Results for “arc”

Expert guidance for FedRAMP certification and compliance. Use this skill whenever a user asks about FedRAMP authorization, ATO (Authority to Operate), cloud security for federal government, NIST SP 800-53 controls, CSP compliance, or any of the core FedRAMP document types: SSP, SAP, SAR, POA&M, CIS/CRM workbooks. Also trigger for questions about FedRAMP impact levels (Low, Moderate, High, LI-SaaS), FedRAMP 20x, OSCAL, 3PAO assessments, continuous monitoring (ConMon), gap assessments, system boundary definition, FedRAMP readiness, or architecture reviews for federal cloud. When in doubt, use this skill — it covers the full FedRAMP lifecycle from readiness through continuous monitoring.

Operate Playwright for browser automation end to end: author and debug E2E test suites (robust locators, network interception and mocking, parallel workers, accessibility snapshot checks), wire them into CI, and drive headless browsing and scraping with an extract -> validate -> save loop. Use when writing, running, fixing, or scraping with Playwright, when a Playwright CI failure or JSON report needs triage, or when the bundled pwrun script should analyze a run. Do not use for QA strategy or test framework selection (route to qa-methodology), for frontend component or architecture design (route to frontend-engineering), or for Cloudflare/DDoS-GUARD challenge bypass (use flaresolverr).

Run a Virtual Think Tank — a structured multi-persona debate — before planning or making architectural/design/strategic decisions. Use this skill whenever the user is about to plan a system, make a technology choice, evaluate trade-offs, decide on an approach, or faces any decision where multiple perspectives would sharpen the outcome. Also trigger when the user says "think tank", "debate this", "perspectives on", "trade-offs", "should I use X or Y", "help me decide", "before we plan", or asks for pros/cons of competing approaches. This skill should run BEFORE any implementation planning begins — it produces a structured analysis that feeds into better plans.

Gracefully retire a skill that is redundant, superseded, or no longer earning its place in the context window. Load when improve-skills finds a skill scoring 0-5/14 AND research confirms the domain is now handled natively by current models, when two skills have overlapping triggers and one subsumes the other, when the user asks to remove a skill, retire a skill, delete a skill, or clean up redundant skills, or when validate-skills flags a skill as a duplicate trigger risk. Handles removal cleanly: updates all callers, removes from AGENTS.md, updates README, and archives rather than deletes so the skill can be recovered if needed.

Design motion, transitions, and bespoke graphics for native Rust gpui apps — the framework behind Zed and the pd-console operator console. Use for with_animation, easing curves, BoxShadow/glow, breathing dots, pane expand/zoom/slide transitions, and custom paint/Vello/wgpu surfaces. Trigger on: gpui animation, with_animation, gpui transition, gpui easing, BoxShadow, gpui shadow, pd-console motion, gpui paint/canvas, Vello, wgpu, "lift/slide/zoom/spring in gpui", reduced-motion in a native Rust UI, "animation re-renders forever", repeat() never stops. NOT for: web/React motion (→ animation-system-architect), CLI/TUI output (→ beautiful-cli-design), general non-motion GUI layout/color/typography (→ beautiful-gui-design).

Turn a rough idea into a fully approved design before any code is written. Load when the user wants to brainstorm, explore ideas, design a feature, think through approaches, plan a new capability, or figure out what to build. Also triggers on "let's think through", "help me design", "explore options", "what's the best approach for", "I have an idea for", "before we build", or any request to design something before implementation. Enforces a hard gate: no code, no implementation until user approves a design. For executable feature specs (FRs, NFRs, ACs as Given/When/Then), route to `feature-spec` instead — brainstorming owns approach and architecture, not machine-readable requirements.

Build and extend pd-console — Port Daddy's GPU-native macOS operator console (GPUI 0.2.x, Zed's Rust UI). Covers the render-agnostic Block/Pane(Surface) contract, the two-thread reqwest↔smol refresh pipeline, Taffy flexbox layout, uniform_list virtual scroll, focus + keyboard nav, the OKLCH theme and ICS maritime flag badges, GPUI's missing text-input, and the real feature-gated cargo/CI gate. Use when adding panes, visual polish, or debugging GPUI rendering/layout/focus in core/pd-console. NOT for the TypeScript daemon, generic Rust toolchain/borrow-checker help (use rust-with-claude-code), or non-pd GPUI apps with a different theme/architecture.

Analyze and optimize X (Twitter) posts for viral potential and reach using heuristics inspired by X's published open-source recommendation architecture. Use when user wants to: (1) Check if a post will go viral, (2) Score a tweet for engagement potential, (3) Optimize or rewrite a tweet for algorithmic reach, (4) Understand why a tweet underperformed, (5) Build audience in a specific niche. Triggers: "Check if this will go viral", "Make this post buzz", "Will this tweet perform well?", "Optimize my tweet", "How can I make this viral?", "rewrite this tweet", "improve my tweet engagement", "twitter algorithm", "tweet reach", "debug underperforming tweet", "バズるかチェックして", "Xでバズる投稿にして", "伸びるかチェックして", "この投稿を伸ばして", "投稿を改善して", "ツイートを最適化して"

Drive mex (`mex-agent`), persistent project memory and code graphs for AI coding agents. One command scaffolds a living wiki, builds a deterministic code graph, and installs a project anchor file (CLAUDE.md, root AGENTS.md, .cursorrules, .windsurfrules, copilot-instructions.md, or .opencode/opencode.json) that your agent auto-loads as a standing rule document. Use when the user wants to `mex setup` a new project, build a symbol-grounded wiki, keep knowledge connected to implementation, route relevant context to agents, or run drift detection (`mex check`, `mex sync`). Triggers on: "mex setup", "project memory", "code graphs", "codebase documentation", "drift detection", "agent memory", "structured scaffolds", "architectural context", "living wiki", "project anchor file".

Drafts litigation-ready U.S. pre-suit demand letters that function as settlement instruments and defensible future exhibits. Enforces element-driven narratives, verified authority, damages methodology, and ethics guardrails. Use this skill when the user mentions demand letter, pre-suit demand, breach and cure notice, settlement demand, insurance policy-limits demand, Stowers demand, FDCPA collection letter, notice of intent to file, cease and desist demand for payment, or pre-litigation correspondence. Also trigger when the user asks about FRE 408 framing, contractual notice compliance, statutory pre-suit prerequisites, evidence preservation notices, or quantifying damages for a demand package. Skill includes 14 verified real-letter exemplars referenced from `references/ARCHETYPE-INDEX.md`.

Turn any GitHub repo, local folder, PR, or markdown/Obsidian vault into an interactive architecture map with CodeFlow — a zero-build single index.html browser app (React 18 + D3.js from pinned CDNs) that runs 100% client-side with no backend and no data collection. Pick an input (public repo, private repo with a local token, local files, PR URL, markdown vault), choose a visualization mode (folder/layer/churn/blast), and read the analysis — dependency graph, blast radius, code ownership, heuristic security scanner, pattern/anti-pattern detection, A–F health score, activity heatmap, PR impact — then export JSON/Markdown/text/SVG/PDF or wire the self-updating CodeFlow Card SVG onto a README.

Comprehensive blog writing skill that handles technical blog posts, personal voice writing, brain dump transformation, and category-aware AEO-optimized content. Use when: (1) writing, editing, or proofreading a blog article or post, (2) transforming unstructured brain dumps into polished posts, (3) writing in specific personal voices (Jarad, Nick Nisi), (4) creating category-aware technology/company/product posts, (5) building tutorials, deep dives, postmortems, benchmarks, or architecture posts, (6) writing engineering blogs, dev blogs, programming blogs, coding tutorials, or documentation posts. Triggers: blog post, blog writing, technical blog, dev tutorial, brain dump, article, content writing, developer article, engineering blog, programming blog, coding tutorial, documentation post, technical writing, blog editing, proofreading, developer content

WOLF v6.3 — Fully autonomous multi-strategy trading for Hyperliquid perps via Senpi MCP. Manages multiple strategies simultaneously, each with independent wallets, budgets, slots, and DSL configs. 5+N cron architecture: 5 shared wolf crons (Emerging Movers 3min, SM Flip 5min, Watchdog 5min, Risk Guardian 5min, Health Check 10min) plus one DSL v5.2 cron per strategy (native Hyperliquid SL sync via dsl-dynamic-stop-loss skill v5.3.1). Same asset can be traded in different strategies simultaneously. Enter early on first jumps, not at confirmed peaks. Dynamic risk-based leverage per strategy. Requires Senpi MCP connection, python3, mcporter CLI, OpenClaw cron system, and dsl-dynamic-stop-loss skill (provides dsl-cli.py + dsl-v5.py).

STATA code pattern library for empirical archival accounting research. Provides tested syntax from 126 peer-reviewed JAR (Journal of Accounting Research) replication files (2017-2025). Use when the user asks procedural questions like "How do I implement [method]?" or "Show me code for [technique]" — including: entropy balancing, propensity score matching (PSM), difference-in-differences (DiD), regression discontinuity (RDD), instrumental variables (IV), event studies (CAR/BHAR), survival analysis, Fama-MacBeth regressions, bootstrap, quantile regression, reghdfe/xtreg/areg, clustering standard errors, fixed effects, esttab/outreg2 table formatting, winsorization, leads/lags. Users can specify their variables (e.g., treatment, outcomes, controls) and receive adapted syntax. NOTE: This skill provides code patterns from published papers, not research design advice.

Instrument a shipped product's AI agents with tracing and observability so you can see what they did, why outputs happened, and what each run cost. Plain-language primer plus free-tier-first backend selection (Langfuse, Phoenix, LangSmith, Braintrust) and OpenTelemetry/OpenInference instrumentation. Load when the user asks to add observability, add tracing, instrument my agents, see what my agent is doing in production, set up Langfuse or Phoenix or LangSmith, debug why my agent gave a bad answer, or track LLM cost per request. Also fires when agent-system-architecture or setup-evaluation requires an observability plan for an agent-chain product. NOT for tracing the coding agent itself — that is run-trace. Precondition for runtime-learning-loop.

Use when the user wants to design, redesign, shape, critique, audit, polish, clarify, distill, harden, optimize, adapt, animate, colorize, extract, or otherwise improve a frontend interface. Covers websites, landing pages, dashboards, product UI, app shells, components, forms, settings, onboarding, and empty states. Handles UX review, visual hierarchy, information architecture, cognitive load, accessibility, performance, responsive behavior, theming, anti-patterns, typography, fonts, spacing, layout, alignment, color, motion, micro-interactions, UX copy, error states, edge cases, i18n, and reusable design systems or tokens. Also use for bland designs that need to become bolder or more delightful, loud designs that should become quieter, live browser iteration on UI elements, or ambitious visual effects that should feel technically extraordinary. Not for backend-only or non-UI tasks.

Use when the user wants to design, redesign, shape, critique, audit, polish, clarify, distill, harden, optimize, adapt, animate, colorize, extract, or otherwise improve a frontend interface. Covers websites, landing pages, dashboards, product UI, app shells, components, forms, settings, onboarding, and empty states. Handles UX review, visual hierarchy, information architecture, cognitive load, accessibility, performance, responsive behavior, theming, anti-patterns, typography, fonts, spacing, layout, alignment, color, motion, micro-interactions, UX copy, error states, edge cases, i18n, and reusable design systems or tokens. Also use for bland designs that need to become bolder or more delightful, loud designs that should become quieter, live browser iteration on UI elements, or ambitious visual effects that should feel technically extraordinary. Not for backend-only or non-UI tasks.

Use when the user wants to design, redesign, shape, critique, audit, polish, clarify, distill, harden, optimize, adapt, animate, colorize, extract, or otherwise improve a frontend interface. Covers websites, landing pages, dashboards, product UI, app shells, components, forms, settings, onboarding, and empty states. Handles UX review, visual hierarchy, information architecture, cognitive load, accessibility, performance, responsive behavior, theming, anti-patterns, typography, fonts, spacing, layout, alignment, color, motion, micro-interactions, UX copy, error states, edge cases, i18n, and reusable design systems or tokens. Also use for bland designs that need to become bolder or more delightful, loud designs that should become quieter, live browser iteration on UI elements, or ambitious visual effects that should feel technically extraordinary. Not for backend-only or non-UI tasks.

Use this skill to classify tabular data end-to-end in MATLAB — load a dataset, prepare and clean it, select promising classifiers, train them, and compare accuracies with cross-validation, holdout, or hyperparameter optimization plus statistical tests. TRIGGER when: user asks to classify tabular data, pick classifiers for a dataset, compare classifier accuracy, run cross-validation or a holdout evaluation, or find the best model with statistical uncertainty. DO NOT TRIGGER when: user has non-tabular inputs (images, sequences, time series), wants a regression model, is training a specific neural network architecture (use matlab-train-network), or wants cost-sensitive learning or an arbitrary class-prior vector (this skill only supports the built-in uniform-prior toggle for imbalanced data).

Use when the user wants to design, redesign, shape, critique, audit, polish, clarify, distill, harden, optimize, adapt, animate, colorize, extract, or otherwise improve a frontend interface. Covers websites, landing pages, dashboards, product UI, app shells, components, forms, settings, onboarding, and empty states. Handles UX review, visual hierarchy, information architecture, cognitive load, accessibility, performance, responsive behavior, theming, anti-patterns, typography, fonts, spacing, layout, alignment, color, motion, micro-interactions, UX copy, error states, edge cases, i18n, and reusable design systems or tokens. Also use for bland designs that need to become bolder or more delightful, loud designs that should become quieter, live browser iteration on UI elements, or ambitious visual effects that should feel technically extraordinary. Not for backend-only or non-UI tasks.

Perform a structured audit of a codebase covering security, code quality, performance, dependencies, architecture, and testing hygiene, then produce a prioritized findings report. Use this skill whenever the user asks for a code review, code audit, security review, codebase assessment, "look over this repo", "what's wrong with this codebase", legacy-code triage, pre-acquisition technical due diligence, or any request to systematically evaluate the health of a project. Trigger even when the user is casual ("can you eyeball my repo?") — this skill imposes the structure that ad-hoc review misses. This skill audits a whole repository at a point in time — for reviewing a diff or PR use the built-in code-review skill; for security checks on pending changes use security-review.

Use BEFORE generating, refactoring, reviewing, or debugging code. Trigger phrases include "write a function/script/class for X", "review this code/diff/PR", "refactor this", "debug this error", "is this implementation correct", "what's wrong with this code", "improve this code", "translate from X to Y", or any prompt with a code block the user wants you to act on. Also fires when planning architectural changes, picking algorithms or data structures, or evaluating dependency upgrades. Calls the code MCP tool to retrieve an engineering scaffold (failure pattern, procedure, correct-pattern example, verification step) before generating. Catches hallucinated APIs, lost edge cases, premature algorithm commitment, silent contract violations, refactors that change behavior masked by passing tests. Do NOT trigger for pure code reading with no action requested, simple syntax questions, file...

A Cursor Canvas is a live React app that the user can open beside the chat. You MUST use a canvas when the agent produces a standalone analytical artifact — quantitative analyses, billing investigations, security audits, architecture reviews, data-heavy content, timelines, charts, tables, interactive explorations, repeatable tools, or any response that benefits from visual layout. Especially prefer a canvas when presenting results from MCP tools (Datadog, Databricks, Linear, Sentry, Slack, etc.) where the data is the deliverable — render it in a rich canvas rather than dumping it into a markdown table or code block. If you catch yourself about to write a markdown table, stop and use a canvas instead. You MUST also read this skill whenever you create, edit, or debug any .canvas.tsx file.

>- Skill Refiner, the eval-guided improvement loop for SKILL.md files. Runs the bootstrap, score, propose, apply, and status cycle as a thin wrapper over the published @intentsolutions/refiner CLI, proposing safe, minimal, bounded SKILL.md edits and accepting an edit only when a held-out eval score strictly improves with no regression on any other case. Ships a 3-layer cost-tiered hook architecture (sinker, line, hook) that gates skill quality at edit time, end of turn, and commit time. Use when improving an existing skill, refining a SKILL.md against measured behavior, bootstrapping an eval set for a skill, or gating skill edits before they ship. Trigger with "/j-rig", "refine this skill", "bootstrap an eval set", "propose a skill edit", "promote the candidate", or "skill refiner status".

Cria visualizações 3D interativas de arquitetura de software usando Three.js, gerando HTML standalone com cenas navegáveis por câmera livre. Use esta skill sempre que o usuário pedir para visualizar arquitetura, módulos, dependências, camadas ou hierarquia de chamadas em 3D. Deve ser ativada quando o usuário mencionar termos como "code city", "cidade de código", "arquitetura 3D", "dependency graph 3D", "module map 3D", "layer stack 3D", "call graph 3D", "architecture tour", "tour pela arquitetura", "visualizar software em 3D", "Three.js" no contexto de software, ou pedir para explorar a estrutura de um sistema com câmera 3D. Funciona com JSON de módulos (nome, pasta, LOC, complexidade) e dependências (grafo orientado). Sempre gera HTML standalone completo. Quando rodada pelo Time Reversa Docs, usa Three.js servido localmente em assets/vendor/ (offline-first).

Uruchamianie i wdrażanie platformy Soneta (enova365, Triva) w kontenerach. Używaj gdy użytkownik: (1) stawia środowisko (server + web) na obrazach Soneta przez `docker compose` albo na Apple `container` / Container Desktop; (2) tworzy bazę danych w kontenerze (usługa init z `dbmgr create`, `--demo`, `--recreate`, licencja, konwersja); (3) wdraża na Kubernetes przez Helm (`helm repo add soneta`, `values.yaml`, `dblist`, `adminMode`); (4) wybiera wersję obrazów (tagi z Docker Hub `soneta/*` lub `registry.soneta.pl`), architekturę (alpine/arm64/amd64), logowanie do registry; (5) potrzebuje SQL Servera — zewnętrznego (`host.docker.internal` / `host.containers.internal`) albo jako kontener `mssql`; (6) rozwiązuje problemy startu stacku (kolejność, port zajęty, brak DNS między usługami w Apple container, zły host-alias). Słowa kluczowe: docker compose, docker-compose.yaml, apple container, Container Desktop, helm, kubernetes, obraz, tag, wersja, mssql, dbmgr, x-init, server.standard, web.standard.

Expert in designing and implementing visceral, satisfying combat systems. Masters hitbox/hurtbox design, frame data, combo systems, enemy archetypes, damage feedback, and the invisible craft that makes players feel powerful. Draws from fighting games, character action games (DMC, Bayonetta), and Souls-like design to create combat that is readable, responsive, and endlessly replayable. Use when "combat system, combat design, hitbox, hurtbox, frame data, hitstop, screen shake, input buffer, coyote time, i-frames, invincibility frames, combo system, attack cancel, recovery frames, punishment window, souls-like combat, action game combat, fighting game, damage feedback, enemy design, boss design, attack telegraph, parry system, stamina system, poise system, weapon feel, game feel combat, melee combat, combat juice, combat, action-game, fighting-game, hitbox, frame-data, game-feel, souls-like, character-action, melee, enemy-design, boss-design, combo, parry, i-frames" mentioned.

Systematic strategic planning framework for philosophy and interdisciplinary academic papers targeting preprint platforms (PhilArchive, arXiv, PhilSci-Archive). Use when users want to: (1) plan a paper on a specific topic, (2) identify research gaps and assess originality, (3) develop optimized paper outlines, (4) prepare for preprint submission, or (5) understand platform requirements and writing standards. Triggered by phrases like 'plan a paper on,' 'help me design a paper about,' 'identify research gaps in,' 'is this idea original,' or when users need structured research planning. The skill guides through three phases: Platform Analysis (identifying target venue and studying sample papers), Theoretical Framework (AI-driven literature search and gap identification), and Outline Optimization (structured design with reviewer-perspective self-assessment). Each phase includes quality evaluation standards and validation checkpoints. Output: optimized detailed outline ready for systematic writing (use with acade

Route web-game auto-playtesting with WAI Play (waiterve/wai-play): decide whether the next move is a testability check, authoring or repairing the `GameFlowAgentAPI` bridge, running a real browser playtest, reading the five-dimension quality report, or unblocking a key node the agent cannot reach. Use when the user wants an AI agent to actually play their HTML5 / canvas / vibe-coded web game and return reproducible evidence, scores, and fix suggestions across the five supported types (survivor-like, arcade shooter, platformer, puzzle/card, visual novel). Triggers on: wai-play, WAI Play, auto-playtest, AI plays my game, web game testing agent, GameFlowAgentAPI, GameFlowIntegration, jumpToScenario, game quality score, playtest evidence. Route Unity/Unreal frame-time work to `game-performance-profiler`, engine build failures to `game-build-log-triage`, human playtest notes to `game-demo-feedback-triage`, and generic browser automation to `browser-harness`.

AI/LLM red-team assessment using the OWASP LLM Top 10 (2025) + OWASP AI Testing Guide (AITG v1, Nov 2025) frameworks, plus OWASP MCP Top 10 runtime testing for agentic/MCP targets. Tests prompt injection, jailbreaks, system prompt leakage, sensitive data extraction, excessive agency, improper output handling, model extraction, content bias, evasion, membership inference, MCP token exposure, MCP command injection, and more. Uses four tools in combination: FuzzyAI (single-turn jailbreak fuzzing), PyRIT (multi-turn orchestrated attacks), Garak (probe-based vulnerability scanning), and promptfoo (plugin-based red-team evaluation). Each tool covers different OWASP categories; running them together gives systematic coverage. Includes a conditional MCP reconnaissance phase and a post-access AI infrastructure phase (chained from /post-exploit). Produces: OWASP LLM Top 10 + AITG + MCP coverage matrix, findings per category, architecture diagram of the AI system, PoCs for confirmed exploits. Chains into /gh-export for

Maintains persistent project context for Business Central AL extensions across sessions, developers, and AI agents. Combines two complementary mechanisms: Architecture Decision Records (ADRs) that capture why technical decisions were made, and Session Handoff documents that capture where the project is right now. Generates, updates, and queries both document types. Use this skill whenever starting a new coding session on an existing project, ending a session and need to document progress, onboarding a new developer or AI agent to an existing codebase, explaining why a technical decision was made, wondering why something is designed a certain way, resuming work after a break, or handing off work between team members. Also trigger when the user says 'document this decision', 'why is this designed like this', 'where did we leave off', 'catch me up', 'what was decided', 'create an ADR', 'end of session', 'handoff', or 'context for next session'.

Build industrial-grade SCADA/HMI dashboards in MATLAB following industrial-HMI conventions (ISA-101-aligned): gray-field philosophy, alarms at source, write safeguards, fixed-range trends, drill-down layout. Produces a real App Designer app (.mlapp, or plain-text .m+.xml on R2026b+) by handing serialization to the matlab-build-app skill when available, and falls back to a programmatic .m app otherwise. Use when wrapping OPC UA / Modbus / MQTT / OSI PI / PI AF monitoring scripts into a live operator app, building plant overviews, designing operator dashboards, or any time a user asks for a "SCADA dashboard", "HMI", "plant dashboard", "operator screen", or "industrial monitoring app" in MATLAB. Trigger on: SCADA, HMI, industrial dashboard, plant overview, operator screen, uigauge, uilamp, alarm banner, gray-field, ISA-101, OPC UA dashboard, setpoint, write safeguards, alarm visualization, OSIsoft PI, AVEVA PI, PI Server, PI Data Archive, PI AF, PI Asset Framework, piclient, afclient.

Expert strategy advisor for products built on LLMs or agents — not general product strategy (see `product-strategy` for that). Use this — proactively and without waiting to be asked — whenever choosing where to apply AI in a product, deciding between RAG and fine-tuning, designing how much autonomy an AI feature should have, evaluating whether an AI feature is actually defensible, or deciding whether to add AI to a feature at all. Also triggers for: "should this be an agent or a simple LLM call", "how much autonomy should this feature have", "RAG vs fine-tuning", "is this AI feature defensible", "our AI feature keeps hallucinating and users don't trust it", "should we build this with AI or just ship it deterministic", "AI product wedge", "what happens to this feature when the models get better", "human-in-the-loop design for AI features". Produces a decision-focused brief: the wedge, the architecture choice, the autonomy level, and the defensibility bet — each with an explicit trade-off.