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Reads a paper figure by figure before re-integrating the full narrative, so the user can identify the core findings quickly and check whether each visual actually supports the authors' main claims. Always separate figure content, figure-linked claim, evidentiary strength, and unsupported interpretation. Never fabricate references, PMIDs, DOIs, figure content, panel labels, result values, or study details that were not actually provided.

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Use when generating Kaplan-Meier survival curves from tabular survival data containing time, event status, and a precomputed risk group. Supports command-line parameter input, parameter validation, automatic time-unit handling, single-file PDF figure export, and session metadata capture.

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 estimating relative immune cell infiltration from a bulk expression matrix with a CIBERSORT-style nu-SVR deconvolution workflow based on an LM22 signature matrix, comparing one case group against one control group, and generating structured tables plus immune-fraction plots. NOT for single-cell RNA-seq, spatial data, clinical diagnosis, or workflows that require the original hosted CIBERSORT web service.

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 a decision tree model in R and generating feature importance ranking outputs. Supports classification and regression, automatic task detection, parameter validation, model evaluation summaries, and exports of feature-importance tables and figures.

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 correcting batch effects in merged bulk expression matrices with sample-level batch metadata while preserving biological group structure and generating before-and-after QC plots. NOT for: single-cell integration, raw FASTQ processing, differential expression without batch labels, or datasets without biological groups.

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 performing GO and KEGG enrichment on a gene list from bulk RNA-seq or microarray studies, then generating a combined GO/KEGG dot chart. NOT for single-cell RNA-seq, methylation data, or non-expression data.

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。

Builds prioritized manuscript revision plans for major or minor revisions by separating comments that require experiments, analyses, clarification, restructuring, or wording changes.

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 profiling CPU/memory hot paths, generating flame graphs, or capturing JFR/perf 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。

Use when generating performance hypotheses backed by git history and code 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。

Guides Metronome usage-based billing integration decisions — event ingestion (single and batch, idempotency, billable metrics), contract design (rate cards, overrides, dimensional pricing, products), invoicing lifecycle (grace periods, finalization, Stripe sync), credit and commit management (prepaid, postpaid, thresholds, auto-recharge), and Stripe integration (arrears invoicing, tax providers, line item limits). Use when building, modifying, or reviewing any Metronome integration — including ingesting usage events, creating customers, contracts, or rate cards, designing billable metric group keys, managing credits and commits, configuring invoicing, syncing invoices with Stripe Billing, billing AI applications for token usage, setting up self-serve (PLG) pricing, reviewing customer health, commit burn, portfolios, or renewals, or migrating from Stripe Billing Meters to Metronome.

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Generates audit-grade, hallucination-free documentation from codebase features with full traceability. Use when documenting features, creating technical specs, or generating verified documentation with code references.

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 when integrating a new requirement into an existing design.

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 Trivy vulnerability scanner in offline mode to discover security vulnerabilities in dependency files. This skill covers setting up offline scanning, executing Trivy against package lock files, and generating JSON vulnerability reports without requiring internet 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。

Migrate Java EE javax.* imports to Jakarta EE jakarta.* namespace. Use when upgrading to Spring Boot 3.x, migrating javax.persistence, javax.validation, javax.servlet imports, or fixing compilation errors after Jakarta EE transition. Covers package mappings, batch sed commands, and verification steps.

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。

Linearizing nonlinear dynamics around operating points for control design.

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。

Automated Planning utilities for loading PDDL domains and problems, generating plans using classical planners, validating plans, and saving plan outputs. Supports standard PDDL parsing, plan synthesis, and correctness verification.

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。

Compare and implement eviction policies (LRU, LFU, FIFO, S3FIFO, ARC) for bounded-capacity caches. Use when choosing or implementing an eviction policy for a buffer pool, page cache, CDN edge, or LLM KV cache, or when writing a replay simulator that supports multiple policies. Clarifies recency vs frequency semantics, queue topology, saturating counters, ghost buffers, and the second-chance rule that distinguishes modern FIFO-family policies from classic LRU.

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。

Matched filtering techniques for gravitational wave detection. Use when searching for signals in detector data using template waveforms, including both time-domain and frequency-domain approaches. Works with PyCBC for generating templates and performing matched filtering.

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。

Generator economic dispatch and cost optimization for power systems. Use when minimizing generation costs, computing optimal generator setpoints, calculating operating margins, or working with generator cost functions.

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 this skill when simulating quadrotor physical dynamics — mapping desired thrust/moments to individual motor RPMs via a propeller allocation matrix, applying first-order motor lag, and integrating the nonlinear equations of motion (translational and rotational) using RK45.

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 this skill when working with symbolic mathematics in Python. This skill should be used for symbolic computation tasks including solving equations algebraically, performing calculus operations (derivatives, integrals, limits), manipulating algebraic expressions, working with matrices symbolically, physics calculations, number theory problems, geometry computations, and generating executable code from mathematical expressions. Apply this skill when the user needs exact symbolic results rather than numerical approximations, or when working with mathematical formulas that contain variables and parameters.

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。

Implement paper-defined attention variants by extracting mechanism invariants, mapping tensor shapes, preserving module interfaces, validating numerical behavior, and integrating the custom attention into an existing transformer stack.

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。

OpenAI Text-to-Speech API for high-quality speech synthesis. Use for generating natural-sounding audio from text with customizable voices and tones.

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。

Fetch and parse LeetCode problems with metadata, constraints, examples, hints, difficulty ratings, and related problems. Integrates with LeetCode API for comprehensive problem data retrieval.

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。