein modeling tasks. Ideal for programmatic batch screening, multi-step chemistry pipelines, and workflows that would otherwise require maintaining local HPC/GPU infrastructure.
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Structure-based docking with AutoDock Vina, Vinardo, and AutoDock4 through the Meeko toolchain. Use this skill to define a docking box, prepare receptors and ligands as PDBQT, run single or batch docking, rescore, and interpret affinities, poses, and ligand efficiency. Covers box definition from a reference ligand or pocket residues, protonation and tautomer decisions, flexible side chains, exhaustiveness and seeds, redocking validation, and virtual screening over compound libraries. Also trigger on vina, smina, gnina, mk_prepare_ligand, mk_prepare_receptor, mk_export, scrub.py, PDBQT, autogrid4, docking box, or binding-pose prediction.
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DiffDock and DiffDock-L diffusion-based molecular docking. Use for blind protein-small-molecule pose prediction from a PDB file or sequence plus SMILES/SDF/MOL2, batch docking over a CSV of complexes, virtual screening triage, sampling multiple poses per complex, and reading the confidence score correctly. Also trigger on DiffDock, DiffDock-L, inference.py, confidence_model, samples_per_complex, ESM embedding preparation for docking, or blind docking without a defined box. Not for binding affinity prediction — the confidence score ranks pose plausibility, not potency.
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International journal scout — searches candidate SCI/SSCI/EI/Scopus journals via Wanfang foreign journal API, performs L1 deterministic rejection screening, and L2 citation fingerprint analysis.
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Chinese journal scout — searches candidate journals via Wanfang API, performs L1 deterministic rejection screening (language/funding/subject mismatch), and L2 citation fingerprint analysis (reference journal distribution → academic ecosystem positioning).
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International journal matcher — performs L3 6-dimensional matching for SCI/SSCI/EI/Scopus journals, computes conditional acceptance probability with review cycle estimation, applies 4-layer weighted ranking, and outputs tiered submission strategy including CAS warning screening and Chinese scholar friendliness analysis.
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プリセット選択式スクリーニング。ファンダメンタル条件でスクリーニング後、テクニカルフィルタを任意で適用。
"Apply information economics to diagnose and remedy market failures caused by asymmetric information. Use this skill when the user needs to analyze adverse selection, moral hazard, or signaling and screening mechanisms, especially in insurance, labor, credit, or product quality markets.".
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"M&A integration playbook covering eight modules: strategic rationale, target screening, due diligence (financial/legal/commercial), valuation with valuation bridge, synergy analysis, deal structuring (stock vs. asset, cash vs. equity, earn-out), SPA key clauses, and post-merger integration (PMI). Use for deal evaluation, valuation disputes, structure design, earn-out design, synergy breakdown, integration risk, or hostile takeover defense. Triggers: 『併購』『收購』『M&A』『盡職調查』『DD』『估值橋』『綜效』『earn-out』『換股比例』『PMI』『買殼』『借殼上市』『敵意併購』『交易結構』. For Taiwan EMBA 財管組 case studies and term reports (台大／政大／陽交). Complements Asgard `biz-dcf` and `fin-modeling` (valuation tools) plus `biz-corporate-governance` (governance layer) by providing the transaction-layer framework.".
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 content against your brand voice, style guide, and messaging pillars, flagging deviations by severity with specific before/after fixes. Use when checking a draft before it ships, when auditing copy for voice consistency and terminology, or when screening for unsubstantiated claims, missing disclaimers, and other legal flags.
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"Apply Benford's Law to detect anomalies in numerical datasets by analyzing first-digit frequency distributions. Use this skill when the user needs to audit financial data for fraud indicators, validate data integrity, or detect fabricated numbers — even if they say 'data manipulation detection', 'first digit test', or 'accounting fraud screening'.".
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Operate a logged-in Miaoshou ERP workflow for TikTok Shop Malaysia product discovery, product screening, profit estimation, listing preparation, and reviewed publishing. Use when the user asks to search or select products in 妙手 ERP, evaluate low-price traffic products, prepare Malaysian TikTok Shop listings, move products into the TikTok collection box, or publish approved listings. Prefer browser operation through the user's existing login; never store credentials or call undocumented private endpoints.
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。
Marketing analytics and data-driven optimization for B2B SaaS campaigns. Use this skill when analyzing marketing performance, optimizing conversion rates, running A/B tests, conducting customer research, creating marketing dashboards, building attribution models, analyzing funnel metrics, or storytelling with data. Also triggers on: marketing ops, performance analytics, CRO, A/B testing, data storytelling, customer insights, tracking plan, event taxonomy, UTM taxonomy, GA4 audit, consent mode, instrumentation contract, sample size calculation, minimum detectable effect, test duration, not enough traffic to A/B test, statistical significance, Bayesian vs frequentist, sample ratio mismatch, experiment feasibility, martech stack, marketing tech stack audit, tool consolidation, build vs buy, vendor evaluation, shadow IT tools, ESP/CRM/platform migration, switching cost, cutover plan, suppression list migration, data portability, customer interviews, survey design, sampling frame, non-response bias, how many people should I interview, thematic saturation, voice of customer, jobs-to-be-done, buyer persona research, is this finding real, data enrichment, which enrichment provider should we buy, waterfall enrichment, contact data quality, match rate, our list data is bad, email verification, catch-all domain, accept-all, bounce rate from bad data, B2B data decay, how often should we re-verify contacts, ZoomInfo vs Apollo vs Clearbit evaluation, cost per usable record, where did this list come from, GDPR Article 14 notice, purchased list compliance, suppression screening, TAM list building, demand planning, marketing plan, how many leads do we need, what pipeline target should marketing carry, funnel model, top-down vs bottom-up plan, plan of record, annual marketing planning, quarterly planning, pipeline coverage we have to create, sales cycle lag, our leads cannot close this quarter, carry-in pipeline, scenario planning, conservative moderate aggressive forecast, stretch target, reforecast, re-forecast triggers, assumption register, capacity ceiling, addressable audience ceiling, marginal cost per lead, blended CPL is wrong, we missed plan and nobody knows why, AI policy for the marketing team, AI governance, AI acceptable use policy, which AI tools are we allowed to use, can I paste customer data into ChatGPT, shadow AI, AI use register, human review of AI output, when can we stop reviewing every AI draft, can the agent send on its own, the AI made a mistake now what, pull back AI autonomy, AI evals, our AI vendor changed the model, AI output went out wrong, prompt injection, can our AI read prospect replies safely, lethal trifecta, the AI leaked internal notes, turn off auto-generated ad assets, auto-applied recommendations, AI literacy training, EU AI Act Article 4, duplicate records in the CRM, dedupe before import, which value wins in a merge, CRM cleanup.
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 a task needs the judgment of an emergency medicine physician — risk-stratifying an undifferentiated chief complaint against a can't-miss diagnosis, deciding an ED disposition (discharge/observation/admit/transfer) under time and bed pressure, reasoning through EMTALA screening-and-stabilization obligations, or triaging a department under crowding/boarding load.
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Use when a task needs the judgment of an environmental scientist — scoping or reviewing a Phase I/Phase II environmental site assessment, interpreting soil or groundwater sampling results against regulatory action levels, drafting an environmental impact statement or NEPA scoping document, running a contaminant fate-and-transport screening calc, or assessing ecological risk to a receptor.
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。