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Authors and edits AWS Step Functions state machines: writes Amazon States Language (ASL) in JSONata, and chooses and structures state types (Task, Choice, Map, Parallel, Pass, Wait, Succeed, Fail). Covers ASL syntax, JSONata data transformation and variables, Retry/Catch error handling, service integrations (.sync, waitForTaskToken callbacks), Distributed Map for large-scale S3/CSV processing, saga/compensation patterns, Standard vs Express workflow choice, TestState API unit testing, and migrating state machines from JSONPath to JSONata. Use when the user is building, authoring, debugging, or migrating a Step Functions state machine or ASL definition, or orchestrating multi-step workflows with branching, retries, or human-approval callbacks, even if they don't say 'Step Functions.' Do NOT use for general Lambda function code, API Gateway, EventBridge wiring, or SAM/CDK application packaging.

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Assigns GO terms, Pfam/InterPro domains, KEGG orthologs, EC numbers, and product names to predicted proteins using eggNOG-mapper (orthology), InterProScan (domain signatures), and KofamScan (KEGG), routing specialized functions to dbCAN/antiSMASH/AMRFinderPlus/SignalP. Covers the orthology-vs-domain-vs-homology paradigms, the annotation-error percolation cascade, domain-presence-is-not-function, GO IEA circularity in enrichment, evidence tiering, and bit-score/coverage thresholds. Use when adding functional annotation to predicted genes, choosing between eggNOG-mapper and InterProScan, or judging how much to trust a functional label.

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Use when writing MATLAB functions with arguments blocks — repeating arguments (arguments (Repeating)), .?ClassName property import in constructors, name-value forwarding with namedargs2cell, or migrating from inputParser or validateattributes. Also when reviewing signatures for implicit-conversion pitfalls (size reshaping, class coercion, computed defaults), or when restricting, constraining, or validating function inputs — scalar vs vector enforcement, type rejection, size checking. Also when asked to harden inputs, make a function more robust, tighten input checking, or rewrite for safer input acceptance — even without "validation" or "arguments block" wording. Triggers on: arguments block, mustBe validators, name-value arguments, varargin, inputParser, nargin/narginchk, restrict input, type checking, scalar constraint, harden inputs, defensive programming. Not for: App Designer callbacks, Simulink mask parameters, class inheritance, or runtime validation outside function or property declarations.

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Expert NIST AI Risk Management Framework (AI RMF 1.0) advisor covering all four functions: GOVERN, MAP, MEASURE, MANAGE. Use this skill whenever a user asks about NIST AI RMF, AI risk management, AI trustworthiness, GOVERN function, MAP function, MEASURE function, MANAGE function, AI RMF Playbook, AI risk profiles, responsible AI, AI bias management, AI transparency, AI explainability, AI reliability, AI safety, NIST AI 100-1, AI risk assessment, AI incident response, or alignment to EU AI Act, ISO 42001, or NIST CSF via AI RMF. Trigger even if the user doesn't say "skill" — any NIST AI RMF or AI governance risk question should use this skill.

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Expert NIST AI Risk Management Framework (AI RMF 1.0) advisor covering all four functions: GOVERN, MAP, MEASURE, MANAGE. Use this skill whenever a user asks about NIST AI RMF, AI risk management, AI trustworthiness, GOVERN function, MAP function, MEASURE function, MANAGE function, AI RMF Playbook, AI risk profiles, responsible AI, AI bias management, AI transparency, AI explainability, AI reliability, AI safety, NIST AI 100-1, AI risk assessment, AI incident response, or alignment to EU AI Act, ISO 42001, or NIST CSF via AI RMF. Trigger even if the user doesn't say "skill" — any NIST AI RMF or AI governance risk question should use this skill.

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Build, review, or migrate Azure Functions in .NET with correct execution model, isolated worker setup, bindings, DI, and Durable Functions patterns. USE FOR: working on Azure Functions in .NET; migrating from the in-process model to the isolated worker model; adding Durable Functions, bindings, or host configuration. DO NOT USE FOR: unrelated stacks; generic tasks that do not need this specific guidance. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.

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Fix ClickHouse query errors when querying AggregatingMergeTree tables that use state functions. Use when: (1) Query fails with type mismatch on AggregateFunction columns, (2) Using sum/count on columns created with sumState/countState/uniqState, (3) Creating views that JOIN with materialized views using AggregatingMergeTree, (4) Getting unexpected results from aggregate columns that show as AggregateFunction(sum, ...) type. The *State() functions store intermediate aggregate states, not final values - you must use *Merge() functions to finalize them.

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Records atomic learned behaviors with confidence scores. Project-scoped instincts are isolated per project and auto-promote to global scope at confidence threshold 0.8. Stores instincts in .claude/context/memory/instincts.jsonl

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Use when you need to query an OpenCTI instance — is this IOC already known, what entities/reports/campaigns exist for an actor — or push new intel into it — creating indicators/observables, labelling, TLP markings, relationships, or importing a STIX 2.1 bundle. Two-way integration. Commonly invoked by /ip-investigation and friends to check whether an indicator is already in your knowledge base, and by analytical skills that want to publish their findings back to OpenCTI. Reads $OPENCTI_URL and $OPENCTI_TOKEN.

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I/O + 계산 혼재 또는 Mock stub + 계산 혼재 메서드를 Functional Core / Imperative Shell(빵속빵, Impure-Pure-Impure Sandwich)로 분리. "순수 함수 분리", "I/O와 계산 나눠", "mock 없이 테스트하게", "functional core", "/segregate-functional-core" 요청 시 사용. 단, 값 반환과 부수효과 분리(CQS)만이면 /separate-query-modifier가 적합. /segregate-functional-core [commit-ref]로 호출.

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Refactor Swift code toward declarative functional programming with immutability, value types, pure functions, transformations, and closure-based APIs. Use when asked for functional refactoring or `/refactor:functional`.

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Assigns GO terms, Pfam/InterPro domains, KEGG orthologs, EC numbers, and product names to predicted proteins using eggNOG-mapper (orthology), InterProScan (domain signatures), and KofamScan (KEGG), routing specialized functions to dbCAN/antiSMASH/AMRFinderPlus/SignalP. Covers the orthology-vs-domain-vs-homology paradigms, the annotation-error percolation cascade, domain-presence-is-not-function, GO IEA circularity in enrichment, evidence tiering, and bit-score/coverage thresholds. Use when adding functional annotation to predicted genes, choosing between eggNOG-mapper and InterProScan, or judging how much to trust a functional label.

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。

Assigns GO terms, Pfam/InterPro domains, KEGG orthologs, EC numbers, and product names to predicted proteins using eggNOG-mapper (orthology), InterProScan (domain signatures), and KofamScan (KEGG), routing specialized functions to dbCAN/antiSMASH/AMRFinderPlus/SignalP. Covers the orthology-vs-domain-vs-homology paradigms, the annotation-error percolation cascade, domain-presence-is-not-function, GO IEA circularity in enrichment, evidence tiering, and bit-score/coverage thresholds. Use when adding functional annotation to predicted genes, choosing between eggNOG-mapper and InterProScan, or judging how much to trust a functional label.

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。

Agent fallback for CCSPlayerController_GetThinkFunction (auto-generated, category: func). Locate CCSPlayerController::GetThinkFunction in the CS2 server module via IDA Pro MCP and emit a fresh, minimal-unique artifact. The deterministic preprocessor could not resolve this symbol on the current gamever - your job is the re-sign. Trigger: CCSPlayerController_GetThinkFunction, CCSPlayerController::GetThinkFunction

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Agent fallback for CCSPlayerController_GetThinkFunction-decompiles (auto-generated, category: func). Locate CCSPlayerController::GetThinkFunction in the CS2 server module via IDA Pro MCP and emit a fresh, minimal-unique artifact. The deterministic preprocessor could not resolve this symbol on the current gamever - your job is the re-sign. Trigger: CCSPlayerController_GetThinkFunction-decompiles, CCSPlayerController::GetThinkFunction

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Tests APIs for Broken Function Level Authorization (BFLA) vulnerabilities where regular users can invoke administrative functions or access privileged API endpoints by directly calling them. The tester identifies admin and privileged endpoints, then attempts to access them with regular user credentials by manipulating HTTP methods, URL paths, and request parameters. Maps to OWASP API5:2023 Broken Function Level Authorization. Activates for requests involving BFLA testing, admin endpoint bypass, function-level access control testing, or API privilege escalation.

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Retrieve normalized OFAC sanctions pressure — designation summaries, recent additions, and per-country/per-program aggregates including sanctioned vessels and aircraft. Use when the user asks which countries or programs face sanctions pressure, or what was recently designated.

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Build an automated IOC enrichment pipeline on OpenCTI (STIX 2.1 native threat intel platform) using its internal enrichment connectors to pull context from VirusTotal, Shodan, AbuseIPDB, and GreyNoise, correlate indicators with known actors/campaigns, and score them for analyst prioritization. Use when deploying OpenCTI or automating enrichment and confidence scoring of newly ingested indicators.

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Deploy an event-driven workflow that routes S3 uploads to either Lambda or Fargate via Step Functions based on file size. Uses EventBridge to trigger a Step Functions state machine when objects are uploaded to S3. Small files are processed by Lambda, large files by a Fargate task. Includes VPC, ECR repository, ECS cluster, and scoped IAM roles. Trigger keywords: Step Functions, Fargate, Lambda, S3 event, EventBridge, ECS, ECR, file processing, workflow orchestration, serverless.

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Builds resilient, long-running, multi-step applications with AWS Lambda durable functions with automatic state persistence, retry logic, and orchestration for long-running executions. Covers the critical replay model, step operations, wait/callback patterns, error handling with saga pattern, testing with LocalDurableTestRunner. Triggers on phrases like lambda durable functions, durable execution, workflow orchestration, state machines, retry/checkpoint patterns, long-running stateful Lambda functions, saga pattern, human-in-the-loop callbacks, reliable serverless applications, context.step, context.wait, context.invoke, context.runInChildContext, withDurableExecution, DurableContext, UnrecoverableInvocationError, durable-execution-sdk, qualified ARN invocation, and durable handler replay.

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Guide for translating Python code to functional Scala style. Use when converting Python code involving higher-order functions, decorators, closures, generators, or when aiming for idiomatic functional Scala with pattern matching, Option handling, and monadic operations.

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基于 instinct 的持续学习系统，通过 hooks 观察会话，创建带置信度评分的 atomic instincts， 并将高置信度 instinct 演进为 skills/commands/agents。v2.1 增加项目级 instincts 防止跨项目污染。

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Use when integrating legacy code, custom code, or C/C++ code into Simulink via C Function blocks. Configure Simulink C Function blocks programmatically using the SymbolSpec API. TRIGGER when the user: - Asks about calling or wrapping C/C++ code or libraries in Simulink - Wants to create, configure, or use a C Function block - Mentions integrating custom code, legacy code, or external C/C++ into a model - Asks to integrate a C++ class into a Simulink model or test bench - Mentions inputs/outputs/parameters for a C++ algorithm block

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Assesses RNA-seq data quality specifically for alternative splicing analysis. QC layers include experimental design audit (library prep, read length, depth, replicates), STAR 2-pass cohort-style alignment, junction saturation curves and discovery plateau detection, novel-vs-known junction ratio diagnostics, junction-overhang distribution, splice-site strength scoring (MaxEntScan intrinsic + SpliceAI context-aware), strandedness verification, GENCODE basic vs comprehensive choice, and rRNA contamination screening. Splicing analysis is more demanding than DGE on read length, depth, library prep, alignment strategy, and annotation choice — failures silently bias PSI estimates and inflate novel-junction false positives. Use when evaluating data suitability for splicing analysis, troubleshooting low event detection, or designing sequencing experiments where AS is a primary endpoint.

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。