Results for “trigger-design”

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.

Use to measure whether a support bot, AI agent, or self-service channel actually reduces contact volume, and to audit a vendor's containment or deflection number. Trigger for "what's our real deflection rate", "is the bot working", "our containment rate is 70% but tickets haven't dropped", automation ROI, self-service savings, AI agent resolution rate, or designing a holdout test for a support bot.

Use to draw a defensible sample of support conversations for QA review, an audit or a manual analysis, so the results generalise to the population rather than to whatever was easy to pull. Trigger for "which tickets should we review", "how do we pick a sample", "is our QA sampling representative", stratified or risk-based sampling, review coverage design, or a finding based on a handful of hand-picked tickets.

Anti-slop frontend design skill for building NEW landing pages, portfolios, and UI from a brief. Reads the brief and existing brand context first, infers a design direction from three configurable dials (variance, motion, density), and ships interfaces that don't look templated or default-AI. Use when starting a new page/UI with no existing identity to preserve, or when asked to design something "that doesn't look like every other AI landing page." For auditing and upgrading an EXISTING UI, use `taste-redesign` instead — this skill is for the moment before code exists. Triggers on: "build a landing page", "design a portfolio", "make this not look generic/templated/AI-generated", "anti-slop", starting UI work with a brief but no established identity yet. Also owns component-library selection (which package for toasts, dropdowns, charts, drag-and-drop, state, etc.) — use this instead of hand-rolling a component or guessing at a dependency. Source: github.com/Leonxlnx/taste-skill (taste-skill, the main v2 varian

Create polished HTML technical reports and multi-page documents for consulting deliverables. Includes a customizable design system with dark cover page, warm cream content pages (8.5x11 portrait), professional typography, and a curated component library (KPI cards, data tables with severity coloring, callout boxes, recommendation cards, priority banners). Trigger on report, audit report, technical document, written report, assessment, or multi-page document. Outputs standalone HTML files exported to PDF via Chrome headless.

Use to design and audit how customers are told they are interacting with AI, or that AI was involved in a decision about them, and to evidence that it happened. Trigger for "do we tell customers it's a bot", "AI disclosure requirements", "should the bot say it's not human", "customer asked if they were talking to a person", AI transparency obligations, or evidencing that an AI-assisted decision was explained.

Use this skill whenever the user wants to conduct an event study, create event study plots, test for parallel trends, implement difference-in-differences designs, or work with any panel data estimation that involves pre/post treatment comparisons. Trigger on phrases like "event study", "parallel trends", "pre-trends", "dynamic treatment effects", "leads and lags", "TWFE", "two-way fixed effects", "staggered adoption", "staggered treatment", "difference-in-differences", "DiD", "Sun and Abraham", "Callaway and Sant'Anna", "de Chaisemartin", "Borusyak", "did_multiplegt", "fixest", "did2s", "bacon decomposition", or any reference to plotting coefficients around a treatment event. Also trigger when the user uploads panel data and wants to estimate treatment effects with variation in treatment timing. All code is in R.

Metal/wgpu/WGSL shader surfaces for native Rust gpui apps (Zed-family, pd-console), stockpiled with beautiful copy-pasteable shader-toy examples. Use for custom GPU fragment passes behind/around gpui panes: ocean/water shaders, pixelated waves and boats, a living harbor, dithered chrome borders, sonar sweeps, aurora/starfields, CRT/scanline post. Trigger on: wgsl, wgpu, metal shader, gpui shader, shadertoy, fragment shader, SDF, noise/fbm, ordered dithering, pixelation, render-to-texture, "pixelated waves and boats", living harbor water. NOT for: web/GLSL/three.js shaders (use a web tool), non-shader gpui motion (use rust-gpui-motion), general GUI layout/color (use beautiful-gui-design), CLI/TUI (use beautiful-cli-design).

Capture exact React element context from a live browser UI and hand it to an AI coding agent as component name, source file path, line number, and HTML. Use when the user wants `react-grab`, element-context copy, component-source lookup from the browser, clipboard-to-agent React debugging, or MCP-backed element selection for React apps. Not for generic browser automation or login/session reuse (`browser-harness`, `playwriter`), broad UI annotation/review (`agentation`), React performance audits (`react-best-practices`), or general design-system work (`design-system`). Triggers on: react-grab, grab element context, copy component to AI, browser component picker, React component inspector, clipboard component source, get element context from browser, grab UI element.

Create a detailed, step-by-step implementation plan for a feature or project. Load when the user asks to plan a feature, create a technical roadmap, break down a PRD into tasks, design an implementation strategy, or sequence engineering work. Also triggers on "how should we build this", "implementation plan for", "technical breakdown", "task list for", or any request to turn a high-level requirement into a concrete execution plan. Supports phased rollouts, architecture-first, and MVP-focused planning.

Eng manager-mode plan review. Lock in the execution plan — architecture, data flow, diagrams, edge cases, test coverage, performance. Walks through issues interactively with opinionated recommendations. Use when asked to "review the architecture", "engineering review", or "lock in the plan". Proactively suggest when the user has a plan or design doc and is about to start coding — to catch architecture issues before implementation. (gstack) Voice triggers (speech-to-text aliases): "tech review", "technical review", "plan engineering review".

Build a complete marketing plan covering the full customer journey from stranger to raving fan. Use when the user mentions "marketing plan", "target market", "USP", "lead nurture", "customer lifetime value", "referral program", "marketing strategy", or "PVP Index". Also trigger when building a marketing plan from scratch, choosing acquisition channels, or designing end-to-end customer lifecycle campaigns. Covers the PVP Index, channel selection, and advocacy systems. For brand messaging, see storybrand-messaging. For conversion optimization, see cro-methodology.

Expert knowledge for Azure Functions development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when building trigger/binding workflows, Durable Functions, containerized apps, Flex/Consumption plans, or CI/CD deploys, and other Azure Functions related development tasks. Not for Azure App Service (use azure-app-service), Azure Logic Apps (use azure-logic-apps), Azure Container Apps (use azure-container-apps), Azure Kubernetes Service (AKS) (use azure-kubernetes-service).

Structure software around the Dependency Rule: source code dependencies point inward from frameworks to use cases to entities. Use when the user mentions "architecture layers", "dependency rule", "ports and adapters", "hexagonal architecture", "use case boundary", "onion architecture", "screaming architecture", or "framework independence". Also trigger when decoupling business logic from databases or frameworks, defining module boundaries, or debating where to put business rules. Covers component principles, boundaries, and SOLID. For code quality, see clean-code. For domain modeling, see domain-driven-design.

Deploy with unified build/test/deploy intent across providers using .agent-loom/deploy.yml and per-provider adapters. Load when the user asks to deploy, ship to preview, release to production, or run a provider-agnostic deploy flow. Also triggers on "deploy anywhere", "deploy to Vercel", "deploy with GitHub Actions", "preview deploy", or "ship this". Runs preflight before any deploy — stops on missing secrets. Pairs with ci-cd-and-automation for pipeline design. Ships vercel and github-actions adapters; extensible adapter interface.

Apply named refactoring transformations to improve code structure without changing behavior. Use when the user mentions "refactor this", "code smells", "extract method", "replace conditional", "technical debt", "move method", "inline variable", or "decompose conditional". Also trigger when cleaning up legacy code, preparing code for new features by restructuring, or identifying which transformation to apply to a specific code smell. Covers smell-driven refactoring, safe transformation sequences, and testing guards. For code quality foundations, see clean-code. For managing complexity, see software-design-philosophy.

Universal Reasoning Framework implementing λο.τ calculus over holarchic structures. Provides severity-based routing (R0-R3 pipelines), modular cognitive architecture (DEC, EVL, PAT, SYN, MEA, HYP, INT), fractal execution patterns, multi-level validation (η≥4, KROG), and adaptive learning. Triggers on: (1) complex multi-step reasoning, (2) high-stakes decisions requiring validation, (3) research synthesis across domains, (4) system design and architecture, (5) crisis management, (6) performance optimization. Implements scale-invariant reasoning from micro (tool calls) through meso (skill composition) to macro (orchestrated workflows).

Implement the Entrepreneurial Operating System (EOS) to align vision and execution across a company. Use when the user mentions "EOS", "V/TO", "quarterly rocks", "Level 10 meetings", "accountability chart", "IDS process", "Entrepreneurial Operating System", or "business operating system". Also trigger when a growing company needs meeting structure, goal-setting frameworks, or a systematic approach to solving recurring organizational issues. Covers the six EOS components: Vision, People, Data, Issues, Process, Traction. For team motivation design, see drive-motivation. For lean experimentation, see lean-startup.

Install, configure, and use the Unity Command Line Interface (CLI) for automated production workflows, project management, and cloud integration. Use when setting up CLI-based build automation, CI/CD pipelines, Editor/module management, authentication, or localhost API calls via the experimental Unity Pipeline package. Designed for verifiable, machine-readable game production workflows where the build machine must be describable and tests must return evidence. Triggers on: Unity CLI, unity command line, unity automation, unity ci/cd, unity build script, unity pipeline, unity production workflows.

Use this skill for any creative writing task involving narrative, character, story structure, or franchise development. Triggers: building or tracking characters, designing plots, writing scenes or chapters, organizing a story universe, developing motifs or themes, building a series or franchise bible, tracking character arcs, resolving plot gaps, or any request to write or develop fiction at any scale — from a single moment to a multi-story cluster. This skill operates like a narrative OS: it tracks state, identifies gaps, organizes hierarchy, and generates content that is consistent with the established world.

World-class autonomous sales and revenue skill system. Use ANY time the user asks to sell, pitch, prospect, close, negotiate, launch, monetize, build funnels, write outreach, craft proposals, develop pricing, design offers, write sales copy, create email sequences, plan campaigns, position products, handle objections, create playbooks, build pipeline, forecast revenue, develop GTM strategy, optimize conversions, write ad copy, create sales decks, score leads, nurture prospects, upsell, cross-sell, retain customers, write case studies, price products, structure enterprise deals, create battle cards, script discovery calls, build affiliate programs, plan product launches, create webinar funnels, design pricing pages, A/B test offers, analyze unit economics, calculate LTV/CAC, or ANY other sales, revenue, monetization, go-to-market, or commercial growth task. If it involves selling, revenue, deals, pipeline, or commercial strategy — USE THIS SKILL. Trigger aggressively.

Use this skill whenever the user wants to search for jobs in any location or market, find job listings, or look up a specific job posting — in any country, city, or remotely. Invoke for open positions, vacancies, and hiring across any sector or role (software, data, design, marketing, finance, legal, operations, etc.). The location is always supplied explicitly by the user. Trigger phrases: find a job, job search, search for jobs, job openings, vacancies, hiring, positions open, remote jobs, "are there any X jobs in <place>", look up this job posting.

Drive Airship (`@airshiplabs/cli`), a CLI that puts a visual, pannable design canvas in front of a running dev server so an engineer can click an element, describe a change, and have Claude Code, Codex, or OpenCode edit the underlying source file directly — no plugin, no build config, nothing added to the project. Use when the user wants to launch a visual editor over a local dev server (`npx @airshiplabs/cli --target <port>`), pick or compare coding agents (`--agent claude|codex|opencode`) for live UI edits, sandbox agent edits with `--safe`, scaffold an `airship.config.json` (`airship init`), or diagnose a broken setup (`airship doctor`). Triggers on: "airship", "@airshiplabs/cli", "visual editor for my codebase", "click to edit UI", "design canvas over dev server", "airship doctor", "airship init", "pair Claude Code with a visual editor on localhost".

Submits and tracks protein-testing experiments on the Adaptyv Bio Foundry cloud lab (wet-lab validation), and optimizes protein sequences before submission with computational tools (NetSolP, SoluProt, SolubleMPNN, ESM). Use when designing proteins that need wet-lab validation - binding/affinity screening, expression testing, thermostability, or fluorescence assays - or when submitting experiments to the Foundry API, browsing the target catalog, tracking experiment status, retrieving results, or pre-screening sequences for solubility/expression. Triggers on "Adaptyv", "Foundry API", "cloud lab", "biolayer interferometry / BLI", "wet-lab validation". Part of the AlterLab Academic Skills suite.

Interview the user with 10-15 targeted questions BEFORE building anything, then confirm a short spec, then build. Use this skill whenever the user asks Claude to build, create, make, code, design, draft, or generate anything non-trivial — an app, website, script, tool, document, presentation, campaign, plan, or system — even if the request looks detailed enough to start immediately. Especially trigger on vague one-line build requests like "make me a dashboard" or "build an app for my gym". Do NOT use for pure questions, explanations, debugging existing code, tiny edits, or reformatting.

Design the architecture for a multi-command skill system — the shared state schema, the command map, the router, and the boundary each command stops at — before any skill files get written. Use when the user wants to turn one skill into a system, combine several related or overlapping skills into a single coherent one, build a skill with subcommands (like `/thing audit`, `/thing polish`), design a suite, family, or collection of skills, or asks "how should I structure this skill set". Also trigger on "skill system", "skill architecture", "multi-command skill", "system of skills", "make these skills work together", "skill with slash commands", or complaints that their skills overlap, collide, duplicate each other, or never fire. Produces an architecture spec only — it never writes SKILL.md or reference files. Hand the finished spec to skill-creator to build from.

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 LangSmith work into one workflow packet before touching SDK code. Use when the user needs LangSmith tracing, offline evals, annotation/review queues, prompt-registry decisions, audit/gap review, or cross-service trace propagation for an LLM app or agent workflow. Choose one packet: trace-debug, eval, review, prompt-registry, propagation, or audit. Triggers on: LangSmith, LangChain tracing, `@traceable` / `traceable`, `wrap_openai` / `wrapOpenAI`, datasets, experiments, annotation queues, feedback criteria, Prompt Hub, run trees, trace IDs, or production confidence for an AI feature. Not for generic SLO/alert design, non-LangSmith deployment orchestration, or runtime guardrails outside LangSmith.

Turn "what should we pay reps and what number do we give them" into a defensible comp and quota plan. Reconciles a top-down number against a bottom-up build, sets the OTE mix, lays out accelerators, and names the behaviors the plan will drive and the ones it will break. Built for B2B sales leaders, customizable to your motion and your segments. Trigger on "design the comp plan", "set quotas", "what OTE should I pay", "reconcile top-down and bottom-up", "what will this plan actually incentivize", or any comp or quota build.

Auto-review pipeline — reads the full CEO, design, eng, and DX review skills from disk and runs them sequentially with auto-decisions using 6 decision principles. Surfaces taste decisions (close approaches, borderline scope, codex disagreements) at a final approval gate. One command, fully reviewed plan out. Use when asked to "auto review", "autoplan", "run all reviews", "review this plan automatically", or "make the decisions for me". Proactively suggest when the user has a plan file and wants to run the full review gauntlet without answering 15-30 intermediate questions. (gstack) Voice triggers (speech-to-text aliases): "auto plan", "automatic review".

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.

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.

Plan a field event, dinner, or conference presence that actually produces pipeline, not just badges scanned. It builds the right guest list, designs a draw worth showing up for, plans the on-site motion so conversations happen, and writes the follow-up that converts, because the pipeline is made after the event, not at it. Built for B2B field marketing and sales teams, customizable to your CRM and your event motion. Trigger on "plan a field event", "customer dinner plan", "conference presence", "how do I get pipeline from this event", "event guest list", "event follow-up plan", or any field-event or in-person pipeline request.

Use this skill whenever LMX is used, produced, reviewed, migrated, or modified. This includes composing campaigns, loops, lifecycle emails, or email-message bodies for the Loops editor or Content API. LMX (Loops Markup Language) is the format used for Loops email content. Trigger on phrases like "create a campaign", "generate an email", "write a welcome email", "draft a lifecycle email", "build an email template", "create an onboarding email", "copy this into LMX", "migrate this email", "convert this email to LMX", "design a new Loops email", "use imagegen for a Loops email", "use gpt-image for an LMX reference", "visual reference for a Loops email", "LMX", "Loops email", or any request to produce, copy, migrate, convert, review, or modify email body content intended for Loops. For net-new emails or major visual redesigns, follow this skill's Net-New Email Design Flow before generating or sourcing new visual assets. Source copy, existing HTML, MJML, Markdown, screenshots, and migration instructions do not byp

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.