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「chain」の検索結果

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概要と使いどころ

Explain why counterexamples violate specifications by analyzing formal specifications (temporal logic, invariants, pre/postconditions, code contracts), informal requirements (user stories, acceptance criteria), test specifications (assertions, property-based tests), and providing step-by-step traces showing state changes, comparing expected vs actual behavior, identifying root causes, and assessing violation impact. Use when debugging test failures, understanding model checker output, explaining runtime assertion violations, analyzing static analysis warnings, or teaching specification concepts. Produces structured markdown explanations with traces, comparisons, state diagrams, and cause chains. Triggers when users ask why something failed, explain a violation, understand a counterexample, debug a specification, or analyze why a test fails.

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。

ArabelaTso/Skills-4-SE2552026年8月21日 更新

Analyze CVE reachability in software repositories by examining how vulnerable dependencies are imported and used. Determines whether vulnerable components, classes, or functions are reachable from project code through call chain analysis, reflection detection, dynamic loading patterns, and configuration-gated behavior. Classifies each CVE as likely reachable, possibly reachable, or likely unreachable with supporting evidence. Use when analyzing security vulnerabilities in dependencies, performing post-disclosure CVE triage, assessing vulnerability impact, or when users ask to analyze CVE reachability, check if vulnerabilities are exploitable, or evaluate dependency security risks.

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。

ArabelaTso/Skills-4-SE2552026年8月21日 更新

Debug LangChain and LangGraph agents by fetching execution traces from LangSmith Studio. Use when debugging agent behavior, investigating errors, analyzing tool calls, checking memory operations, or examining agent performance. Automatically fetches recent traces and analyzes execution patterns. Requires langsmith-fetch CLI installed.

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。

w95/awesome-claude-corporate-skills2542026年2月27日 更新

Create Standard Operating Procedures with step-by-step workflows, RACI matrices, process maps, approval chains, and version control

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。

w95/awesome-claude-corporate-skills2542026年2月27日 更新

孙宇晨(Justin Sun / 孙割)的思维框架与行为逻辑。基于6个维度(著作、深度采访、表达DNA、 他者视角、决策记录、时间线)共1500+行调研素材的深度蒸馏, 提炼6个核心心智模型、8条决策启发式、5种割味造句公式和完整的表达DNA。 用途:作为思维顾问,用孙宇晨的视角分析营销策略、注意力经济、危机公关、叙事操控、蹭热点方法论。 割味十足的角色扮演——自大、暴论、碰瓷、数字轰炸、成功学底色。 当用户提到「用孙宇晨的视角」「孙割会怎么做」「孙宇晨模式」「Justin Sun perspective」「割神」时使用。 即使用户只是说「帮我用孙割的角度想想」「如果孙宇晨会怎么操作」「切换到割神」「用割的视角」也应触发。

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。

alchaincyf/sun-yuchen-perspective2492026年10月13日 更新

surrealdb

無料

Expert SurrealDB 3 architect and developer skill (tracks v3.1.4+). SurrealQL mastery, multi-model data modeling (document, graph, vector, time-series, geospatial), schema design, security, deployment, performance tuning, SDK integration (JS, Python, Go, Rust, Java, .NET, C, PHP, Swift, Kotlin, Ruby), Surrealism WASM extensions, SurrealML scope coverage (preview), built-in MCP (v3.1+) and standalone SurrealMCP, LangChain Python integration, editor tooling (LSP v0.1.6, tree-sitter, CodeMirror v1.0.6, VS Code/JetBrains/Neovim/Zed), and ecosystem integrations (Surrealist v3.9, Surreal-Sync, SurrealFS, SurrealKit v0.7, n8n, Agent Skills, setup-surreal). Universal skill for 30+ AI agents.

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。

skillmds/skillmd2482026年10月14日 更新

Production-grade Python and AI engineering standards. Use for ANY Python work — FastAPI services, LangChain/LangGraph agents, RAG pipelines, LLM API integrations, document processing, or data scripts — whether writing new code, reviewing, refactoring, or debugging. Enforces typed Pydantic boundaries, structured errors and logging, LLM call timeouts/retries/output validation, secret management, async correctness, and tested code.

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。

skillmds/skillmd2482026年10月14日 更新

server

無料

Imported skill server from langchain

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。

skillmds/skillmd2482026年10月14日 更新

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.

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。

skillmds/skillmd2482026年10月14日 更新

Use when user asks to create/generate/edit/modify a BPM workflow, design a Flowable BPMN process, or says "创建流程", "生成流程", "新建流程", "设计流程", "画流程", "审批流程", "工作流", "BPM", "BPMN", "create flow", "create process", "new workflow", "generate workflow". Also triggers when user describes an approval chain like "先经理审批再HR审批" or mentions process nodes like "开始→审批→网关→结束". Also triggers for OA application creation: "创建OA应用", "创建审批单", "创建报销单", "创建请假单", "做一个OA表单带流程", "一键创建表单和流程", "create OA app", "create approval form with workflow". Also triggers for ANY operation on existing processes: "编辑流程", "修改流程", "删除监听器", "添加监听器", "删除节点", "添加节点", "修改节点", "配置节点", "流程中的", "edit process", "modify process", "delete listener", "add listener", "remove listener", "add node", "delete node", "configure node". Key rule: whenever user mentions a specific process name (like "网关测试") with any modification intent, this skill MUST be invoked FIRST before any manual API exploration.

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。

jeecgboot/skills2412026年10月15日 更新

Use when the user wants an end-to-end AI4S research pipeline — broad direction or specific topic in, full research package out (exploration + literature survey + experiment + paper). Meta-skill that chains the four downstream skills in order. Pure markdown, no Python runtime.

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。

ai4s-research/ai4s-skills2392026年7月28日 更新

Launch and drive an isolated World of Warcraft 1.12.1 client against a local Thistle Tea server, including keyboard input, left/right clicks, camera drags, screenshots, real instance entry, log observation, and Tidewave runtime probes. Use for real-client gameplay acceptance, client-sensitive protocol or movement verification, reproducing live bugs, checking visibility and instance isolation, or validating a scripted content chain beyond automated tests.

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。

pikdum/thistle_tea2382026年10月11日 更新

Claude Code Bash tool patterns with hooks, automation, git workflows. Use for PreToolUse hooks, command chaining, CLI orchestration, custom commands, or encountering bash permissions, command failures, security guards, hook configurations.

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。

secondsky/claude-skills2302026年9月28日 更新

Lightweight standalone design spec for AI coding agents: codebase scan → bounded clarify pass (3–5 questions, not a grill) → reviewed design doc written to docs/plans/ → independent scored review → stop. No task board, no build. Use when you want a spec to discuss, hand off, or review before committing to implementation. Chains into absolute-work when ready to build. Triggers on "absolute spec", "write a spec", "spec out this feature", "draft a design doc", "I want a spec to hand off / review, don't build it yet".

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。

maddhruv/absolute2212026年7月6日 更新

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.

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。

seb1n/awesome-ai-agent-skills2172026年8月10日 更新

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.

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。

seb1n/awesome-ai-agent-skills2172026年8月10日 更新

Think and work like an expert Mining Geologist. Use when a task calls for Mining Geologist judgment. Reasons from deposit-type models (porphyry, VMS, SEDEX, orogenic Au, IOCG, skarn) through oriented core logging, domaining, variography, OK/MIK/LUC estimation, and Chain-of-Mining reconciliation to JORC Table 1, NI 43-101 Item 14, and CIM MRMR reporting; uses Leapfrog, GIM Suite/MX Deposit, and Parker F-series factors while treating support/compositing errors, batch QA/QC failures, OK smoothing bias, and Inferred-overclaim as first-class failure modes.

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。

K-Dense-AI/scientific-agents2132026年10月3日 更新

Think and work like an expert Instrumentation Engineer. Use when a task calls for Instrumentation Engineer judgment. Reasons from the process variable, its transduction chain, 4-20 mA loop integrity, and traceable calibration through ISA-5.1 P&IDs, NAMUR NE43 fault bands, ISO 5167 orifice sizing, and GUM uncertainty budgets while treating plugged impulse lines, double-applied square-root, ground-loop noise, and unrevalidated SIS bypasses as first-class failure modes.

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。

K-Dense-AI/scientific-agents2132026年10月3日 更新

Think and work like an expert Histologist. Use when a task calls for Histologist judgment. Reasons from fixation-through-stain pre-analytical chain (NBF, grossing, processing, embedding orientation, microtomy); validates H&E pH/QC, CAP IHC (90% concordance, predictive scoring systems), RNAscope controls, WSI (60-case validation), and treats floaters, autolysis, crush, ice-crystal, and decalcification artifacts as first-class failure modes.

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。

K-Dense-AI/scientific-agents2132026年10月3日 更新

Think and work like an expert Geotechnical Scientist. Use when a task calls for Geotechnical Scientist judgment. Reasons from Terzaghi effective stress, Mohr–Coulomb/CSSM, and consolidation/seepage through SPT/CPTU (Robertson SBT), triaxial/oedometer (ASTM D-series), Boulanger–Idriss liquefaction, Hoek–Brown/GSI rock mass, EC7 characteristic values, and PLAXIS/Slide2/RS2/GeoStudio workflows while treating sample disturbance, N-value correction chains, spatial variability, and LEM-vs-FEM mismatch as first-class failure modes.

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。

K-Dense-AI/scientific-agents2132026年10月3日 更新

Think and work like an expert Forensic Chemist. Use when a task calls for Forensic Chemist judgment. Reasons from chain of custody, validated methods, measurement uncertainty, and class-versus-individual characteristics through GC-MS, LC-MS/MS, FTIR, and SWGDRUG-aligned identification under ISO/IEC 17025, while treating carryover contamination, secondary transfer, isomer co-elution, and upgrading equivocal results into source attribution as first-class failure modes.

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。

K-Dense-AI/scientific-agents2132026年10月3日 更新

Think and work like an expert Electronics Engineer. Use when a task calls for Electronics Engineer judgment. Reasons from datasheet-corner device physics, signal-chain error budgets, and analog-digital return-path coupling through LTspice/IBIS-AMI simulation, ICT/boundary-scan coverage, golden-board signature comparison, and IPC/AEC-Q standards while treating ESD versus EOS overstress, MLCC DC-bias derating, reference and clock-jitter ENOB loss, and NFF field returns as first-class failure modes.

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。

K-Dense-AI/scientific-agents2132026年10月3日 更新

Think and work like an expert Environmental Scientist. Use when a task calls for Environmental Scientist judgment. Reasons from source-pathway-receptor linkages, multimedia partitioning, and dose-as-exposure through conceptual site models, fate models (MODFLOW/MT3DMS, AERMOD), SW-846 QA/QC chains, and ProUCL/Mann-Kendall statistics while treating censoring bias, conceptual-model error, well-construction artifacts, and seasonal confounding as first-class failure modes.

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。

K-Dense-AI/scientific-agents2132026年10月3日 更新

Think and work like an expert Environmental Health Scientist. Use when a task calls for Environmental Health Scientist judgment. Reasons from source–pathway–receptor chains, classical vs Berkson exposure error, and tiered biomonitoring (NHANES/BEs); runs STROBE-grade epi, IRIS/OEHHA/ATSDR risk assessment, AERMOD/CALPUFF, EPHT/EJSCREEN, and HIA while treating surrogate misclassification, mobility bias, and detection≠harm as first-class failure modes.

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。

K-Dense-AI/scientific-agents2132026年10月3日 更新