Results for “arc”

Maintains a product repo's canonical docs structure (CLAUDE.md, progress.txt, docs/product+system+design-system, context/) as single source of truth, and keeps the log.md session history separate from the progress.txt state snapshot. Trigger after feature completion, before git push, on architecture changes, at session end, or explicit "update docs"/"sync documentation" requests. Skip for trivial changes (<10 LOC, no logic/schema/UI changes).

Provides Codex CLI delegation workflows for complex code generation and development tasks using OpenAI's GPT-5.3-codex models, including English prompt formulation, execution flags, sandbox modes, and safe result handling. Use when the user explicitly asks to use Codex for complex programming tasks such as code generation, refactoring, or architectural analysis. Triggers on "use codex", "delegate to codex", "run codex cli", "ask codex", "codex exec", "codex review".

Run the quarter-boundary ritual — roll the quarter-scoped project folders, archive finished projects, grade the closing quarter's GOALS/OKRs, and draft the new quarter's goals behind an approval gate. Trigger on /quarter-rollover, "quarter rollover", "roll the quarter", "close out Q2", "new quarter setup", or when tools/quarter_path.py --check reports drift. Distinct from /triage-tasks (daily gate) and /audit (repo health check).

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.

Skilled agent architecture replacing multi-agent system for RL training. Trigger when: (1) planning agent-guided training, (2) implementing tool-augmented LLM consultations, (3) comparing skilled vs multi-agent approaches, (4) designing simulate-verify loops for training, (5) implementing prompt evolution / learnable parameters, (6) understanding Claude Agent SDK integration in training, (7) debugging SkilledTrainer consultations or tool calls, (8) configuring agent safety bounds for training actions.

Turn natural-language descriptions into editable `.drawio` diagrams and export them to PNG / SVG / PDF / JPG via the native draw.io desktop CLI, or turn an existing codebase (Python / JS-TS / Go / Rust) into an auto-laid-out structure diagram. Wraps Agents365-ai/drawio-skill: 6 diagram presets (ERD, UML class, sequence, architecture, ML/DL, flowchart), search across 10,000+ official AWS/Azure/GCP/Cisco/K8s/UML/ BPMN shapes, 321 AI/LLM brand logos, vision self-check + auto-fix, and a 5-round iterative refinement loop. No MCP server, no daemon — runs from a single SKILL.md and the draw.io CLI. Use when the user wants polished, precise, exportable diagrams or wants to visualize code structure. Triggers on: drawio, draw.io, drawio diagram, architecture diagram, ERD, UML diagram, sequence diagram, flowchart, network diagram, visualize codebase, code structure diagram, class hierarchy, export diagram png/svg/pdf, AWS/Azure/GCP icon, draw.io shapes.

Find and query PI assets using MATLAB's Industrial Communication Toolbox. Two paths: (1) direct PI tag lookup via piclient + tags when user provides a tag name or description (R2022a+), (2) Asset Framework navigation via afclient when user needs element/attribute hierarchy (R2026a+). Use when working with PI AF servers, PI Data Archive tags, asset hierarchies, element templates, attribute lookup, or historical data from OSIsoft PI systems.

Turn assumptions, funnel opportunities, and product questions into a prioritised, feasibility-checked experiment backlog. Filters by traffic reality, metric latency, and method feasibility — not just ICE/RICE scoring. Maintains a living portfolio with status (idea → designed → running → readout → archived). Load when the user says "what should we test next", "build an experiment backlog", "prioritise our tests", "where should we experiment", "what's worth testing", or when the experimentation orchestrator routes here.

Expert knowledge for Azure Health Bot development including best practices, decision making, architecture & design patterns, security, configuration, and integrations & coding patterns. Use when wiring Health Bot to channels/APIs, configuring scenarios/cards, securing PHI, using orchestrator, or estimating costs, and other Azure Health Bot related development tasks. Not for Azure AI Bot Service (use azure-bot-service), Azure Communication Services (use azure-communication-services), Azure Functions (use azure-functions).

Personal leadership development for founders and first-time CEOs. Covers founder archetype identification, delegation frameworks, energy management, CEO calendar audits, leadership style evolution, blind spot identification, imposter syndrome, founder mental health, and succession planning. Use when a founder feels like the bottleneck, struggles to delegate, is burning out, transitioning from IC to executive, managing a board, or when user mentions founder mode, CEO growth, leadership development, delegation, burnout, or imposter syndrome.

Systematic API design methodology with 6 validated patterns covering parameter categorization, safe refactoring, audit-first approach, automated validation, quality gates, and example-driven documentation. Use when designing new APIs, improving API consistency, implementing breaking change policies, or building API quality enforcement. Provides deterministic decision trees (5-tier parameter system), validation tool architecture, pre-commit hook patterns. Validated with 82.5% cross-domain transferability, 37.5% efficiency gains through audit-first refactoring.

Designs the contract for an API change inside one codebase — a component's props, a function surface, URL or query parameters, an event payload, or a module boundary — through a discovery pass, an options document with one recommendation, a question round, and an adversarial validation round, with every element of the contract justified from one stated goal. Use when you want to design, shape, decide, or nail down an interface, contract, signature, or API change for a capability you can already describe, sized for roughly one pull request. Produces a design document and changes no code. Does not specify what a feature should do — use plan-a-feature. Does not plan delivery or sequencing — use plan-implementation. Does not assess the architecture of existing code — use architectural-analysis. Does not write the code — use tdd. Does not restructure existing code — use refactor. Runs its rounds without pausing for review; to review each round as it lands, use pairing.

Expert knowledge for Azure Blueprints development including troubleshooting, architecture & design patterns, security, configuration, and integrations & coding patterns. Use when defining Azure Blueprints, mapping built-in compliance sets, automating via CLI/PowerShell/REST, or fixing assignment errors, and other Azure Blueprints related development tasks. Not for Azure Policy (use azure-policy), Azure Resource Manager (use azure-resource-manager), Azure Managed Applications (use azure-managed-applications), Azure Deployment Environments (use azure-deployment-environments).

Post-execution skill crystallization and learning engine updates for WinDAGs. Runs after successful execution to update Thompson sampling parameters, track method quality, detect monster-barring, log near-miss events, and signal Kuhnian crises. Activate on "curator", "learning update", "skill crystallization", "Thompson sampling", "monster-barring", "near-miss", "Kuhnian crisis", "post-execution learning". NOT for pre-execution risk scanning (use windags-premortem), retrospective analysis (use windags-looking-back), or DAG construction (use windags-architect).

Expert knowledge for Azure Repos development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, and integrations & coding patterns. Use when managing Azure Git/TFVC repos, branch policies/PRs, migrations from TFVC/SVN, CodeQL/GHAS scanning, or auth/permissions, and other Azure Repos related development tasks. Not for Azure DevOps (use azure-devops), Azure Boards (use azure-boards), Azure Pipelines (use azure-pipelines), Azure Artifacts (use azure-artifacts).

Voice agents represent the frontier of AI interaction - humans speaking naturally with AI systems. The challenge isn't just speech recognition and synthesis, it's achieving natural conversation flow with sub-800ms latency while handling interruptions, background noise, and emotional nuance. This skill covers two architectures: speech-to-speech (OpenAI Realtime API, lowest latency, most natural) and pipeline (STT→LLM→TTS, more control, easier to debug). Key insight: latency is the constraint. Hu

Voice agents represent the frontier of AI interaction - humans speaking naturally with AI systems. The challenge isn't just speech recognition and synthesis, it's achieving natural conversation flow with sub-800ms latency while handling interruptions, background noise, and emotional nuance. This skill covers two architectures: speech-to-speech (OpenAI Realtime API, lowest latency, most natural) and pipeline (STT→LLM→TTS, more control, easier to debug). Key insight: latency is the constraint. Hu

Voice agents represent the frontier of AI interaction - humans speaking naturally with AI systems. The challenge isn't just speech recognition and synthesis, it's achieving natural conversation flow with sub-800ms latency while handling interruptions, background noise, and emotional nuance. This skill covers two architectures: speech-to-speech (OpenAI Realtime API, lowest latency, most natural) and pipeline (STT→LLM→TTS, more control, easier to debug). Key insight: latency is the constraint. Hu

Bidirectional conversion between Elm and Scala. Use when migrating projects between these languages in either direction. Extends meta-convert-dev with Elm↔Scala specific patterns. Use when migrating Elm frontend applications to Scala backends or full-stack Scala, translating The Elm Architecture to functional Scala patterns, or refactoring type-safe functional code from compile-time guarantees to more powerful type system features. Extends meta-convert-dev with Elm-to-Scala specific patterns.

Build production-ready systems with stability patterns: circuit breakers, bulkheads, timeouts, and retry logic. Use when the user mentions "production outage", "circuit breaker", "timeout strategy", "deployment pipeline", "chaos engineering", "bulkhead pattern", "retry with backoff", or "health checks". Also trigger when designing resilient microservices, planning zero-downtime deployments, or investigating cascading failure scenarios. Covers capacity planning, health checks, and anti-fragility patterns. For data systems, see ddia-systems. For system architecture, see system-design.

Expert knowledge for Azure DevOps development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when managing org/projects, Boards/work items, repos/pipelines, Analytics/OData/Power BI, or Azure DevOps Server, and other Azure DevOps related development tasks. Not for Azure Boards (use azure-boards), Azure Pipelines (use azure-pipelines), Azure Repos (use azure-repos), Azure Test Plans (use azure-test-plans).

Expert knowledge for Azure Databricks development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using Unity Catalog, Lakeflow/Lakebase, SQL warehouses, Model Serving, or Lakehouse Federation, and other Azure Databricks related development tasks. Not for Azure Synapse Analytics (use azure-synapse-analytics), Azure HDInsight (use azure-hdinsight), Azure Machine Learning (use azure-machine-learning), Azure Data Factory (use azure-data-factory).

This skill should be used when the user asks to "find skills in the wild", "assimilate popular workflows", "discover SKILL.md files in repos", "research external skills", "find workflow patterns", "survey the skill landscape", "what skills exist out there", or wants to investigate public repositories for extractable processes, babysitter plugins, and reusable procedural insights. Searches GitHub for SKILL.md files, classifies repos by archetype, and maintains structured research under docs/reference-repos/.

Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports continuous agent operation via Claude Code /loop and OpenClaw heartbeat, and produces research presentations and papers. Use when starting a research project, running autonomous experiments, or managing a multi-hypothesis research effort.

Expert knowledge for Azure DNS development including decision making, architecture & design patterns, limits & quotas, security, configuration, and integrations & coding patterns. Use when managing DNS zones/records, DNSSEC, Private DNS/resolvers, reverse DNS, or migrating legacy Private DNS, and other Azure DNS related development tasks. Not for Azure Traffic Manager (use azure-traffic-manager), Azure Front Door (use azure-front-door), Azure Virtual Network (use azure-virtual-network), Azure Private Link (use azure-private-link).

Expert knowledge for Azure Copilot development including troubleshooting, decision making, architecture & design patterns, security, configuration, and integrations & coding patterns. Use when sizing VMs, generating Bicep/Terraform, configuring Cosmos DB storage, or debugging App Service/VM disks, and other Azure Copilot related development tasks. Not for Azure AI services (use microsoft-foundry-tools), Azure Machine Learning (use azure-machine-learning), Azure AI Search (use azure-cognitive-search), Azure AI Bot Service (use azure-bot-service).

Composite skill — land a safe first PR in an unfamiliar repo without rediscovery loops or guessing at conventions. Chains onboard-new-repo (architecture + scripts + CI scan) → context-pack (relevant files + standards + ADRs for the scoped change) → scope-and-execute (narrow the task) → test-driven-development (write failing test first) → pr-to-release (open the PR). Use the first time you contribute to a repo OR when an unfamiliar contributor needs a guided first change.

Design and implement ML operations — model registry, serving patterns, deployment strategies (shadow/canary/blue-green), drift detection, feature stores, retraining triggers, and prediction monitoring. Use when asked to "deploy a model", "model registry", "MLflow", "feature store", "drift detection", "retrain trigger", "shadow mode", "model versioning", "serving infrastructure", or "ML pipeline". Do NOT use for: prompt engineering or RAG pipelines — see prompt-engineering and rag-architect skills. Do NOT use for: general API deployment without an ML component.

Modern authentication implementation for 2026 - passkeys (WebAuthn), OAuth (Google, Apple), magic links, and cross-device sync. Use for passwordless-first authentication, social login setup, Supabase Auth, Next.js auth flows, and multi-factor authentication. Activate on "passkeys", "WebAuthn", "Google Sign-In", "Apple Sign-In", "magic link", "passwordless", "authentication", "login", "OAuth", "social login". NOT for session management without auth (use standard JWT docs), authorization/RBAC (use security-auditor), or API key management (use api-architect).

Bidirectional conversion between Clojure and Elm. Use when migrating projects between these languages in either direction. Extends meta-convert-dev with Clojure↔Elm specific patterns. Use when migrating Clojure projects to Elm, translating functional patterns from JVM to browser, or building type-safe frontends from Clojure logic. Extends meta-convert-dev with Clojure-to-Elm specific patterns for handling dynamic-to-static typing, REPL-driven to TEA architecture, and side effects to managed effects.

Expert knowledge for Azure Quantum development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using QDK with Azure Quantum workspaces, IonQ/Quantinuum/Rigetti targets, QIR/OpenQASM jobs, or Resource Estimator, and other Azure Quantum related development tasks. Not for Azure HPC Cache (use azure-hpc-cache), Azure Batch (use azure-batch), Azure Databricks (use azure-databricks), Azure Machine Learning (use azure-machine-learning).

Expert knowledge for Azure Pipelines development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when setting up YAML CI/CD, service connections/Key Vault, self-hosted agents, IIS/Kubernetes deploys, or Git integrations, and other Azure Pipelines related development tasks. Not for Azure DevOps (use azure-devops), Azure Boards (use azure-boards), Azure Repos (use azure-repos), Azure Test Plans (use azure-test-plans).

Expert knowledge for Azure Networking development including troubleshooting, decision making, architecture & design patterns, and security. Use when designing hub-spoke VNets, Azure Firewall/NSG rules, App Gateway/Front Door WAF, DDoS, or fixing Microsoft.Network errors, and other Azure Networking related development tasks. Not for Azure Virtual Network (use azure-virtual-network), Azure Virtual Network Manager (use azure-virtual-network-manager), Azure Virtual WAN (use azure-virtual-wan), Azure Network Watcher (use azure-network-watcher).

Expert knowledge for Azure Payment Hsm development including troubleshooting, best practices, decision making, architecture & design patterns, security, and configuration. Use when configuring Payment HSM VNets/FastPath, payShield Manager access, HA/DR topologies, SKUs, or traffic inspection, and other Azure Payment Hsm related development tasks. Not for Azure Dedicated HSM (use azure-dedicated-hsm), Azure Cloud Hsm (use azure-cloud-hsm), Azure Key Vault (use azure-key-vault), Azure Information Protection (use azure-information-protection).

Intelligent inbox triage that connects to Gmail or Outlook, scores emails by importance (0-100) using sender recognition, urgency keywords, thread depth, time sensitivity, and business relevance, categorizes into Urgent/Important/Routine/Archive, and generates reply suggestions for top priority items. Use when the user wants to check email, review inbox, triage messages, see what's urgent, or get reply suggestions. Triggers on check email, inbox triage, what's important, email summary, unread emails, priority inbox.

Quarto agente do Time de Migração. Opera em duas fases. Fase 1: detecta a topologia do legado, sempre propõe uma topologia moderna alternativa e produz topology_decision.md (com pausa humana para aprovação). Fase 2: desenha as specs do sistema novo sob a topologia escolhida, produzindo target_architecture.md, target_domain_model.md, target_data_model.md e data_migration_plan.md, com rastreabilidade total para o legado. Ativação: /reversa-designer (geralmente invocado por /reversa-migrate).