「dependency graph」の検索結果

Use when the user asks to create, convert, style, animate, export, share, or deploy HTML slide decks, presentations, pitch decks, talks, or keynotes; including zero-dependency or single-file output, PowerPoint/PPT/PPTX input, PDF export, browser delivery, visual style exploration, or "loopy slides". Not for general web pages or landing pages.

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Modular design, interface stability, dependency management, AI-code health. Triggers: architecture, design, structure, modules, dependencies, coupling, system design.

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Diagnosis before prescription: reproduce, hypothesize, isolate, fix root cause, add a regression test; refactor mode maps deps, coupling and blast radius. Triggers: bug, error, fix, broken, not working, crashed, stack trace, exception, refactor, restructure, coupling, dependency map.

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Execute a work unit end-to-end: sequence tasks by dependency, implement, test between tasks, commit, and track progress. Use to deliver a complete feature in one session. Invoked as /agiflow:run-work <work-unit>. Uses get_work_unit, list_tasks, update_task, get_work_unit_progress.

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INVOKE THIS SKILL when setting up a new project or when asked about package versions, installation, or dependency management for LangChain, LangGraph, LangSmith, or Deep Agents. Covers required packages, minimum versions, environment requirements, versioning best practices, and common community tool packages for both Python and TypeScript.

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Analyze Swift Package Manager dependencies, package plugins, module variants, and CI-oriented build overhead that slow Xcode builds. Use when a developer suspects packages, plugins, or dependency graph shape are hurting clean or incremental build performance, mentions SPM slowness, package resolution time, build plugin overhead, duplicate module builds from configuration drift, circular dependencies between modules, oversized modules needing splitting, or modularization best practices.

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Multi-agent DAG orchestration for workflows where AI agents collaborate via dependency graphs, covering agent spawning, output merging, and quality evaluation. Use when a task needs multiple specialized agents or to parallelize AI work.

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Configure Hilt dependency injection with proper scoping, modules, and constructor injection in Android. Use when changing the production Hilt graph; defer test overrides, Room wiring, navigation-specific scopes, and generic DI explanations to their specific skills.

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Inspect and update NuGet package families using ManagedCode NuGetPackageManager, the nuget-manager CLI, or its VS Code extension. USE FOR: checking outdated packages; dry-run family reviews; applying reviewed literal package and explicit project SDK version updates. DO NOT USE FOR: adding/removing packages; dependency graph resolution; authenticated private-feed setup. INVOKES: inspect declarations, preview and review exact versions, apply authorized changes, then restore and run focused regressions.

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Track complex, multi-session work with dependency graphs using bd (beads) issue tracker. Use when work spans multiple sessions, has complex dependencies, or requires persistent context across compaction cycles. For simple single-session linear tasks, TodoWrite remains appropriate.

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Uses 1M context window to ingest an entire codebase and output a file-by-file migration plan. Supports JS to TS, React class to hooks, framework migrations, and more. Generates migration-plan.md with file inventory, dependency graph, migration order, file-by-file changes, estimated effort, and risk assessment.

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Code intelligence over repositories Guaardvark has indexed: semantic code search, exact-text search, file listing, PageRank repository map, import dependency graph, reading one class or function, analysis and generated rewrites, plus the System Mapper and self-improvement status. Use when the user asks where something is implemented, how modules depend on each other, or wants an architecture map of an indexed repo.

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End-to-end, phase-gated SDLC for AI coding agents: relentless design interview → reviewed spec → dependency-graphed task board → safe-wave TDD execution → verification → converge. Handles features, bugs, refactors, greenfield projects, planning breakdowns, and migrations. Triggers on "absolute work", "build this end-to-end", "plan and build", "break this into tasks", "grill me on this plan", "pick up this ticket", "run this migration".

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Revenue decomposition, segment breakdown, geographic revenue split, product-line waterfall, revenue mix analysis, business segment performance, divisional revenue, revenue concentration, customer revenue dependency, channel revenue analysis

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Supply chain mapping, supplier dependency analysis, customer concentration, geographic concentration, bottleneck identification, supply chain risk, logistics network, sourcing strategy, inventory management, vertical integration analysis

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Design and operate bounded multi-agent workflows with task decomposition, dependency graphs, ownership, handoff contracts, shared-state controls, approvals, recovery, and synthesis. Use when a task contains genuinely independent workstreams, specialized roles, parallel research or implementation, reviewer-worker loops, or coordination problems that one agent should not execute sequentially.

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Implement consumer-driven contract testing with Pact-JS (v16). Covers consumer test writing, broker-driven provider verification, Pact Broker setup, can-i-deploy as a deployment gate, webhook-triggered verification, pending pacts, and schema-first vs consumer-first approaches (OpenAPI/Ajv, Schemathesis). Use when: "contract test," "Pact," "consumer-driven," "API contract," "provider verification," "can-i-deploy." Not for: stubbing or mocking a dependency to isolate a test — use service-virtualization; general REST/GraphQL endpoint assertions against your own API — use api-testing. Related: api-testing, service-virtualization, ci-cd-integration, test-environments.

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Runs multi-agent waves across subagents, teams, and workflows. Use when fanning out over a dependency graph, adding verifier or voting passes, or loop-until-dry Loop Engineering.

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 guide for type-safe Python AI agent development with Pydantic AI — dependency injection, structured outputs, model-agnostic routing, and graph workflows / Panduan ahli pengembangan agen AI Python type-safe dengan Pydantic AI.

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Decomposes a contextualized plan into atomic, independently executable tasks with complete embedded context, registered through the plan API. Use after SAM Stage 3 Context Integration produces the contextualized plan artifact — when the plan is ready for task generation with CLEAR ordering, CoVe checks, and dependency graphs for parallel execution.

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Turn a one-line objective into a step-by-step construction plan for multi-session, multi-agent engineering projects. Each step has a self-contained context brief so a fresh agent can execute it cold. Includes adversarial review gate, dependency graph, parallel step detection, anti-pattern catalog, and plan mutation protocol. TRIGGER when: user requests a plan, blueprint, or roadmap for a complex multi-PR task, or describes work that needs multiple sessions. DO NOT TRIGGER when: task is completable in a single PR or fewer than 3 tool calls, or user says "just do it".

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 query architecture-aware code search, dependency graphs, call flows, impact analysis, and belief maps for Python, TypeScript, TSX, Rust, C#, Java, Go, Ruby, and Ruby on Rails repositories. Use before non-trivial code changes to find module boundaries, blast radius, architecture violations, and the minimal source files to read.

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。

Visualize system dependencies and relationships

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AI-powered code vulnerability analysis and dependency scanning using Gemini CLI security extension patterns. Detects hardcoded secrets, injection attacks, weak cryptography, authentication flaws, and LLM prompt injection. Also scans dependencies against the OSV.dev vulnerability 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。