260 skills · sorted by relevance
Use when complex problems require systematic step-by-step reasoning with ability to revise thoughts, branch into alternative approaches, or dynamically adjust scope. Ideal for multi-stage analysis, design planning, problem decomposition, or tasks with initially unclear scope.
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Use when building self-correcting retrieval systems for AI agents. Keywords: RAG, retrieval, Corrective RAG, Self-RAG, query decomposition, reranking, hallucination, grounding.
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"Build ARIMA models for time series forecasting with trend and seasonality decomposition. Use this skill when the user needs to forecast future values from historical sequential data, test for stationarity, or select ARIMA parameters — even if they say 'time series forecast', 'predict next month sales', or 'ARIMA model'.".
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EEG preprocessing and analysis — filtering, artifact removal, time-frequency decomposition, and ERP workflows.
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Build agents that work toward goals with minimal supervision — goal decomposition, self-correction loops, safe autonomy levels, and long-horizon task management. Use when an agent must run for hours or complete open-ended tasks.
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Use when a task needs the judgment of an embalmer — assessing a case's decomposition stage and preservation difficulty, calculating an arterial-fluid dilution ratio for a specific case condition, choosing a restorative-art approach for trauma or discoloration, or working through a bloodborne-pathogen exposure-control decision.
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Decompose a business problem MECE using an issue tree, generate hypotheses, and prioritize branches for investigation. Use when a consultant says "help me structure this problem", "I need to break down this issue", "build me an issue tree", "I don't know where to start on this problem", "help me frame the problem", "what are the hypotheses here", "we need to prioritize our analysis", or "the client problem is too broad". Also trigger when describing a complex or ambiguous business problem that needs systematic decomposition before analysis begins, or when an engagement is starting and a workplan needs to be structured from a problem statement.
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Use this skill when writing acceptance criteria for Salesforce features, structuring UAT test scripts from user stories, classifying defects found during UAT, or planning regression testing before a Salesforce release. Trigger keywords: UAT, user acceptance testing, test script, acceptance criteria, defect classification, regression testing, test plan, test case. NOT for Given/When/Then AC syntax — use admin/acceptance-criteria-given-when-then. NOT for the per-case field schema and AC-to-case decomposition — use admin/uat-test-case-design. NOT for automated Flow or Apex test coverage — use flow/flow-testing. Also covers: UAT plan record, sandbox selection for UAT, pre-UAT environment checklist, sandbox email deliverability for testers, test-user personas, negative and bulk coverage, defect triage categories, go/no-go sign-off, quick-deploy window.
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Configure Communications Cloud for the first time: org setup sequence, permission sets, Enterprise Product Catalog (EPC) service catalog, TM Forum-aligned order decomposition setup, Account record-type segmentation, contract lifecycle activation. NOT for designing the commercial-to-technical order process itself - use admin/industries-process-design. NOT for calling TM Forum Open APIs (TMF622, TMF679) - use omnistudio/industries-api-extensions.
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Microservices design expert covering service decomposition, communication patterns, and distributed systems
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Analyze demand forecasting systems — time-series decomposition (ARIMA, Prophet, ETS), seasonal and event-driven demand modeling, booking curve and pickup analysis, cancellation prediction, forecast accuracy metrics (MAPE, bias), and model retraining pipelines.
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Use when complex problems require systematic step-by-step reasoning with ability to revise thoughts, branch into alternative approaches, or dynamically adjust scope. Ideal for multi-stage analysis, design planning, problem decomposition, or tasks with initially unclear scope.
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Deep analysis of competitor patent claims: element decomposition, scope interpretation, equivalents prediction, prosecution history estoppel analysis. Automatically converts PDF/DOCX/DOC patent files to structured Markdown via patent-pdf-to-md skill before analysis. Invoke when user asks to analyze/parse competitor patent claims, decompose claim elements, assess claim scope, or perform 权利要求解析/要素分解/保护范围解读. 竞品专利权利要求深度解析：要素分解、保护范围解读、等同范围预判、审查历史限制分析。自动调用patent-pdf-to-md技能将PDF/DOCX/DOC专利文件转换为结构化Markdown后再进行分析。当用户要求解析竞品专利权利要求、要素分解、保护范围时触发。
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The operation-generator shape for Skmtc: one definition per (path, method), for any output family — client hooks, SDK methods, forms, route stubs, docs. Output varies wildly; the DECOMPOSITION of the operation and the peer-consumption rules do not. Core teaching: every schema that appears in your output is a reference to a model generator's definition, obtained through the insert machinery — never rendered text. Use when authoring or editing a generator whose subject is operations ("write a gen for API clients/hooks/routes", "generate an SDK/form per endpoint"). Covers the projection shape only — accumulators (many operations → one file) are out of scope. Load ALONGSIDE skmtc-generator and the emitted language's skill.
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Apply this opinionated workflow when modernizing legacy systems: strangler fig pattern, branch by abstraction, characterization tests, incremental monolith decomposition, framework upgrades, feature-flagged migration with rollback.
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Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing.
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This skill should be used when the user asks to "implement a DiD regression", "run a staggered difference-in-differences", "set up an event study", "implement IV / 2SLS", "run a regression discontinuity design", "build synthetic control", "do propensity score matching", "test parallel trends", "run Honest DiD", "wild cluster bootstrap", "Callaway-Sant'Anna", "Sun-Abraham", "Bacon decomposition", "double machine learning", "debiased machine learning", "DML / DoubleML", "cross-fitting", "causal forest", "heterogeneous treatment effects", "ATE with ML controls", "which causal design fits my data", or needs causal-inference code, diagnostics, or identification writing in Python (StatsPAI), R, or Stata. Based on Scott Cunningham's Causal Inference: The Mixtape, with runnable validated templates, ML-based causal inference patterns, and Journal of Finance applications.
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Foundational theory for process-oriented concurrency through synchronous message-passing, applicable to multi-agent coordination and parallel decomposition
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Analytical methods for decomposing expert performance into cognitive and physical task components
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Decompose DAGs into execution chains using width bounds, stratification, virtual nodes, and matching. Use when minimizing workflow lanes in dependency graphs. NOT for cyclic graphs, generic scheduling, or one-off queries with no preprocessing payoff.
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The intelligence layer of WinDAGs. Decomposes natural language tasks into Hierarchical Task DAGs (HTDAGs), matches subtasks to skills, executes waves in parallel, and dynamically expands nodes when complexity exceeds executor capability. Use for 'orchestrate', 'execute DAG', 'parallel agents', 'decompose task', 'coordinate skills'. NOT for single-skill tasks or simple linear workflows.
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Builds, validates, schedules, and dynamically modifies DAG execution graphs. Decomposes problems into nodes with dependencies, performs topological sorting, detects cycles, resolves conflicts, and schedules wave-based parallel execution. Use when designing a DAG structure, validating dependencies, planning execution order, or modifying a DAG at runtime. Activate on "build DAG", "plan workflow", "DAG dependencies", "topological sort", "schedule execution", "modify DAG", "replan". NOT for executing DAGs (use dag-runtime), validating outputs (use dag-quality), or matching skills to nodes (use dag-skills-matcher).
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Parses complex problems into DAG (Directed Acyclic Graph) execution structures. Decomposes tasks into nodes with dependencies, identifies parallelization opportunities, and creates optimal execution plans. Activate on 'build dag', 'create workflow graph', 'decompose task', 'execution graph', 'task graph'. NOT for simple linear tasks or when an existing DAG structure is provided.
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Application architecture patterns and code organization — clean architecture, hexagonal architecture, feature-based structure, dependency inversion, module boundaries, SOLID principles at module scale. Activate on "clean architecture", "hexagonal architecture", "feature-based structure", "dependency inversion", "module boundaries", "architecture pattern", "folder structure", "fat controller", "circular dependency", "IoC container". NOT for microservice decomposition (use microservices-patterns) or database schema design (use database-design-patterns).
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