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Specialized agentic and context engineering prompt methodology for Claude. Use when designing AI agents — system prompts, tool definitions, context window architectures, failure mode planning, multi-agent coordination, agent evaluation. Trigger on: "design an agent," "build a system prompt for an agent," "architect an agent's context," "design tool definitions," "agent system prompt," "context window architecture," "tool interface design," "evaluate agent behavior," "multi-agent coordination," "context engineering for agents," "ACI design." Provides 12 tested approaches for agent architecture (designing agents, not using them). Do NOT use for evaluating or scoring individual non-agent prompts (use rootnode-prompt-validation if available). Do NOT use for general software architecture that hosts agents (use standard technical reasoning for that).

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Generalised autonomous optimisation loop — soft RLVR for any artifact a user can measure. Use this skill whenever a user wants to iteratively improve an artifact — code, prompts, documents, configs, designs, content — by running structured experiments, evaluating results against a multi-dimensional rubric, and learning from each attempt. Triggers include: "optimise this", "keep improving until it's good", "run experiments on", "autoresearch", "iterate on this overnight", "try different approaches and pick the best", or any request implying repeated evaluate-and-improve cycles. Also use when the user wants to improve a system prompt, a data pipeline, a writing style, or any artifact where quality can be decomposed into measurable tracks. For inference optimisation tasks (model latency, throughput, quantization, GPU deployment), a* delegates the low-level tuning to AITune while maintaining quality tracking and learning.

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Angular forms: Reactive Forms (preferred — typed FormGroup/FormControl since Angular 14, FormBuilder, custom + async validators, FormArray, multi-step) and Template-driven (`[(ngModel)]` + FormsModule). Validation strategies, server error mapping, accessibility. Use this skill to: - Build Reactive Forms with typed FormGroup/FormControl. - Use FormBuilder для concise syntax. - Implement custom synchronous and async validators. - Wire FormArray for dynamic field lists. - Map server errors back to form fields. - Pick Reactive vs Template-driven (prefer Reactive). Do NOT use this skill for: - General conventions (see angular-conventions). - State management beyond forms (see angular-state-and-rx). - Routing (see angular-routing). - Testing forms (see angular-testing).

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React Server Components vs Client Components in Next.js: the boundary discipline, "use client" / "use server" directives, Server Actions, what serializes across the boundary, common pitfalls. Use this skill to: - Decide whether a component should be RSC or Client. - Push the "use client" boundary as deep as possible. - Implement Server Actions correctly (auth, validation, revalidation). - Compose RSC and Client components without breaking the model. - Pass data across the boundary safely. Do NOT use this skill for: - General Next.js conventions (see nextjs-conventions). - Specific data-fetching APIs and caching (see nextjs-data-fetching). - Routing (see nextjs-routing).

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Testing Angular 18-21: TestBed, component harnesses (@angular/cdk/testing), Karma+Jasmine (default historical) vs Jest (jest-preset-angular, modern), Angular Testing Library (RTL-style). HttpClient mocking via HttpTestingController. NgRx Effects testing. Cypress / Playwright e2e. Use this skill to: - Detect runner (Karma+Jasmine vs Jest) and configure correctly. - Write component tests with TestBed. - Use component harnesses for Material / custom UI components. - Mock HttpClient via provideHttpClientTesting + HttpTestingController. - Test signal-based inputs with componentRef.setInput(). - Test NgRx Effects with provideMockActions. Do NOT use this skill for: - General Angular conventions (see angular-conventions). - Routing patterns broadly (see angular-routing — covers testing routes briefly). - Form patterns broadly (see angular-forms).

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Data fetching, caching, and revalidation patterns in Next.js App Router. Covers native fetch caching, ISR/SSG/SSR per-route choice, generateStaticParams, unstable_cache, revalidatePath/revalidateTag, Route Handlers, dynamic vs static rendering. Use this skill to: - Pick the right rendering mode (SSG / ISR / SSR / streaming) per route. - Use native fetch caching options correctly. - Build Route Handlers that match REST conventions. - Invalidate cached data after mutations. - Use generateStaticParams for dynamic SSG. Do NOT use this skill for: - RSC vs Client boundaries (see server-component-patterns). - Routing primitives (see nextjs-routing). - General conventions (see nextjs-conventions).

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Testing strategies for Next.js: Server Components, Client Components, Server Actions, Route Handlers, end-to-end with Playwright. Vitest/Jest configuration, React Testing Library patterns, msw for network mocks. Use this skill to: - Pick the right test layer for what you're testing. - Configure Vitest or Jest for Next.js. - Test Server Components without running them in isolation. - Test Server Actions and Route Handlers as pure functions. - Set up Playwright for e2e and integration of RSC. Do NOT use this skill for: - General Next.js conventions (see nextjs-conventions). - RSC vs Client model (see server-component-patterns). - Plain Node.js test patterns (see nodejs-plugin equivalents).

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TypeScript and JavaScript performance and code-quality rules, usable in any framework. Use when writing, reviewing, or refactoring hot paths, data-structure choices, async flows, or bundle-size-sensitive code. Triggers include "optimize this function", "too slow", "reduce allocations", "bundle size", "lazy load", "speed up imports", or general code review of utilities and services.

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General workflow for auditing ML CI failures, experiment regressions, training run failures, golden metric failures, and telemetry-backed ML work-product claims from local repositories, logs, metrics, configs, and artifacts. Use when Codex needs to decide whether an ML failure is a model/convergence issue, correctness bug, data/config issue, infrastructure/runtime issue, evaluation/gating policy issue, or unsupported claim, and produce structured evidence-backed outputs.

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Complete methodology for computing publication-quality cumulative abnormal returns with proper event-study test statistics, matching the robustness of Kaspereit's eventstudy2 for Stata. Covers dateline construction, event-date mapping, estimation and event windows, thin-trading adjustment, OLS with Theil prediction error correction, abnormal return computation, CAR/CAAR/AAR accumulation, boundary contamination guards, and common tests such as Patell, BMP, Kolari-Pynnonen, generalized sign, Wilcoxon, and GRANK-T. Use when the user mentions abnormal returns, event windows, market-model regressions, CARs, CAAR, AAR, eventstudy2, thin trading, trade-to-trade returns, or event-study test statistics.

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Place pre-screened literature citations into a LaTeX or Word manuscript, or restyle the citations already in one. Three modes: (1) inline placement — inline \cite{}/\citet{}/\citep{} with a compiled references.bib, for author-date journals (APA, MLA, Harvard, Chicago author-date, IEEE, Vancouver); (2) footnote placement — full formatted \footnote{} or OOXML footnotes for legal and notes styles (Bluebook, OSCOLA, Chicago, APA, McGill) with Id./supra short forms; (3) restyle — convert existing footnote citations from one style to another. This skill is manual-invoke ONLY — trigger ONLY when the user explicitly runs /cite-placement or explicitly names the "cite-placement" skill. Do NOT auto-trigger on general citation, footnote, or reference requests.

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Use when a task needs the judgment of a Bioinformatics Scientist — designing a differential-expression or GWAS analysis with correct multiple-testing correction, calling and triaging variants from NGS data using genotype-likelihood confidence rather than raw allele counts, interpreting a BLAST/DIAMOND homology search, choosing or building a genomics pipeline (aligner, caller, normalization method) for a specific data-generating process, or judging whether a computational-biology analysis's statistical design actually supports the biological claim being made. Distinct from a bioinformatics-technician (executes and QC-gates an established pipeline someone else designed) and a data-scientist (general product/experimentation judgment, not genomics-specific statistics) — this role owns which method and statistical model a genomics question requires, and why.

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Designs, deploys, and secures Google Cloud Agent Gateway solutions. Use when the user needs to configure multi-agent security, ingress (CLIENT_TO_AGENT), or egress (AGENT_TO_ANYWHERE) patterns involving Model Armor, IAP, and Agent Registry. Don't use for general Cloud Load Balancing or basic VPC setup not related to Agent Gateways.

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Manages clusters, instances, and backups for AlloyDB for PostgreSQL, and integrates with AlloyDB Model Context Protocol (MCP) tools for automated database operations. Use when creating, configuring, or administering AlloyDB databases. Do NOT use for general PostgreSQL instances (e.g. Cloud SQL) or other GCP databases.

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Troubleshoots Google Cloud Gemini Enterprise Agent Platform issues (Agent Gateway, Registry, Identity, Policies, Model Armor, Identity-Aware Proxy (IAP)). Use when agent requests fail with 403 (especially unauthorized egress), Agent Runtime queries return 500, or gateway/IAP logs show permission errors. Don't use for general Google Cloud Identity and Access Management (IAM) debugging or networking issues unrelated to the Agent Platform stack.

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Leverages BigQuery's built-in machine learning and GenAI capabilities for advanced data analytics. Use when you need to write SQL queries that perform time-series forecasting, predict values, detect outliers or anomalies, find key drivers, perform semantic search or vector search, classify text, calculate similarity, summarize content, translate language, evaluate models, filter by semantic conditions, measure the causal effect of an intervention, compute correlations between columns, detect change points or structural breaks, extract trend or seasonality components, or leverage generative AI capabilities in BigQuery. Do not use for general BigQuery dataset, table, or job management requests.

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Use when an admin or partner needs to package an Experience Cloud (Community) site as a reusable Lightning Bolt Solution for distribution — covers the export workflow from Experience Builder, what gets bundled (ExperienceBundle, custom apps, flow categories, theme, layouts, navigation menus) versus what does NOT (data, CMS content, files), choosing Bolt vs managed package vs unlocked package vs cloning a site, sandbox-to-production promotion, multi-org distribution, AppExchange listing as a Bolt, and template versioning via the LightningBolt metadata `versionNumber`. Triggers: 'turn this community into a reusable template', 'package an Experience Cloud site to ship to multiple orgs', 'export Experience Builder template for AppExchange', 'should we use a Bolt or a managed package for this community', 'create an industry-specific community starter', 'how do we version our partner portal template', 'distribute branded Experience site across business units'. NOT for general Experience Cloud site build, content, or member setup (use admin/experience-cloud-site-setup, admin/experience-cloud-cms-content, admin/experience-cloud-member-management). NOT for shipping Apex / LWC / data-model functionality as a product (use devops/managed-package-development, devops/second-generation-managed-packages, devops/unlocked-package-development). NOT for moving a single Experience site between sandbox and prod as a one-off (use devops/experience-cloud-deployment-admin, devops/cicd-for-experience-cloud).

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Build or extend a dygo Business App using the supported project layout, generators, metadata, SDK, and validation workflow. Use for general app work that is not primarily Entity modeling, access, hooks, Jobs, fixtures, or patches.

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Benchmark multi-agent coordination systems with experiment design, power analysis, human rating protocols, bootstrap confidence intervals, and reproducible reporting. Use for salvage latency, recovery fidelity, coordination overhead, and protocol comparison studies. NOT for ML model benchmarking, web-product A/B testing, survey design, or general-purpose data science.

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Time-travel debugging for DAG executions. Inspect agent state at any node, replay decisions with modified inputs, compare execution traces side-by-side, and identify where reasoning diverged. Inspired by LangGraph Studio's state-editing model and Temporal's event history. Activate on "debug DAG", "replay execution", "time travel debug", "inspect node state", "what went wrong at step", "compare runs", "execution diff". NOT for live monitoring (use dag-runtime + websocket-streaming), failure analysis (use dag-ops), or general code debugging.

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Use when designing or fixing a Content Security Policy on a real site, choosing between nonce-based and hash-based CSP, adding strict-dynamic, debugging "Refused to execute inline script" errors, deploying CSP in report-only mode first, configuring report-to / report-uri, or auditing an existing policy for unsafe-inline / unsafe-eval / wildcards. Triggers: "CSP blocks legitimate inline script", strict-dynamic, nonce-{RANDOM}, sha256-{HASH}, object-src none, base-uri none, frame-ancestors, Trusted Types, X-Content-Security-Policy obsolete, report-only vs enforced. NOT for general HTTP security headers (HSTS, COOP/COEP), Trusted Types deep dive, CORS configuration, or building a WAF.

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Forces the laziest solution that actually works, simplest, shortest, most minimal. Channels a senior dev who has seen everything: question whether the task needs to exist at all (YAGNI), reach for the standard library before custom code, native platform features before dependencies, one line before fifty. Supports intensity levels: lite, full (default), ultra. Use on ANY coding task: writing, adding, refactoring, fixing, reviewing, or designing code, and choosing libraries or dependencies. Also use whenever the user says "ponytail", "be lazy", "lazy mode", "simplest solution", "minimal solution", "yagni", "do less", or "shortest path", or complains about over-engineering, bloat, boilerplate, or unnecessary dependencies. Do NOT use for non-coding requests (general knowledge, prose, translation, summaries, recipes).

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Knowledge base from EU Artificial Intelligence Act — Regulation (EU) 2024/1689. Use for AI system classification (high-risk vs prohibited vs general), conformity assessment procedures, GPAI model obligations, transparency and disclosure requirements, governance structure (AI Office / AI Board), penalty tiers, value-chain operator obligations, post-market monitoring, and Annex III use-case analysis. Does not cover EU liability rules for AI (separate proposed directive) or implementation guidance documents issued after July 2024.

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Knowledge base from the FAA AMS Lifecycle V&V Guidelines (verification and validation), Version 3.0, dated April 2017. Use for applying verification and validation across the FAA Acquisition Management System (AMS) lifecycle: the verify-vs-validate distinction (built right vs. right thing), the work-product/component/product tiers, the nine V&V planning elements, the four method catalogs (Appendix C), decision-point-gated V&V (SASP, CRD, Investment Analysis, Solution Implementation, In-Service Management), the Appendix B document checklists (PRD/APB/ISPD/PMP), and the DT/OT/IOA test triad. Scope limits: this is an FAA acquisition-V&V doctrine, NOT a general SE process model, requirements-engineering manual, or test-design textbook. It borrows its core V&V definitions from CMMI v1.2 (SEI, paraphrased) and presumes the surrounding AMS lifecycle (see the FAA SEM pack); its phase tables are policy snapshots as of April 2017 and it is thin on detailed test methods, tool specifics, and anything outside FAA acquisition governance.

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