「graph」の検索結果

Summarizes Google Cloud Data Lineage graphs to help users debug data quality issues and understand data provenance for BQ/GCS. Use when summarizing upstream and downstream data flows, and presenting complex lineage data as an intuitive Markdown report. Don't use for generic BigQuery queries, editing lineage relationships, or downstream deprecation. Don't use for downstream blast-radius impact analysis (use datalineage-bigquery-asset-impact-analysis skill instead).

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Build robust backend systems — APIs (REST, GraphQL, gRPC), authentication (OAuth 2.1, JWT), databases, performance optimization, security (OWASP Top 10), scalability patterns (microservices, caching, sharding), and testing. Use when designing APIs, implementing auth, optimizing queries, handling security vulnerabilities, building microservices, or developing production-ready backend systems.

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Requirements for a Salesforce Enterprise Territory Management (ETM) territory design: alignment criteria, coverage model selection, assignment rule logic, hierarchy depth and breadth, and user-to-territory ratios. Trigger keywords: territory design, territory alignment, territory model requirements, sales coverage model, territory criteria, geographic territory, named account territory, overlay territory. NOT for ETM setup steps — use admin/enterprise-territory-management. NOT for loading territory assignment data — use data/territory-data-alignment. Trigger keywords: territory design questionnaire, territory requirements document, assignment rule matrix, territory type priority, realignment plan, territory access level decision, accountAccessLevel, opportunityAccessLevel, ObjectTerritory2Association, UserTerritory2Association, Planning state model, territory acceptance tests.

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Use when an ISV or consulting partner is creating, pricing, publishing, or maintaining an AgentExchange listing through the Partner Console — covers the listing plan, the 5-step Listing Builder workflow (Fill in the Basics, Set Pricing, Add Details, Link Your Solution, Grow Your Business), commercialization models (revenue share vs. annual fee vs. freemium), AgentExchange Checkout setup (Stripe connection, pricing plans, fees, geographic eligibility), Private Offers, Security Review gating, and post-publish listing updates. NOT for building the agent or managed package itself (use agentforce/agentforce-agent-creation and devops/second-generation-managed-packages), NOT for LMA/Trialforce licensing internals (use devops/isv-license-management-and-trialforce), and NOT for the customer-side install experience.

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Use when configuring Financial Services Cloud (FSC) financial accounts — including account types, FinancialHolding positions, FinancialGoal records, FinancialAccountRole assignments (Primary Owner, Joint Owner, Beneficiary), and household balance rollup behavior. Triggers: configure financial accounts in FSC, set up financial account roles Financial Services Cloud, FSC household balance rollup not working, add holdings to financial account, FinancialAccountType picklist setup, FSC brokerage or retirement account configuration. NOT for household group setup and rollup scheduling — use admin/household-model-configuration. NOT for the FinServ object graph and SOQL — use data/fsc-data-model.

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Detects fixed font sizes, fixed-height text containers, and Dynamic Type suppression patterns that prevent text from scaling with the user's preferred reading size, violating Apple's HIG Typography guidelines.

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สร้าง shot list + mood board + retouching plan + Lightroom preset recipe สำหรับงานถ่ายภาพ

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สร้าง Midjourney/DALL-E prompt คุณภาพสูง + design brief สำหรับโลโก้ โปสเตอร์ อินโฟกราฟิก โซเชียลมีเดีย

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Deep learning framework development with tinygrad - a minimal tensor library with autograd, JIT compilation, and multi-device support. Use when writing neural networks, training models, implementing tensor operations, working with UOps/PatternMatcher for graph transformations, or contributing to tinygrad internals. Triggers on tinygrad imports, Tensor operations, nn modules, optimizer usage, schedule/codegen work, or device backends.

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How to use the coding agents installed on this machine (claude, codex, droid, grok, vktr, hermes, opencode, copilot, pi) as plain sub-agents in orchestrated workflows: one headless invocation, one prompt in, one result out. Deliberately ignores each CLI's own orchestration features (droid --mission, codex multi_agent, hermes delegation/moa/kanban, grok --agents, opencode personas). The orchestrator is whatever session is running the skill; the CLIs are interchangeable workers. Use when the user asks "which agent should run this", "fan out workers", "run X headless", or when a lifeos skill (overnight, timeboxed-iterating, workgraph, gauntlet, bughunt, dark-factory) needs to launch an agent CLI, or when a worker needs company context it should ask Viktor for (vktr).

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 a timeframe-bounded research dossier by decomposing a topic into year orchestrators and month-level research passes that each write one condensed paragraph plus sources directly into a target file. Use when the user asks for a dossier, timeline, chronology, release history, or month-by-month research bounded by specific years or months. Not for timeless explainers or single-document summaries with no temporal scope.

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。

Apply or audit the approved Quiet Signal core visual language when the user explicitly asks for Quiet Signal styling on a defined website, interface, poster, social graphic, report, presentation, or other design artifact. This skill supplies visual identity and rule checks; it does not plan content, invent platform requirements, or run a production 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。

Number theory expert covering primes, divisibility, modular arithmetic, Diophantine equations, and cryptographic applications

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。

RESTful and GraphQL API design expert covering best practices, security, and scalability

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 in combinatorics, graph theory, discrete probability, and algorithms with applications to computer science

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 in groups, rings, fields, and algebraic structures with applications to cryptography and number theory

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Understand what SKMTC is, how its engine works, and the architectural invariants — for agents building or extending infrastructure *around* SKMTC rather than authoring generators or running the CLI. Covers the three-phase pipeline, the host/Worker boundary, cross-generator coordination, the manifest, the attribution / gen-maps (provenance) subsystem, the package graph, the dependency substrate, and the design decisions that make SKMTC behave unlike a typical codegen tool. Use this skill when the user asks "what is SKMTC", "how does the SKMTC engine work", "explain the SKMTC architecture", or is building platform infrastructure around SKMTC — a hosted generate API, a schema or generator registry, tracing or provenance tooling, a web app or SaaS that wraps the engine, or platform-level CI integration. This skill is the system mental model. It does NOT cover authoring generators (→ skmtc-generator), running CLI commands (→ skmtc-cli), or diagnosing broken runs (→ skmtc-debug).

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Use the Skmtc CLI to scaffold projects, install or clone generators from JSR, configure schema sources and enrichments, and produce code artifacts from an OpenAPI v3 or GraphQL SDL schema. Teaches the workspace mental model (`<root>/.skmtc/<project>/`, client.json, bundle, manifest) and the agent contract (strict text / strict JSON modes, exit codes, recipe errors, `agent-context` + `doctor`); the command surface itself is discovered from the binary — `skmtc --help`, `skmtc <cmd> -h` — rather than carried in this skill. Use this skill when the user asks to "run skmtc", "generate code from an OpenAPI schema", "install a skmtc generator", "scaffold a skmtc project", "watch a skmtc project", "configure enrichments", "publish a stack", "deploy to skmtc-hub" (the command is `publish`; there is no `deploy`), "skmtc in CI", or invokes any CLI subcommand. For *authoring* a generator package (Projections, Snippets, transform functions), defer to `skmtc-generator`. When something is broken (no output, wrong output, error messages, a failed bundle build), verify before proposing a fix: read the manifest and the parse issues, and reproduce the failure first.

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。

Design thinking and decision-making for web UI. Use when designing components, layouts, color schemes, typography, or creating aesthetic interfaces. Teaches principles, not fixed values.

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。

API design principles and decision-making. REST vs GraphQL vs tRPC selection, response formats, versioning, pagination.

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 publication-grade scientific figure images or prompts, including graphical abstracts, mechanism diagrams, anatomy diagrams, surgical step figures, experimental workflows, pathology mechanisms, device principles, and structured paper illustrations. If the user asks to generate the image, use ChatGPT image generation.

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。

Master the `agentbrain` CLI (npm package `agentbrain-cli`). Use this skill whenever the user runs, installs, configures, or asks about `agentbrain` commands; when they interact with the AgentBrain enterprise data hub — organizations, connectors, knowledge bases, workflows, media/documents, knowledge graph, permissions, LLM/prompt config, cost & usage, governance, audit logs; when uploading documents/files to AgentBrain (`agentbrain media upload`); when authenticating (login/logout/token/apiKey); when retrieving MCP context (`agentbrain retrieve-context`); when running SQL against a connector; when the user pastes any `agentbrain …` command; when they edit `~/.agentbrain/config.json`; or when they mention AgentBrain, agent-brain, agentbrain-cli, or the `.agentbrain` folder.

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。

Cold-start brownfield reconnaissance — reconstruct the WHOLE game design from an existing codebase when there is no GDD yet. Optionally accepts a free-text game description, notes, or reference links to guide and enrich the scan. Use when the user wants to build the game design from the code, e.g. "create a GDD from this codebase", "reconstruct the design from our code — it's a roguelike deck-builder", "build a design doc from the existing project, here are some refs". Scans the project (engine auto-detected), extracts a system roster + dependency graph and content-type schemas / resources / entities into one passive .unikit/gamedesign/RECON.md, tags every fact provenance:extracted-from-code, and writes a mandatory Intent Gap for what code cannot know (pillars, fantasy, the "why"). Writes nothing else and calls no skill. STRICTLY whole-project cold-start — if a GDD already exists and the user wants a specific part investigated, use /unikit-gd-explore (code lens); to author the GDD use /unikit-gd-spec.

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。

Enter explore mode for technical work — a thinking partner for {{engine_name}} code, architecture, and implementation decisions before you write any code. Use it to investigate a technical solution, design the architecture of a feature, choose between frameworks or libraries, compare implementation approaches, analyze how existing code or a system works, or deeply root-cause a complex bug without fixing it yet. The ultra mode adds adaptive artifacts (a C4 view, ADRs, a dependency graph) to the research folder — run it on "ultra research", "ultra explore" or "ultraresearch"; otherwise run the default. Trigger on "let's explore this technical solution", "how should we architect this feature", "which framework should we use", "run an ultra research on the save system", "investigate this error deeply". Research and analysis only — it never writes code. This is the CODE / engineering explorer — for GAME-DESIGN, GDD, mechanics, or balance research (no code) use /unikit-gd-explore.

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。