「dependencies」の検索結果

Scan project dependencies for known vulnerabilities, generate software bills of materials, and enforce license compliance across the software supply chain. Use when the user requests dependency scanning or provides relevant inputs for this workflow.

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Perform a broad, authorized security audit across application, infrastructure, identity, dependencies, and operations. Use when the user needs a scoped security posture review; use the SAST, DAST, dependency-scanning, or threat-modeling skill when the request is limited to one specialist activity.

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Analyze open-source license compatibility, obligations, and compliance risks across project dependencies. Use when the user requests license analysis or provides relevant inputs for this workflow.

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Audit agent skills, plugins, prompts, manifests, scripts, dependencies, and bundled assets for provenance, prompt-injection, permission, execution, exfiltration, persistence, and update risk. Use when evaluating a third-party skill before installing, enabling, updating, publishing, or distributing it; reviewing an untrusted SKILL.md, agent configuration, MCP integration, archive, or repository; comparing a package with a known-good version; or investigating unexpected tool, network, credential, or filesystem behavior.

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Use when working in a repository that uses bd or Beads for durable project task tracking, issue dependencies, blocker management, multi-session handoff, or shared work memory. Trigger when the user asks to find ready work, claim or close tasks, create follow-up work, inspect blockers, recover project context, or choose between local planning and persistent project tracking.

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iFlytek Machine Translation (机器翻译) — translate text between Chinese, English, Japanese, Korean, French, Spanish, German, Russian, Arabic, Thai, Vietnamese, and many more languages. Use when the user wants to translate text. Pure Python stdlib, no pip dependencies.

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Use when user asks to clone a voice, train a custom voice model, or synthesize speech with a cloned voice. iFlytek Voice Clone tts(声音复刻) — train a custom voice model from audio samples and synthesize speech with the cloned voice. Supports the full workflow: get training text → create task → upload audio → submit training → poll results → synthesize with cloned voice. Pure Python stdlib, no pip dependencies.

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iFlytek Official Document Proofreading (公文校对) — detect and correct errors in Chinese text including typos, punctuation, word order, factual mistakes, sensitive content, and more (27 error types). Supports up to 220,000 characters. Use when the user wants to proofread, check, or correct Chinese text, especially official documents. Pure Python stdlib, no pip dependencies.

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Use when user asks to analyze an image, describe image contents, or answer questions about a picture. iFlytek Image Understanding (图片理解) — analyze and answer questions about images using Spark Vision model. WebSocket API, pure Python stdlib, no pip dependencies.

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Think and work like an expert Cancer Biologist. Use when a task calls for Cancer Biologist judgment. Reasons from hallmark capabilities, clonal evolution, and TME context; separates driver from passenger, cell-autonomous from stromal mechanisms, and 2D artifacts from PDO/PDX-validated dependencies using TCGA, DepMap, OncoKB, and REMARK-grade biomarker logic.

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Research npm packages — download stats, dependencies, maintainers, version history, and security advisories. Useful for technology due diligence, dependency auditing, and evaluating package reliability before adoption.

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Grounded in first principles, rigorously examine and refine a user's plan, task, decision, goal, strategy, proposal, or idea through structured, progressively deeper questioning, in order to bridge the gap between the User and the Agent. Use when the user explicitly requests grilling, challenge, pressure-testing, cross-examination, red-team review, pre-mortem analysis, or a decision audit. The goal is to uncover unclear objectives, hidden assumptions, contradictions, weak evidence, missing information, overlooked constraints, dependencies, risks, trade-offs, failure modes, and misalignment between intended outcomes and likely real-world results. Begin by establishing a shared understanding of the user's actual intent, goals, constraints, and success criteria. Ask focused, high-leverage questions rather than broad or repetitive ones. Adapt each question based on previous answers, probing deeper where uncertainty, unsupported assumptions, or strategic weaknesses remain. Regularly summarize the current understanding of the user's intent and position to confirm alignment, expose misunderstandings, and refine the problem or goal definition. Distinguish facts, assumptions, hypotheses, and unknowns. Challenge reasoning rigorously while remaining constructive, respectful, and solution-oriented. Continue until the reasoning is internally coherent, evidence-aware, constraint-conscious, risk-assessed, and translated into a clearer, more actionable goal, decision, or plan. Ultimately, achieving the super-alignment.

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Extracts executable task definitions from Markdown files and runs them as shell pipelines. Parses fenced code blocks with task metadata annotations and manages dependencies between tasks.

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Multi-agent orchestration framework for autonomous AI collaboration. Use when building teams of specialized agents working together on complex tasks, when you need role-based agent collaboration with memory, or for production workflows requiring sequential/hierarchical execution. Built without LangChain dependencies for lean, fast execution.

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Security forensics for git repos, AI skills, and MCP servers. Audits dependencies, detects prompt injection, credential theft, runtime dynamism, manifest drift, known CVEs, CISA KEV (actively exploited) vulns, and 2026 attack patterns. Not for fixing vulnerabilities or pentesting.

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Transform v0.6 specifications into context-window-sized work packages (EPICs) during PRD v0.7 Build Execution. Triggers on requests to create epics, scope work, break down implementation, or when user asks "create epics", "scope work", "break down work", "context window sizing", "what to build first?", "implementation planning", "epic breakdown". Consumes API-, DBT-, FEA-, ARC-. Outputs EPIC- entries with objectives, ID references, dependencies, and context windows. Feeds v0.7 Test Planning.

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Analyze indexed codebases via graph database (neug) and vector index (zvec). Covers call graphs, dependencies, dead code, hotspots, module coupling, architecture reports, semantic search, impact analysis, bug root cause from GitHub issues, class diagrams (UML), and PR review (risk scoring, conflict detection, auto-merge candidates, labeling). Also covers creating, inspecting, and repairing a CodeScope index. Use for: code structure, who calls what, why something changed, similar functions, module boundaries, bug tracing, class relationships, PR risk/conflicts, or any question benefiting from a code knowledge graph. Applies when a `.codegraph` index exists in the workspace, or when the user wants to create one.

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DuckDB is an in-process analytical database that runs embedded inside your application with zero external dependencies. It can query CSV, Parquet, and JSON files directly without loading them into tables first, making it ideal for local data exploration, ETL pipelines, and analytical workloads where spinning up a server is overkill.

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Expert in understanding and navigating legacy codebases. Covers code archaeology techniques, finding hidden knowledge, mapping dependencies, and extracting understanding from code without documentation. Knows how to read the stories that old code tells. Use when "legacy code, old codebase, understand this code, archaeology, what does this do, no documentation, ancient code, " mentioned.

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Docker makes "works on my machine" a deployment strategy. Package your app with its dependencies, run it anywhere. But Docker's simplicity hides real complexity. A naive Dockerfile can be 10x larger and slower than it needs to be. This skill covers Dockerfile optimization, multi-stage builds, Docker Compose for development, security hardening, and production patterns. Key insight: your Dockerfile is code. Review it, optimize it, version it. 2025 lesson: Containers aren't VMs. The patterns that work for VMs (install everything, big base images) are anti-patterns for containers. Think small, think immutable, think layers. Use when "docker, dockerfile, container, docker compose, docker build, containerize, docker image, multi-stage build, docker network, docker volume, docker, containers, devops, deployment, infrastructure, security, optimization" mentioned.

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Review code architecture for maintainability, catch structural issues before they become debtUse when "Reviewing pull requests with structural changes, Planning refactoring work, Evaluating new feature architecture, Assessing technical debt, Before major releases, When code feels "hard to change", architecture, code-review, refactoring, design-patterns, technical-debt, dependencies, maintainability" mentioned.

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Audit dependencies for licensing, security, and maintenance risk. Use when a senior developer needs risk assessment.

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Guide installation of project dependencies and toolchains. Use when a junior developer is stuck setting up the environment.

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。

Generate navigable semantic maps from PDF documents. Extracts section structure via font analysis, then runs LLM extraction per section for claims, symbols, and dependencies — all page-anchored. Produces _MAP.md (progressive disclosure), .symbols.json (definition index), .anchors.json (claim references), and a _USAGE.md snippet for CLAUDE.md. Use when analyzing papers, specs, or legal docs; when asked to "map this document", "index this PDF", "what does this paper say"; or when a coding agent needs grounded reference material from a PDF source. Analogous to tree-sitting but for prose documents.

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。