Tests associations between categorical variables in clinical data using chi-square, Fisher's exact, Boschloo, Cochran-Mantel-Haenszel, and modern McNemar variants with calibrated confidence intervals (Wilson, Newcombe, Miettinen-Nurminen). Use when analyzing categorical outcomes, paired binary endpoints, or testing treatment-outcome independence in confirmatory or exploratory clinical trials.
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。
huang-sh/DeepScience☆ 42026年7月15日 更新
Training patterns for autoregressive neural PDE solvers (FNO, DeepONet, CNO). Covers rollout training, noise injection for stability, multi-component loss functions (H1, frequency-sensitive, boundary-aware), per-channel normalization for coupled multi-variable systems, and the PDEBench nRMSE metric. Use when training any neural operator that predicts time-dependent PDE solutions.
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。
huang-sh/DeepScience☆ 42026年7月15日 更新
Hamiltonian mechanics — symplectic integrators (leapfrog, Yoshida), Hamilton's equations, Poisson brackets, canonical transformations, action-angle variables, and KAM theory analysis. Use for energy-conserving long-time integration of conservative 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。
huang-sh/DeepScience☆ 42026年7月15日 更新
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。
huang-sh/DeepScience☆ 42026年7月15日 更新
Orchestrates an end-to-end long-read structural-variant pipeline - basecalling to minimap2 alignment (platform-matched preset) to Sniffles2/cuteSV/pbsv calling to optional assembly-based calling (dipcall/PAV) to two-step .snf cohort merging to Truvari benchmarking - chaining ONT and PacBio HiFi runs while handing the SV signal mechanism off to the component skills. Use when running a long-read SV workflow from reads to a benchmarked callset, choosing the minimap2 preset and SV caller by platform and goal, deciding when long reads are worth it for the insertions and repeat-mediated SVs short reads physically miss, building a joint-genotyped cohort with the two-step .snf design, or parameterizing a Truvari benchmark against GIAB HG002 Tier 1 plus CMRG. Not for the SV signal mechanism itself (see variant-calling/structural-variant-calling) or short-read SV.
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。
huang-sh/DeepScience☆ 42026年7月15日 更新
Orchestrates the end-to-end germline short-variant pipeline from FASTQ to a filtered, normalized, benchmarked VCF, chaining QC/trim, BWA-MEM2 alignment, duplicate marking, optional BQSR, calling (bcftools/GATK HaplotypeCaller/DeepVariant/DRAGEN), normalization, site+genotype filtering, annotation, and hap.py/vcfeval benchmarking. Use when deciding the pipeline-wide reference-genome commitment (GRCh38 analysis set vs T2T, ALT/decoy handling), sequencing the steps in the defensible order (normalize BEFORE annotate, filter site- then genotype-level), choosing the calling engine and single-sample vs cohort joint-calling, picking a filtering strategy by cohort size, or benchmarking stratified within GIAB confident regions. Hands off mechanism to the variant-calling and read-alignment component skills; not a re-teach of any single step.
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。
huang-sh/DeepScience☆ 42026年7月15日 更新
Drug-combination synergy analysis — quantify whether two drugs together are synergistic, additive, or antagonistic using the standard reference models (Bliss independence, HSA / highest single agent, Loewe additivity, ZIP, and the Chou-Talalay Combination Index). Use when you have measured single-drug and combination effects (inhibition/viability) and need a synergy score. Explains which model to use, what data each one needs, and how to read the score. NOT for looking up pre-computed synergy in a database (use the SYNERGxDB tool / cell-line-profiling skill).
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。
huang-sh/DeepScience☆ 42026年7月15日 更新
Lipid analysis and lipid-disease associations using LIPID MAPS classification, HMDB metabolite data, KEGG/Reactome lipid pathways (sphingolipid, eicosanoid, steroid, fatty acid), and PubChem chemical info. Use for lipid identification, lipid metabolism pathway mapping, and lipid-associated disease analysis (cardiovascular, diabetes, NAFLD).
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。
huang-sh/DeepScience☆ 42026年7月15日 更新
Interpretable ADMET analysis with mechanistic reasoning. Maps liabilities to structural causes and biological pathways. Based on CoTox (Park 2025) and DrugR (Liu 2026).
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。
huang-sh/DeepScience☆ 42026年7月15日 更新
Zero-knowledge proofs and privacy patterns on Stellar. Covers Groth16 verification in smart contracts via BLS12-381 host functions (CAP-0059, available), the BN254 + Poseidon proposals (CAP-0074/0075, status-sensitive), and concrete toolchain walkthroughs for Circom (on-chain verifiable today), Noir, and RISC Zero (attestation pattern until BN254 lands). Use when building privacy-preserving applications, ZK-verifier contracts, or wiring a proving toolchain to Stellar.
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。
stellar-zk/stellar-zk☆ 42026年10月10日 更新
Maintain English-source localization, adding Russian only when requested, with correct owner paths, variables, selectors, and runtime behavior.
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。
ArcaneSS14/arcane-new☆ 42026年10月13日 更新
Preserve user work, scope, metadata, history, and verifiable delivery.
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。
ArcaneSS14/arcane-new☆ 42026年10月13日 更新
Evaluate AI agent performance and reliability. TRIGGERS - Use when user needs help with ai-agent-evaluation related tasks.
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。
Winbda/claude-skills-collection☆ 42026年4月6日 更新
Downloads CARLA so nothing has to be fetched by hand — an official release package for any published version (resolved live from the GitHub releases, with AdditionalMaps), the rolling nightly Dev build, a shallow source checkout of a branch or tag, or the carlasim/carla Docker image. Reports exactly what was obtained and where, plus the environment variables the other skills read. Use when the user asks to "download CARLA", "get CARLA 0.9.16", "install CARLA", "clone the carla repo", "fetch the latest CARLA", or has no CARLA at all yet.
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。
carla-simulator/carla-agentic-tools☆ 42026年9月12日 更新
Expert database administrator specializing in high-availability systems, performance optimization, and disaster recovery. Masters PostgreSQL, MySQL, MongoDB, and Redis with focus on reliability, scalability, and operational excellence.
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。
aibot88/sec_skill_store☆ 42026年5月27日 更新
Expert payment integration specialist mastering payment gateway integration, PCI compliance, and financial transaction processing. Specializes in secure payment flows, multi-currency support, and fraud prevention with focus on reliability, compliance, and seamless user experience.
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。
aibot88/sec_skill_store☆ 42026年5月27日 更新
Expert network engineer specializing in cloud and hybrid network architectures, security, and performance optimization. Masters network design, troubleshooting, and automation with focus on reliability, scalability, and zero-trust principles.
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。
aibot88/sec_skill_store☆ 42026年5月27日 更新
Expert Kubernetes specialist mastering container orchestration, cluster management, and cloud-native architectures. Specializes in production-grade deployments, security hardening, and performance optimization with focus on scalability and reliability.
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。
aibot88/sec_skill_store☆ 42026年5月27日 更新
Use when designing how an Agentforce agent extracts structured input parameters (slots) from a user's natural-language utterance to invoke an action. Triggers: 'agent extract date from user', 'agent action argument extraction', 'agentforce slot filling', 'agent invocable input mapping', 'agent fails to fill required parameter'. NOT for action authoring (use agentforce/agent-actions) or for prompt-template variable binding (use agentforce/prompt-builder-templates).
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。
aibot88/sec_skill_store☆ 42026年5月27日 更新
Use this skill when adding a new symbiont (agent integration) to Myco's SymbiontInstaller — the component that manages agent lifecycle operations (init, update, remove, doctor) for all registered agents. Activates whenever you need to onboard a new AI agent (Claude Code, Cursor, Windsurf, a custom agent, etc.) so that `myco init`, `myco update`, and `myco doctor` manage its installation. Apply this skill even if the user doesn't explicitly mention SymbiontInstaller — any time you're adding a new agent type, creating a symbiont manifest, wiring new hook templates, or extending the supported agents list, this skill applies. Also relevant when a new symbiont needs the cross-platform hook guard or environment variable injection.
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。
aibot88/sec_skill_store☆ 42026年5月27日 更新
Analyzes advertising yield across programmatic and direct-sold inventory including CPM/CPC/CPA performance, header bidding efficiency, fill rate optimization, inventory monetization strategies, and compliance with IAB standards and OpenRTB protocols. USE THIS SKILL WHEN: - You need to audit ad revenue performance or diagnose yield drops - Someone asks about header bidding configuration or Prebid.js optimization - You are analyzing fill rates, eCPM trends, or ad unit performance - A project involves programmatic advertising, SSP integration, or ad serving - You need to verify ads.txt, sellers.json, or supply chain compliance - Someone mentions CPM floors, bid density, or auction dynamics - You are evaluating ad quality controls, viewability, or ad-related UX impact - A codebase integrates Google Ad Manager, AdMob, Prebid, or any SSP TRIGGER PHRASES: "ad yield", "eCPM", "fill rate", "header bidding", "Prebid", "programmatic ads", "ad monetization", "CPM optimization", "ad revenue", "ads.txt", "ad serving", "SSP", "ad unit performance", "viewability"
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。
aibot88/sec_skill_store☆ 42026年5月27日 更新
[32] ANALYZE. Ensure every critical claim has verifiable evidence with confidence levels. Each fact must have source + confidence percentage. If confidence <85%, enter Loop150 to find more sources. Use for critical decisions, factual claims, legal/compliance work, or any situation where unverified claims are dangerous.
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。
aibot88/sec_skill_store☆ 42026年5月27日 更新
Cloudflare-first platform selection. Decision trees for Workers, D1, R2, KV, DO, Queues, Vectorize, Containers, Sandboxes, Flagship, Agent Memory, Workflows v2. Default stack, override conditions, auth, data patterns, reliability.
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。
aibot88/sec_skill_store☆ 42026年5月27日 更新
Draft or review an employer's Otameshi Tenshoku listing so a candidate can decide whether to apply: check item by item that the work, deliverable, out-of-scope tasks, required experience, support, hours, schedule, location, compensation, contract form and payment timing are stated, find open-ended, unpaid-company-work, guarantee, personal-attribute and unverifiable wording, and confirm that the hours, weekly availability, period, rate and budget hold together. Use when a hiring manager, recruiter or small-business owner wants a listing written from a plan, wants an existing draft or published listing reviewed before or after posting, or wants to know why applications are not coming; do not use to design the work itself (trial-work-design), to reply to applicants (applicant-reply), to state market rates, legal, tax or service conditions from memory, or to post or update the listing on the user's behalf.
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。
ficilcom/otame4-work-skills☆ 42026年9月23日 更新