「map」の検索結果

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.

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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.

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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。

Audit a MaaFramework/Maa-series project's Git history to learn how Pipeline JSON, interface options, Python AgentServer CustomAction code, and related data tables evolved. Use when asked to review a Maa project from initial commit through a target commit, map `action: Custom` pipeline nodes to `@AgentServer.custom_action(...)` implementations, find pipeline/custom/option patterns or breakages, and produce a report plus skill-improvement recommendations.

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。

Optimize for search engine visibility and ranking. Use when the task involves `improve SEO`, `optimize for search`, `fix meta tags`, `add structured data`, `sitemap optimization`, or `search engine optimization`.

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。

Maps the 18 OCapN/CapTP/E-rights patterns reinvented piecemeal across codex-rs to their canonical Spritely/Goblins equivalents and the hermes-* bridge skills that formalize each correspondence. Use when auditing capability architecture in codex-rs, planning a Hoot/Goblins port, or reasoning about which E-rights primitive a codex-rs module implicitly implements.

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。

Sonify GF(3) color streams via CatSharp scale. Maps Gay.jl colors to pitch classes and plays through sox. No voice synthesis.

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。

GitHub interactome explorer for bmorphism/plurigrid ecosystem. Maps collaborations across AlgebraicJulia, Topos Institute, Anthropic, and MCP servers. Use for discovering cobordisms between research communities.

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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.

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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。

Searches and explores Burp Suite project files (.burp) from the command line. Use when searching response headers or bodies with regex patterns, extracting security audit findings, dumping proxy history or site map data, or analyzing HTTP traffic captured in a Burp project.

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Bridge layer connecting the emerging agentic coordination protocol ecosystem (MCP, A2A, ANP, AGNTCY, AITP, LMOS, AAIF) to the plurigrid/asi skill graph. Maps protocol-level identity mechanisms (W3C DID, OAuth 2.1, IPSIE, Entra Agent ID, Agent Cards, passport.gay) into GF(3)-classified skills. The central unsolved problem — no unified identity across MCP+A2A+A2UI — is directly addressed by the bisimulation oracle composition.

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 layer connecting the Agentic AI Foundation (AAIF) governance structure — Linux Foundation stewardship of MCP, goose, AGENTS.md — to the plurigrid/asi skill graph. Formalizes AAIF-compatible skill registration, IPSIE identity profiles, and the cross-protocol interoperability surface. Maps the AAIF platinum member ecosystem (AWS, Anthropic, Block, Bloomberg, Cloudflare, Google, Microsoft, OpenAI) to specific ASI skill integration points.

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。

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.

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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。

Mermaid diagram syntax reference for all diagram types — flowchart, sequence, class, state, ER, gantt, git graph, mindmap, timeline, user journey, pie, quadrant, XY chart, block, sankey, C4, kanban, and more. Use when constructing or debugging any Mermaid diagram definition.

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 writing asyncio Python code — async/await coroutines, concurrent I/O with asyncio.gather, task creation and cancellation, semaphore rate limiting, producer-consumer queues, async context managers, async generators, WebSocket servers, aiohttp web scraping, async database operations, run_in_executor for blocking calls, or testing async code with pytest-asyncio. Covers FastAPI and aiohttp patterns, synchronization primitives, timeout handling, and common pitfalls like event loop blocking and missing await.

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 CLI applications with Typer and Rich — creating commands with Annotated parameter syntax, defining arguments and options, composing subcommands, async concurrent CLI tasks with semaphores, testing with CliRunner, PEP 723 shebang scripts, progress bars, Rich terminal output, or non-TTY display width handling.

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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。

Local codebase analysis research angle — derives behavioral contracts, coding conventions, SKILL.md flow insertion points, and agent data availability maps from actual source files. Use when the blocking question is answered by reading the repository: what does this function actually do, what pattern does the codebase use for X, where in this workflow does a new step go, or what data does this agent already have.

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。