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概要と使いどころ
Observational drawing and visual perception techniques for art education. Covers contour drawing, gesture drawing, negative space, proportion and measurement, value mapping, spatial depth cues, and the cognitive shift from symbolic to perceptual seeing. Use when teaching drawing fundamentals, analyzing observational accuracy, or developing visual literacy in any medium.
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。
OpenStack Nova compute service skill for deploying, configuring, operating, and troubleshooting cloud compute infrastructure. Covers instance lifecycle management (create/stop/start/reboot/resize/migrate/evacuate), compute scheduling with filters and weights, flavor management, live migration procedures, hypervisor management (KVM/QEMU via libvirt), placement service for resource tracking, CPU pinning, NUMA topology, huge pages, cell mapping, VNC/SPICE console access, and capacity planning. Use when deploying compute via Kolla-Ansible, managing instances, debugging scheduler failures, or planning compute capacity.
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。
Functions, categories, and information. How mathematical objects relate to each other — morphisms, entropy, signal processing.
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。
Required reference for Prisma ORM 7 SQL driver adapter work. Use when implementing or modifying adapters, adding database drivers, or touching SqlDriverAdapter, Transaction, savepoint, result mapping, or DriverAdapterError behavior. Covers current transaction lifecycle, optional savepoint hooks, original database-error preservation, and 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。
Foundational skills for Zephyr RTOS development. Covers essential Embedded C patterns (BIT, CONTAINER_OF), real-time concurrency primitives (mutexes, semaphores, spinlocks), hardware literacy (datasheet-to-DTS mapping), and defensive programming. Trigger when writing core application logic, drivers, or troubleshooting foundational 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。
Devicetree management for Zephyr RTOS. Covers syntax, bindings, overlays, Hardware Model v2 (HWMv2), and advanced node/property deletion patterns. Trigger when defining hardware topology, creating overlays, or mapping pins and peripherals.
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 and navigate DSP (Data Structure Protocol) — graph-based long-term structural memory of codebases for LLM agents. Stores entities (modules, functions), their dependencies (imports), public API (shared/exports), and reasons for every connection. Use when: (1) project has a .dsp/ directory, (2) user asks to set up DSP or bootstrap project structure, (3) creating/modifying/deleting code files in a DSP-tracked project, (4) navigating project structure, understanding dependencies, or finding modules, (5) user mentions DSP, dsp-cli, .dsp, or structure mapping.
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 an interactive account org chart, leadership map, buying committee, or manager/report neighborhood around a person. Use for decision makers, reporting-line candidates, stakeholder mapping, and warm intro paths. Skip standalone email lookup and unrelated cross-company prospecting.
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。
Botnet architecture taxonomy, detection techniques, and defensive analysis. Covers C2 topologies, DGA analysis, fast-flux detection, P2P overlay mapping, blockchain C2, and ML-based traffic fingerprinting. Defensive research for understanding and countering distributed malicious infrastructure.
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。
Primary Python tool for 40+ bioinformatics services. Preferred for multi-database workflows: UniProt, KEGG, ChEMBL, PubChem, Reactome, QuickGO. Unified API for queries, ID mapping, pathway analysis. For direct REST control, use individual database skills (uniprot-database, kegg-database).
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。
Coordinated botnet takedown methodologies — sinkholing, infrastructure mapping, legal frameworks, BGP intervention, and post-disruption analysis. Studies Operation Endgame, Badbox 2.0, and emerging blockchain C2 countermeasures. Defensive disruption for authorized law enforcement, CERT, and academic research contexts.
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。
Bridge active inference theory with robot control using K-Scale's JAX/MuJoCo stack. Use when connecting predictive coding to locomotion policies, mapping KL divergence minimization to RL training, applying mean field approximation to robotics state estimation, or implementing sim2real as inference about future observations.
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。
Same reverse-thinking method as dh:rt-ica (see dh:rt-ica or dh-glossary for the definition), but localizes any MISSING input to the affected task only, as a non-blocking information-completeness pre-pass before task decomposition and plan generation. Use when grooming backlog items, generating plans, decomposing tasks under uncertainty, or working in brownfield and refactor scenarios. Produces completeness summary (APPROVED-FOR-PLANNING, APPROVED-WITH-GAPS, or BLOCKED-FOR-PLANNING), missing input report with dependency mapping, required unblock actions, and planning annotations for downstream tasks. Use dh:rt-ica instead at the S2 implementation gate where missing inputs must halt execution.
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。
Dasel v3 selectors for Spring bean factory XML — use when querying any Spring ApplicationContext XML for bean discovery, dependency wiring, JMS destination mapping, property injection extraction, or cross-bean reference tracing. Load this skill before writing dasel selectors against Spring bean XML files (applicationContext.xml, *_beans.xml, spring-*.xml).
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 PLAN artifact from SAM Stage 2 needs contextualization against actual codebase state — grounds the design plan in reality by performing scope analysis (NEW/MODIFY/COMPLETE classification), conflict detection between plan assumptions and codebase patterns, and resource mapping to concrete file paths and integration points. Produces an updated ARTIFACT:PLAN registered via MCP with a Contextualization section appended.
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。
Integrates patterns from external sources (URLs or local files) into local skills, agents, and plugins. Use when comparing external agent definitions against local equivalents, extracting best practices from frameworks like GSD or BMAD-METHOD, enhancing local skills with external workflow patterns, or adding interoperability with external tool ecosystems. Runs a 3-phase workflow — parallel candidate mapping, contextual enhancement, and validation — with source attribution and backlog tracking for deferred enhancements.
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 a Clay table configuration into local Deepline scripts. Handles extraction (MCP or script), documentation, action mapping, script generation, and parity validation against Clay ground truth.
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。
Dynamic agent composition and management system. USE WHEN user says create custom agents, spin up custom agents, specialized agents, OR asks for agent personalities, available traits, agent voices. Handles custom agent creation, personality assignment, voice mapping, and parallel agent orchestration.
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。
Learn unfamiliar technologies used in a repo by studying how the repo actually uses them. Finds patterns, explains concepts in context, and points to examples in the codebase. Use when a repo uses frameworks, languages, or patterns you haven't worked with before. Not for mapping a repo's architecture or domain language. Use oss-explore-repo for that.
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 高教社杯/CUMCM mathematical modeling papers need lightweight literature search, citation verification, method/background references, standards or policy sources, Chinese-English source routing, DOI/BibTeX checks, or claim-to-citation mapping without fabricated sources.
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。
Automate and research the GitHub Copilot app desktop experience. Use for driving Copilot App sessions, creating isolated app experiments, mapping app UI/accessibility controls, taking app screenshots, saving PNG captures, optimizing WebP images, testing prompts without submitting, using app/session APIs, canvases, automations, macOS Accessibility, AppleScript, and safe end-to-end Copilot App workflow probes.
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 for .NET application development involving services, managers, dependency injection, Microsoft.Extensions.Hosting, dependency direction, MiniDb persistence, DTOs, Mapster mapping, shared entities/helpers, MCP tools, module workflows, solution analysis, cancellation, logging, and logic shared by CLI and Blazor.
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。
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for OAuth, OIDC, redirect flows, state or nonce handling, PKCE, token exchange, refresh logic, claim mapping, and accepted login paths. Use when the user asks to trace redirects, callback parameters, scopes, state, nonce, PKCE, refresh tokens, consent, or explain how an OAuth or OIDC chain turns into accepted identity or privilege. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.
日本語の概要は準備中です。原文の説明を表示しています。
Internal downstream skill for ctf-sandbox-orchestrator. CTF-sandbox workflow for AD CS, certificate templates, enrollment rights, EKUs, SAN controls, PKINIT, certificate mapping, and cert-based privilege paths. Use when the user asks about ESC-style abuse, certificate templates, enrollment agents, EKUs, SAN or subject controls, smartcard or PKINIT logon, CA policy, or how an issued cert turns into accepted privilege. Use only after `$ctf-sandbox-orchestrator` has already established sandbox assumptions and routed here.
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。