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概要と使いどころ
Evaluates regulated utility rate-setting with RAB methodology, allowed return analysis, and regulatory reset risk assessment. Use when analyzing regulatory frameworks, modeling rate cases, or evaluating allowed return mechanics.
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 renewable energy project economics with PPA structures, merchant tail risk, and production profile analysis. Use when analyzing wind/solar projects, evaluating PPA terms, or modeling renewable energy cash flows.
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 MBS structures with prepayment modeling (CPR/CDR), collateral analysis, and tranche-level credit risk assessment. Use when analyzing MBS, modeling prepayment scenarios, or evaluating residential mortgage pools.
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 mezzanine structures with PIK toggle, equity kickers, and intercreditor subordination mechanics. Use when analyzing mezzanine financing, comparing subordinated debt terms, or modeling layered capital structures.
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 management fee designs with commitment-period vs invested-capital bases, step-downs, and offset provisions. Use when analyzing fee structures, comparing fee levels, or modeling fee revenue for GPs.
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 midstream assets with throughput analysis, fee-based vs commodity-exposed revenue, and contract structure assessment. Use when analyzing midstream investments, evaluating pipeline assets, or assessing gathering systems.
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 optimal leverage with WACC minimization, rating impact, and financial flexibility assessment across market conditions. Use when optimizing capital structure, analyzing target leverage, or evaluating rating implications.
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 GP-led continuation vehicle structures with existing LP options, pricing methodology, and new investor terms. Use when analyzing continuation vehicles, structuring GP-led transactions, or evaluating tender offers.
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 non-traditional securitization collateral including solar, data centers, digital infrastructure, and IP royalties. Use when analyzing esoteric ABS, evaluating non-standard collateral, or structuring novel asset classes.
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 target leverage capacity with cash flow coverage, stress testing, and optimal debt structure across term loans, revolver, and mezzanine. Use when analyzing debt capacity, structuring acquisition financing, or stress testing leverage.
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 ingesting text samples to extract quantitative writing style metrics. Produces a structured JSON style profile with vocabulary patterns, sentence structure, tone markers, and formatting preferences. Invoke before voice-clone-generator to establish style constraints.
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。
Comprehensive 7-phase SEO optimization workflow covering technical SEO auditing, keyword research strategy, on-page optimization, content SEO and E-E-A-T, structured data implementation, link building, and AI search optimization. Use when auditing search engine visibility, diagnosing ranking issues, implementing schema markup, conducting keyword research, optimizing content for E-E-A-T signals, or adapting for AI-driven search (AI Overviews, SGE).
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, structure, or improve form interfaces for clarity, completion rates, and user confidence. Use when the user asks to build a form, redesign a form flow, improve form conversion, add multi-step forms, fix form UX, or structure field layouts and validation.
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。
Detect, validate, and generate Schema.org structured data. JSON-LD format preferred. Use when user says "schema", "structured data", "rich results", "JSON-LD", or "markup".
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。
Technical SEO audit across 9 categories: crawlability, indexability, security, URL structure, mobile, Core Web Vitals, structured data, JavaScript rendering, and IndexNow protocol. Use when user says "technical SEO", "crawl issues", "robots.txt", "Core Web Vitals", "site speed", or "security headers".
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。
AWS の構成図を draw.io の XML（.drawio）で描く。公式の AWS4 アイコンを使い、タイトルや凡例や番号バッジのような装飾を足さず、どのサービスがどこにあって何とつながっているかだけで読める図にする。AWS 構成図、アーキテクチャ図、インフラ構成図、ネットワーク構成図、VPC の図を描いてほしいとき、draw.io で AWS の図を作りたいとき、既存の構成図を直したいときは必ずこの Skill を使う。Use this whenever the user asks for an AWS architecture diagram, cloud infrastructure diagram, VPC diagram, or a draw.io file of AWS resources, even if they don't say "draw.io". Mermaid、PlantUML、Python の diagrams ライブラリなど、ほかの形式を指定されたときは使わない。Do not use it when the user explicitly asks for Mermaid, PlantUML, or the Python diagrams library.
Audit a repo's test, lint, type-check, static analysis, build, and debug infrastructure for AI coding agents. Generate scored reports and optimized configs for the lint-on-write hook. Triggers on audit tests, test harness, lint setup, check test infrastructure, entering a new 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。
Use when analyzing vault structure, finding orphan notes, discovering missing connections, identifying bridge concepts, or checking vault health from a graph perspective. Triggers on "vault graph", "map vault", "find orphans", "missing links", "vault structure", "knowledge graph".
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 and scores Claude prompts and system prompts using a six-dimension Scorecard with anchored 1-5 rubrics. Trigger on: "review my prompt," "score this prompt," "rate my prompt," "evaluate this prompt," "improve my prompt," "what's wrong with my prompt." Also trigger on: "why isn't my prompt working," "my prompt produces bad output," "help me fix this prompt," "is this prompt any good." Scorecard covers Objective Clarity, Context Specificity, Reasoning Fit, Output Precision, Behavioral Calibration, and Architectural Efficiency, plus a five-question Output Evaluation Rubric for assessing actual output. Also use when the user pastes a system prompt and asks for feedback. Activate whenever structured quality assessment of a single prompt is the primary need. Do NOT use for project-level audits involving Custom Instructions architecture, knowledge file organization, or multi-file Project structure (use rootnode-project-audit if available).
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。
Protein language models through the EvolutionaryScale `esm` Python SDK. Generate and embed sequences with ESM3 (multimodal sequence, structure and function prompting), extract per-residue and mean-pooled embeddings with ESM C, fold sequences with ESMFold2, and run inference locally or against the Forge and Biohub hosted clients. Use this skill for protein representation learning, variant effect and mutational scanning from likelihoods, sequence generation and inpainting, structure prediction from sequence alone, and embedding features for downstream models. Also trigger on esm, ESM3, ESMC, ESM Cambrian, ESMFold2, `from esm.models`, ESMProtein, GenerationConfig, forge.evolutionaryscale.ai, biohub.ai, or ESM_API_KEY.
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。
Retrieve protein sequences, annotation, and structures from UniProtKB, the RCSB PDB, and AlphaFold DB. Use this skill to resolve a gene or protein name to a UniProt accession, pull sequences and FASTA files, find binding sites and domains, search the PDB by UniProt accession, sequence, ligand, or text, download mmCIF/PDB coordinates and biological assemblies, fetch AlphaFold models with their pLDDT confidence, and check whether a structure is actually usable before docking or simulating it. Also trigger on UniProt accessions, PDB ids, rest.uniprot.org, search.rcsb.org, files.rcsb.org, alphafold.ebi.ac.uk, id mapping, SEQRES, or missing residues.
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。
This skill should be used when the user asks to "create a plugin", "scaffold a plugin", "understand plugin structure", "organize plugin components", "set up plugin.json", "use ${CLAUDE_PLUGIN_ROOT}", "add commands/agents/skills/hooks", "configure auto-discovery", or needs guidance on plugin directory layout, manifest configuration, component organization, file naming conventions, or Claude Code plugin architecture best practices.
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。
Identify insects using body plan analysis, dichotomous keys to order, wing venation, mouthpart type, antennae form, leg and tarsal structure, and confidence levels. Covers the fundamental hexapod body plan verification, a simplified dichotomous key to major orders, wing venation and type analysis, mouthpart classification, antennae morphology, leg specialization and tarsal formula, and a structured confidence assessment framework. Use when you need to identify an unknown insect beyond preliminary order placement, are working through a specimen for taxonomic study, want to distinguish between similar orders or families, or need to assign a confidence level to a field identification.
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。
Forecast infrastructure and application metrics using Prophet or statsmodels for capacity planning, cost optimization, and proactive scaling. Visualize predictions in Grafana and set up alerts for projected resource exhaustion. Use when forecasting infrastructure capacity needs for CPU, memory, or disk, planning hardware procurement for next quarter, predicting cost trends to optimize cloud spending, or setting up proactive scaling policies based on predicted load.
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。