「structures」の検索結果

Expert en algorithmes (Big-O, dynamic programming, graphs, sorting, LeetCode patterns)

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。

Expert en gestion de programmes d'affiliation (recruitment, commission structures, tracking, fraud prevention)

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。

Build comprehensive 3-5 year financial models with revenue projections, cost structures, cash flow analysis, and scenario planning for early-stage startups.

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。

Scaffolds new agent skills for the dotnet/skills repository. Use when creating a new skill, generating SKILL.md files, or setting up skill directory structures. Handles frontmatter generation, section templates, and validation guidance.

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。

Reference for how BDB structures autonomous software engineering work — the seven-node dispatcher graph (Architect, TechLead, UI/UX, Engineering, Media/EventTech, Reviewer, Shipping) that /startcycle-graph actually runs. Use when you need the high-level lifecycle framing without inventing your own process.

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。

Convert any document into a production-ready Ozor.ai video prompt. Use this skill whenever the user uploads a document (PDF, PowerPoint, Google Doc, Markdown, text file, or URL) and wants to turn it into a video. Also trigger when the user mentions 'turn this doc into a video', 'make a video from this PDF', 'video from my pitch deck', 'convert this presentation to video', 'video from this blog post', 'video from these release notes', 'create a video from this document', 'turn this into an explainer', or any request to transform written content into video format using Ozor. This skill reads the document, extracts the core message, structures it into scenes, and outputs a complete prompt the user can paste directly into Ozor.ai.

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。

Strips AI fingerprints from written text by enforcing banned vocabulary, forbidden sentence structures, and authentic prose patterns. Use when writing articles, documentation, social content, emails, or any text that should read as human-authored rather than AI-generated.

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。

Intelligently organizes files and folders by understanding context, finding duplicates, and suggesting better organizational structures. Use when user wants to clean up directories, organize downloads, remove duplicates, or restructure projects.

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。

Enforces Conventional Commits format for git commit messages. Use when formatting commits or validating message structures. For git commands, see git-control.

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。

Detects rootkit presence on compromised systems by identifying hidden processes, hooked system calls, modified kernel structures, hidden files, and covert network connections using memory forensics, cross-view detection, and integrity checking techniques. Activates for requests involving rootkit detection, hidden process discovery, kernel integrity checking, or system call hook analysis.

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 kernel-level rootkits in Linux memory dumps using Volatility3 linux plugins (check_syscall, lsmod, hidden_modules), rkhunter system scanning, and /proc vs /sys discrepancy analysis to identify hooked syscalls, hidden kernel modules, and tampered system 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。

Analyzes malware command-and-control (C2) communication protocols to understand beacon patterns, command structures, data encoding, and infrastructure. Covers HTTP, HTTPS, DNS, and custom protocol C2 analysis for detection development and threat intelligence. Activates for requests involving C2 analysis, beacon detection, C2 protocol reverse engineering, or command-and-control infrastructure mapping.

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Detect PowerShell Empire framework artifacts in Windows event logs by identifying Base64 encoded launcher patterns, default user agents, staging URL structures, stager IOCs, and known Empire module signatures in Script Block Logging events.

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Intelligently organizes your files and folders across your computer by understanding context, finding duplicates, suggesting better structures, and automating cleanup tasks. Reduces cognitive load and keeps your digital workspace tidy without manual effort.

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。

Algorithm design and analysis skill for DSH; use when working on optimization, data structures, or computational procedures.

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Create exercise directory structures with sections, problems, solutions, and explainers that pass linting. Use when user wants to scaffold exercises, create exercise stubs, or set up a new course section.

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Structures biological experiments so inference is valid by construction, covering Fisher's principles (randomization, replication, local control), the experimental-vs-observational unit distinction and pseudoreplication (Hurlbert 1984; Lazic 2018), randomization mechanics (complete, restricted, stratified, rerandomization, run-order), blocking layouts (randomized complete block, Latin square, incomplete block), factorial designs and interactions, and the split-plot/nested error strata hidden inside multi-batch genomics. Use when deciding the experimental unit and what counts as a replicate, planning randomization and run order, choosing a blocked/factorial/split-plot/nested layout, avoiding pseudoreplication in cell-culture or animal studies, or specifying the random-effects structure of the analysis model. For assigning samples to sequencing batches/lanes/plates and batch-effect correction see experimental-design/batch-design; for regulated clinical-trial randomization see clinical-biostatistics.

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Materials science toolkit. Crystal structures (CIF, POSCAR), phase diagrams, band structure, DOS, Materials Project integration, format conversion, for computational materials science.

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Inorganic chemistry, physical chemistry, and materials science — crystal structures, coordination chemistry, lattice parameters, thermodynamic properties, electronic structure. Use for unit cell volume calculations, coordination geometry, materials property estimation, and inorganic-mechanism reasoning. Complementary to tooluniverse-organic-chemistry.

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 structure prediction from sequence. ESMFold-based, single GPU, no MSA needed. Predicts 3D structures with pLDDT confidence scores for drug discovery targets.

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Experimental data: discover and acquire raw or near-raw measurements, sample-level observations, and reusable datasets from sequencing, transcriptomics, single-cell, perturbation, proteomics, metabolomics, genomics, and structural-biology repositories. Use for study or sample metadata, matrices, reads, spectra, experimental structures, and downloadable research artifacts—not curated database assertions or reference marker collections.

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Biological knowledge: query curated assertions and reference databases for genes, variants, pathways, regulation, cell types, reference atlases, perturbation-derived relationships, phenotypes, therapeutic targets, drugs, interactions, annotations, signatures, compounds, and predicted structures. Use for identifiers, marker sets, gene sets, associations, ontologies, database facts, and cross-database coverage.

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Standardizes molecular structures using ChEMBL chembl_structure_pipeline and RDKit rdMolStandardize covering sanitization, salt/solvent stripping, neutralization, tautomer canonicalization, stereochemistry standardization, mixture handling, and isotope normalization. Explicitly compares ChEMBL pipeline, canSARchem, and PubChem standardization choices. Use when preparing libraries for QSAR training, joining datasets across sources, deduplicating compound collections, or building canonical compound registries.

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Design agency team structures. TRIGGERS - Use when user needs help with agency-team-structure 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。