2,450 件 ・ 関連度順
概要と使いどころ
Corporate finance fundamentals for evaluating business decisions under cost, time, and risk constraints. Covers time value of money, net present value, internal rate of return, payback period, break-even analysis, cost-benefit analysis, debt vs equity, working capital, and the basic financial statements. Use when evaluating an investment, sizing a funding round, structuring a capital stack, or reading a balance sheet.
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。
Systems programming from memory management through networking. Covers memory models (stack vs heap, manual allocation, garbage collection, ownership/borrowing), concurrency (threads, mutexes, channels, async/await, actor model, data races vs race conditions), operating system concepts (processes, virtual memory, page tables, system calls, file descriptors, signals), compilation (lexing, parsing, code generation, linking, static vs dynamic libraries), networking fundamentals (TCP/IP, sockets, HTTP, DNS, TLS), and the hardware-software boundary (caches, cache lines, false sharing, memory-mapped I/O). Use when working with low-level code, diagnosing system-level bugs, understanding performance characteristics, or bridging between high-level languages and machine 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。
Guidance for writing OpenStack operations manual procedures following NASA document standards. Use when authoring per-service procedures, maintenance workflows, or operational documentation that must be verified against running 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。
Guidance for generating OpenStack runbooks with dual task/symptom indexing following NASA procedure standards. Use when creating incident response procedures, operational runbooks, or troubleshooting guides that must be verified against running 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。
NASA Systems Engineering methodology mapped to cloud operations. Use when planning, executing, verifying, or documenting OpenStack cloud infrastructure following NASA SP-6105 and NPR 7123.1 processes. Provides phase gate criteria, document templates, and cross-references for all 7 SE lifecycle phases applied to cloud deployment and operations.
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。
Guidance for verifying documentation accuracy against running OpenStack infrastructure. Use when detecting documentation drift, validating operations procedures against live systems, or confirming that runbooks produce expected results on the deployed cloud. Implements NASA Product Verification (SP-6105 SS 5.3) applied to documentation artifacts.
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。
Prisma Compute deployment and hosting guide. Use whenever the user mentions Prisma Compute, `prisma.compute.ts`, `defineComputeConfig`, deploying or hosting a Prisma app, `@prisma/cli app deploy`, `compute:deploy`, `create-prisma --deploy`, `PRISMA_SERVICE_TOKEN`, Compute auth/workspaces, apps/deployments/build logs/domains, localhost vs `0.0.0.0`, deploy port binding, or framework deploy readiness for Hono, Elysia, Next.js, TanStack Start, Astro, Nuxt, Svelte, Nest, Turborepo, or custom/prebuilt artifacts.
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。
Scaffold high-fidelity UI component mockups using the project UI stack consuming approved wireframes. Use when implementing approved wireframes.
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。
USB and CAN connectivity for Zephyr RTOS. Covers USB device stack configuration (CDC ACM, HID, MSC), CAN controller integration, and professional USB-to-CAN adapter patterns including buffering and protocol packetization. Trigger when adding USB interfaces, implementing CAN bus communication, or building hardware diagnostic tools.
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。
Testing and debugging for Zephyr RTOS. Covers the Ztest framework for unit and integration tests, the Twister test runner for automated HIL/SIM validation, and advanced tracing/debugging techniques (SystemView, Thread Analyzer, Stack analysis). Trigger when writing unit tests, setting up CI/CD pipelines, or analyzing system performance and latency.
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。
IP networking fundamentals for Zephyr RTOS. Covers IoT protocol selection (LwM2M, CoAP, MQTT), IP stack configuration and trimming (IPv4/IPv6, UDP/TCP), and professional SDK integration as Zephyr modules using West manifests. Trigger when building cloud-connected applications, optimizing network memory usage, or integrating external cloud SDKs.
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 pull request creation and review, including posting findings, controlling scope, readying work for review, and splitting work into a stacked-PR chain.
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。
Scholze-Clausen condensed mathematics bridge to sheaf neural networks via 6-functor formalism
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。
Forth/Factor/Joy: stack-based concatenative programming where composition replaces application.
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。
Interleave layer bridging the BigQuery cluster to plurigrid/asi. Routes BigQuery queries through asi's DuckDB stack, wires patent search into asi knowledge graph, connects Looker Studio dashboards to CatColab, and feeds BigQuery ML into the lolita physics emulation pipeline.
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。
Run and interpret engine-stack benchmarks (Steel, ember, shale)
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。
Use when building terminal UI apps with the Textual framework — creating widgets, screens, layouts, handling events, managing reactive attributes, testing with Pilot, snapshot testing with pytest-textual-snapshot, or running background workers. Covers App lifecycle, CSS styling, screen stack, custom messages, actions, bindings, and the Worker API.
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 creating or updating the project skill discovery config — generates or regenerates .dh/skill_discovery.yaml by scanning the repo to infer tech stack, inventorying installed skills via npx skills list, loading candidate skill content before suggesting, and writing a config-driven skill injection file. Triggers on /dh:setup-skill-discovery invocations and programmatic --auto calls from add-new-feature Phase 3.
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。
Opt-in recreation of the Bun + TypeScript framework used to build Vapi's landing-page voice agents from a ROUGH_DRAFT.md spec, including a scenario registry, language stacks, prompt composer, assistant builder, and idempotent bootstrap script. Use only when the user explicitly invokes vapi-bootstrap-framework or specifically asks to reproduce or experiment with the landing-page-agent architecture. Do not use for ordinary Vapi assistant builds; use create-assistant, create-tool, create-squad, and vapi-prompt-builder instead. Targets Bun + TypeScript + @vapi-ai/server-sdk.
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。
Researches one B2B SaaS competitor across 13 dimensions (company, product, ICP, pricing, reviews, content, launches, search and AI visibility, tech stack, hiring, go-to-market, social, paid ads) and writes a sourced dossier with an executive summary, a threat level, strengths and weaknesses, and a data gaps section. Run it once per competitor, then compare. Triggers: "competitor analysis", "competitor research on", "what is [competitor] doing", "compare us to", "competitive landscape", "battlecard prep". Run after /company-context and /icp-research. Not for researching your own company (use /company-context).
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。
Researches a company from its website and public sources and writes a company profile: what it sells, who buys it, traction, funding, team, hiring, tech stack, key people, red flags and the gaps you still need to fill, every claim tagged with a source and a confidence level. Use it as the first file in a marketing context folder, or to prep a discovery call or qualify a prospect. Triggers: "company research", "company background", "company context for", "qualify this prospect", "discovery call prep", "account brief", or a company URL handed over for research. Not for competitor analysis (use /competitor-research) or customer profiles (use /icp-research).
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 user wants to create a new CLAUDE.md from scratch for a repository, bootstrap project memory for a new repo, set up a repo for Claude Code for the first time, or completely rewrite an existing CLAUDE.md deemed unsalvageable. Produces a lean, high-signal file under 80 lines (120 hard cap) by scanning the repo for mechanical content (stack, commands, structure) and interviewing the user for judgment content (WHY, gotchas, out-of-scope, approval gates, testing philosophy, external docs like Confluence pages). NOT for auditing or incrementally improving an existing CLAUDE.md — that is the claude-md-improver skill's role.
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。
Query and manage Sentry error-tracking data — list and search issues, drill into events and stack traces, inspect projects and releases, resolve/ignore issues, and pull stats. Use this whenever the user mentions a Sentry issue, crash, error group, or exception; pastes a sentry.io or self-hosted Sentry URL; asks "why is this erroring", "how many times has this happened", "what's the top error in {project}", "resolve this issue", or wants a Sentry-based digest — even if they don't say "API". Always start from this skill when interacting with this service — its bundled scripts and recipes are the fastest path.
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。