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概要と使いどころ
Adjusts reported earnings for non-recurring items, accounting policy choices, and run-rate normalizations. Use when performing QofE analysis, adjusting EBITDA, or validating seller financial presentations.
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。
Structures S-1/F-1 prospectus content with business description, risk factors, MD&A, and financial presentation requirements. Use when preparing IPO prospectuses, drafting SEC filings, or structuring offering documents.
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。
Creates debt offering memoranda with credit overview, financial analysis, industry section, and risk factors for investor marketing. Use when preparing debt OMs, writing credit marketing materials, or structuring lender presentations.
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 make-whole premium claims in bankruptcy with contract interpretation, present value disputes, and secured status analysis. Use when analyzing make-whole claims, evaluating redemption disputes, or assessing premium recovery.
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。
Prepares credit rating agency presentations with methodology alignment, peer positioning, and target rating analysis. Use when advising on credit ratings, preparing rating agency meetings, or analyzing rating methodology impact.
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。
Generates architecture, database, and system diagrams using Mermaid syntax. Creates visual representations of system architecture, database schemas, component relationships, data flows, and standalone HTML exports.
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 or improve search experiences, result presentation, and filtering interfaces. Use when the user asks to add search, redesign search results, improve findability, build autocomplete, add filters, or fix zero-results dead ends.
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 and order products online. Compares prices across Polish marketplaces (Allegro, Ceneo, Google Shopping), presents options in a comparison table, and assists with checkout. Handles the full flow: priority selection, research, comparison, cart, and checkout handoff. Triggers on: "order", "buy", "find product", "compare prices", "cheapest", "zamow", "kup", "znajdz produkt", "porownaj ceny", "najtaniej".
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。
Hunt GraphQL-specific vulns — introspection abuse, field-level authorization (IDOR), batched queries for brute force, alias-based rate limit bypass, deep query DoS, mutation injection, schema disclosure, suggestion leakage, CSRF via GET, and CSRF via POST without preflight. Use when a GraphQL endpoint is present.
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 explicitly asks EZ_math_model to create defense slides, academic presentation slides, Beamer slides, or PPT after the modeling paper has been packaged.
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。
Validate SEO and LLM-readability claims against live HTML, local fixtures, and generated artifacts. Use this skill when the user asks to prove whether title, meta description, canonical, hreflang, JSON-LD, NewsArticle.image, OpenGraph, Twitter cards, RSS autodiscovery, robots.txt, llms.txt, sitemap, news-sitemap, crawler access, or performance evidence is actually present, and when subjective audits must become timestamped pass/fail reports.
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。
Extract transcript and key slides from a local video file, then create a vault-formatted lecture note. Use this skill whenever a user provides a local video file (.mp4, .mov, .mkv, .avi, .webm) and wants notes from it. Triggers on "lecture", "take notes", "I have a recording of", "class video", "transcribe this presentation", "process this video for notes", or /lecture. Works with any language — auto-detects transcript language.
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。
Configure render settings, compositing nodes, output formats, and execute renders via Cycles or EEVEE engines using Python API or command-line interface. Use when automating render execution for batch processing, configuring quality and performance trade-offs, setting up compositing pipelines for post-processing, generating multiple output formats from a single render, or producing final output for publication or presentation.
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。
Epistemic transparency — acknowledging uncertainty, flagging limitations, avoiding overconfidence, and communicating what is known, unknown, and uncertain with proportional confidence. Maps the HEXACO personality dimension to AI reasoning: truthful calibration of confidence, proactive disclosure of gaps, and resistance to the temptation to appear more certain than warranted. Use before presenting a conclusion, when answering questions where knowledge is partial or inferred, after noticing a temptation to state uncertain information as certain, or when a user is making decisions based on provided information.
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。
Systematically interpret infrared spectra to identify functional groups present in a sample. Covers diagnostic region analysis (4000-1500 cm-1), fingerprint region assessment (1500-400 cm-1), hydrogen bonding effects, and compilation of a functional group inventory with confidence levels.
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。
Create general-purpose Dockerfiles for Node.js, Python, Go, Rust, and Java projects. Covers base image selection, dependency installation, user permissions, COPY patterns, ENTRYPOINT vs CMD, and .dockerignore. Use when containerizing an application for the first time, creating a consistent build/runtime environment, preparing an app for cloud deployment or Docker Compose, or when no existing Dockerfile is present in the project.
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。
Create production-ready Grafana dashboards with reusable panels, template variables, annotations, and provisioning for version-controlled dashboard deployment. Use when creating visual representations of Prometheus, Loki, or other data source metrics, building operational dashboards for SRE teams, migrating from manual dashboard creation to version-controlled provisioning, or establishing executive-level SLO compliance reporting.
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。
Address unwanted dog behaviors through desensitization, counter-conditioning, and management. Covers reactivity (to dogs, people, sounds), separation anxiety, resource guarding, excessive barking, and leash pulling using force-free methods with systematic threshold management. Use when a dog shows reactivity toward other dogs, people, or stimuli; when separation anxiety causes destructive behavior or vocalization; when resource guarding is present; or when behaviors like excessive barking or leash pulling interfere with daily life — after basic obedience is established.
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。
Operator-side guide for generating a target-specific training curve chart for a legacy experiment PR. Use when auditing or presenting historical runs.
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。
Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For ready-made ADMET numbers without training a model use admet-prediction; for benchmark datasets and task-aware splits use pytdc.
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。
Use when modeling a domain, encoding a state machine, hardening APIs, making invalid states unrepresentable, or parsing instead of validating. Not for TypeScript: use typescript-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。
Create standardized charts and visual assets from analytics query results. Prefer dependency-free plain HTML/CSS/JavaScript/SVG for interactive exploratory charts; use local plotting tools such as matplotlib, seaborn, or Plotly when static/report-ready exports or specialized charting libraries are more appropriate. Use for trends, comparisons, distributions, report assets, CSV-to-chart work, and presentation-ready data visuals.
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。
Create standardized charts and visual assets from analytics query results. Prefer dependency-free plain HTML/CSS/JavaScript/SVG for interactive exploratory charts; use local plotting tools such as matplotlib, seaborn, or Plotly when static/report-ready exports or specialized charting libraries are more appropriate. Use for trends, comparisons, distributions, report assets, CSV-to-chart work, and presentation-ready data visuals.
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。