「tooling」の検索結果

Before building new infrastructure or a non-trivial capability, check whether the Nuxt ecosystem, UnJS, Vite, or other OSS already solves it — within our constraints (Nuxt, OSS, self-hostable, no mandatory SaaS). Use when weighing build-vs-adopt or starting new infra/tooling.

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Catalog and selection guide for Exasol tools, extensions, connectors, integrations, and architecture patterns. Use when the primary intent is comparison, discovery, support-status research, or architecture selection rather than executing a workflow; hand operational work to the selected dedicated skill. Covers deploy, load, explore, enrich, surface, and scale capability families, including named products with no dedicated skill of their own such as Lakehouse Turbo, the Exasol MCP Server, Governed SQL / Text-to-SQL MCP Server, Agent Control Plane, Extension Manager, Terraform and Ansible tooling, and BI, ETL, and warehouse integrations such as Databricks, SAP, Kafka, Power BI, and Tableau.

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GitHub operations via `gh` CLI: issues, PRs, CI runs, code review, API queries. Use when: (1) checking PR status or CI, (2) creating/commenting on issues, (3) listing/filtering PRs or issues, (4) viewing run logs. NOT for: complex web UI interactions requiring manual browser flows (use browser tooling when available), bulk operations across many repos (script with gh api), or when gh auth is not configured.

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Audit over-engineered codebases by factoring layers into live obligations, quotienting redundant distinctions, ablating unearned surface, and normalizing survivors while preserving required behavior. Use when change latency or agent difficulty comes from frameworks, plugins, DI, codegen, task runners, config indirection, ORMs, GraphQL, monorepo/infra tooling, or web stacks, or when asked to remove layers. Produces an evidence-backed Reduction Certificate, cuts, migration phases, proof signals, and rollback. Not for one local readability cleanup (use tw-complexity-mitigator).

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Brutally honest AGENTS.md/CLAUDE.md review backed by real A/B test evidence. Not just opinions — actual proof that your rules are dead weight. Roasts instruction files for bloat, slop, and redundancy, then proves it by running evals. Use when user says "roast my agents.md", "roast my CLAUDE.md", "prove my rules are useless", "eval roast", or wants entertaining evidence-based feedback on their AI config files. Also triggers on "audit my instructions" or "are my rules helping".

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Author a completed-spec fixture — a seeded intent graph plus a believable four-phase interview transcript — for a brunch feature, so it can feed any downstream tooling that consumes a completed spec or dogfood the UI. Use when you need a real completed specification in .brunch/brunch.db for testing or demo: pick a feature from memory/SPEC.md (or a free description), choose a stress profile (coverage|clean), and produce a registered scenario seed + an interview-questions doc that match exactly.

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Knowledge base from the FAA Requirements Engineering Management Handbook (DOT/FAA/AR-08/32). Use for writing requirements for real-time, embedded control systems — the avionics/medical slant — via the Handbook's eleven recommended practices: system overview and boundary (monitored/controlled variables), operational concepts (use cases), environmental assumptions, change-tolerant functional architecture (dependency diagrams), safety-driven architecture revision (FHA/PSSA/fault tree), system modes, detailed behavior and performance requirements (ideal value + tolerance + latency), the four-variable model bridge to software requirements (MON/CON/NAT/REQ, IN'/REQ'/OUT', DO-178B mapping), subsystem allocation, and rationale. Carries the Isolette Thermostat and Flight Control System worked examples. Slanted toward control systems with sensors and actuators; thin on information-system / enterprise / agile requirements, on requirements-management tooling, and on the formal-methods internals of SCR/RSML/SpecTRM (it names them, does not re-teach them). Does not reproduce the IEEE/DO-178B/ARP standards it cites.

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Knowledge base from DOE Systems Engineering Methodology (SEM) Version 3 — the DOE SDLC for IT investments. Use for staged systems lifecycle planning through maintenance, Stage Exits, Structured Walkthroughs, In-Stage Assessments, requirements traceability, and CMM-aligned process discipline. Covers SEM3 (2002) guidance only; does not replace modern DevSecOps tooling standards, DOE O 413.3B capital-asset directives, or current CIO policy supersessions.

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Knowledge pack from 'Towards Digital Engineering: The Advent of Digital Systems Engineering' (Huang et al., Old Dominion University, arXiv:2002.11672, 2020). Use when reasoning about digital engineering, the DoD Digital Engineering Strategy and its five goals, the Authoritative Source of Truth, digitalization vs. digitization, digital augmentation, digitalized models, provenance, unique identification, and the four-level (vision/strategy/action/foundation) DSE research framework. Covers the conceptual/vision layer of the field. SCOPE LIMITS: this is a single ~28-page vision paper, not a methods handbook — it is thin on step-by-step procedures, tooling, case studies, and quantitative evaluation, and it predates 2020 so it does not cover later digital-engineering standards or recent AI/LLM advances. Use the sebok pack for established SE canon and MBSE depth.

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Knowledge base from CISA Cross-Sector Cybersecurity Performance Goals 2.0. Use for prioritized IT/OT baseline cyber goals aligned to NIST CSF 2.0 (including GOVERN), critical-infrastructure hygiene, MSP/supply-chain expectations, and board-friendly outcome language. Covers CPG 2.0 goals only; does not replace full CSF programs, sector-specific goals (SSGs), or CSET tooling detail.

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Discover and execute specialized skills from a curated marketplace catalog on GitHub, fetched on-demand and run ephemerally (no permanent installation). The catalog covers a broad and growing range — document generation (PDF/Word/Excel/PowerPoint), visual design, presentations, web app testing, animated GIFs, dev tooling (MCP scaffolding, frontend components), creative outputs (algorithmic art), technical documentation lookup (library/API/framework references), and more. Consult this skill whenever the user asks for a capability that could plausibly be covered by a community skill — the catalog is the source of truth, not this description.

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Navigate the user through reading code stop by stop to pay down understanding debt left by AI tooling. Explicit invocation only.

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。

Tutor the user step by step on a plan, implementation, codebase, or topic to pay down understanding debt left by AI tooling. Explicit invocation only.

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。

Quiz the user on a plan, implementation, or codebase to pay down understanding debt left by AI tooling. Explicit invocation only.

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Package a contribution (gist, script, fix or skill) as a product instead of a bare snippet: README with problem and result, install steps, a test, license, honest limits and a clear entry point. Use before publishing a gist, sharing a fix or releasing a slice of internal tooling. Triggers: /pack, package this properly, make it a product, ship it with quality.

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Diagnose active BIRD daemon incidents across config, runtime, environment, sockets, logs, source, and tooling. Use when BIRD fails to start or reload, crashes, loses sessions/routes, behaves differently after lint passes, or when birdcc and bird -p disagree. Collect read-only evidence first, protect routing secrets, and route isolated config edits to bird-agent or pure implementation research to bird-source-explorer.

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Use this skill whenever the user asks to inspect, audit, review, assess, or evaluate a codebase/project for maintainability, extensibility, architecture, reliability, security, testing, engineering quality, or AI-agent/prompt/tooling risks. Also use this when invoked by `/inspect`. It performs a read-only, evidence-backed, multi-agent project inspection with complete file coverage, per-dimension scoring, blunt technical findings, and explicit degraded-mode disclosure.

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Control FriendlyBet operating cost, free-tier viability, provider/API cost, GitHub Actions/Supabase/Vercel usage, documentation hygiene, company memory updates, playbook maintenance, and skill validation. Use when evaluating cost, operational burden, tooling, internal docs, company memory, or whether agent profiles should become full skills.

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Per-surface tech stack decision tables and development tooling options. Use during /create-prd-stack to present constraint-filtered options to the user.

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Vite 8.0.10 build tooling — HMR, fast builds, plugins, and optimized production assets. Use when configuring Vite, setting up React/Vue projects with Vite, or optimizing frontend build performance.

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Creates devcontainers with Claude Code, language-specific tooling (Python/Node/Rust/Go), and persistent volumes. Use when adding devcontainer support to a project, setting up isolated development environments, or configuring sandboxed Claude Code workspaces.

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Build efficient, scalable monorepos that enable code sharing, consistent tooling, and atomic changes across multiple packages and applications.

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TypeScript and JavaScript expert with deep knowledge of type-level programming, performance optimization, monorepo management, migration strategies, and modern tooling. Use PROACTIVELY for any TypeScript/JavaScript issues including complex type gymnastics, build performance, debugging, and architectural decisions.

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Dependency management, vulnerability scanning, license compliance, package updates, and monorepo tooling. Use when updating dependencies, auditing packages, managing licenses, or configuring package managers.

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。