Generates complete two-sample Mendelian randomization (MR) research designs from a user-provided research direction. Use when users want to design, plan, or build a study using two-sample MR to test causal relationships. Triggers:"design a two-sample MR study", "build a publishable MR paper", "test whether this biomarker causally affects this disease", "generate Lite/Standard/Advanced MR plans", "screen multiple exposures with MR", "bidirectional MR design", "causal inference using GWAS summary statistics", or "I want to study X and Y using MR". Always outputs four workload configurations (Lite / Standard / Advanced / Publication+) with a recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, and publication upgrade path.

UiPath Integration Service connector authoring (REST+JSON) on disk via `uip is connectors builder`. Triggers on `element.json`, `element-metadata.json`, `standard-resources/*.json`, a `periodic-uipath-*`/`periodic-design-*` connector repo, or any request to build/edit an IS connector: init the connector shell, configure auth (19 types via `auth set`, incl. jwtOauth claims, FPS, none), add activities with fields/params/methods/hooks, wire polling/webhook triggers, add auth-system resources, inspect, validate, and surgically read/write files via state. Import + publish live on the parent `uip is connectors` (need `uip login`). NOT for operating a published connector — connections, ping, run an activity→uipath-platform; NOT for `.flow` connector nodes→uipath-maestro-flow.

TRIGGER for authoring, operating or diagnosing UiPath Maestro BPMN. Author `<Name>.bpmn.ts` with the TypeScript builder SDK (`@uipath/maestro-builder-sdk/bpmn`) and run the `uip maestro bpmn` check/compile/format/validate loop. Operate: refresh package metadata, pack, upload to Studio Web, publish, debug a real run, run a deployed process, inspect jobs and instances, pause/resume/cancel/retry/migrate. Diagnose a failed or stuck run: job status, incidents, runtime variables, the deployed BPMN asset, element executions, cursors, traces. Covers events, gateways, tasks, sub-processes, sequence flows, bindings, static rules, semantic `.bpmn` output, boundary handlers and branching written by nesting, event sub-processes, Data Fabric (Data Service) record and file operations through `.dataService()`, and any registry-backed extension type through `.activity()` — including one the SDK ships no typed method for. Flow builder authoring → uipath-maestro-flow; case plans → uipath-maestro-case.

[BETA] Expert assistance for the Adobe I/O CLI plugin `@adobe/aio-cli-plugin-aem-rde` — the `aio aem rde` / `aio aem:rde` command tree used to deploy, inspect, log-tail, snapshot, and troubleshoot AEM Rapid Development Environments (RDEs). Activate ONLY when the user explicitly references RDE concepts: 'AEM RDE', 'Rapid Development Environment', `aio aem rde`, `aio aem:rde`, `aem-rde`, RDE snapshots, RDE deploy/install, `rde install`, `rde inspect`, `rde status`, `rde history`, `rde reset`, the `@adobe/aio-cli-plugin-aem-rde` package, or Cloud Manager program/environment configuration that is specifically for an RDE environment. Do NOT activate on generic AEMaaCS phrases like 'deploy to AEM Cloud', 'push my bundle', 'tail the publish log', 'cloud sandbox', or unqualified 'dispatcher-config / frontend / env-config deployments' — those belong to Cloud Manager pipelines, not RDE. This skill is in beta. Verify all outputs before applying them to production projects.

Load before calling data-tables or parse-file. Use for natural standalone requests like "what data tables do I have?", "show/list my tables", or "what columns are in this table?", and whenever the user asks to list, show, create, inspect, import, seed, query, update, clean up, rename columns in, or delete data tables and rows, especially from CSV/XLSX/JSON attachments. Also load before building or planning workflows that create or write to Data Tables (then load workflow-builder before build-workflow).

Integrate an application's backend with FastClaw cloud agents over the FastClaw API. Use when a project needs to chat with one or more existing FastClaw agents (agent-scope API key), or to create, update, delete and chat with agents on demand (user-scope API key) — e.g. "connect this app to FastClaw", "call my FastClaw agent from the backend", "let each user create their own agent".

Reviews GitHub pull requests through specialist review agents, runs repository validation, verifies corrections, aggregates findings, and posts the result. Scales reviewers to risk by default (code review alone for small or mechanical changes; code, seam, and simplification review for contracts, state, and security); the operator can add scopes or explicitly request only selected scopes. Use when the operator asks to review a PR, re-review fixes, check whether a PR is ready to merge, run review agents, or invokes $prp-review.

Azure AI Content Safety SDK for Python. Use for detecting harmful content in text and images with multi-severity classification.

Azure Batch SDK for Java. Run large-scale parallel and HPC batch jobs with pools, jobs, tasks, and compute nodes.

Automate ActiveCampaign tasks via Rube MCP (Composio): manage contacts, tags, list subscriptions, automation enrollment, and tasks. Always search tools first for current schemas.

角色动态 sprite / 桌宠皮肤 / atlas 状态行生产线：母图锚身份 → Frame A 首帧 → AI 视频生成（人工环节）→ 截帧确定性加工 → atlas row + 三道闸。 Use when: 做桌宠皮肤、给猫补新状态动画行、visible-cafe/F258 猫形象素材、 角色小循环动画（呼吸/走路/睡觉/挥手级）、把静态角色"做成动态的"。 Not for: 动画短片成片（用 anime-forge）、录屏/教程视频（用 video-forge）、 单张静态图/头像（用 image-generation）、UI 图标。 Output: 192×208 atlas row strip + QA 包（contact/GIF/provenance）→ 过三道闸 + operator identity veto 后接 pet.json。

Use when designing, reviewing, or paying for an agent memory system — adding memory to an agent, choosing between long-context / RAG / graph / agentic memory, auditing what a CLAUDE.md or memory directory actually holds, deciding what to keep and what to expire, or when a memory store keeps growing and nobody has said what leaves it. Prices the write path, picks which cost to pay, classifies records as facts / skills / logs, and refuses a design that has no forgetting policy.

Use when defining, reviewing, or operating SLOs/SLIs/error budgets. Triggers on "define an SLO", "what should our SLO be", "error budget", "burn rate", "SLI", "service level objective", "Google SRE workbook", "multi-window burn-rate alert", or any reliability-target question. Ships SLO designer, error-budget calculator with multi-window burn-rate thresholds, and SLO reviewer that catches the common bugs (target too aggressive, window too short, conflicting SLOs, no SLI definition). 4 references on SLO principles + SLI design + error budget math + composition with feature-flags-architect/chaos-engineering/kubernetes-operator. NOT a generic observability skill — specifically the SLO discipline.

Router/index for the 2 finance skills bundled in this plugin: financial-analyst (ratio analysis, DCF valuation, budget variance, rolling forecasts) and saas-metrics-coach (ARR/MRR, churn, CAC/LTV, NRR, quick ratio). Use when a finance request doesn't obviously match one skill and you need to pick the right one (e.g., 'analyze these financials', 'how healthy are my SaaS metrics').

Use when planning, running, or learning from chaos engineering experiments. Triggers on "chaos experiment", "fault injection", "gameday", "resilience test", "blast radius", "steady state", "abort criteria", "Chaos Toolkit", "Chaos Mesh", "Litmus", "Gremlin", "AWS FIS", or any deliberate failure-injection question. Ships experiment designer, blast-radius calculator, and postmortem generator (all stdlib Python), 4 references on chaos principles + experiment design + attack taxonomy + tooling landscape, and a /chaos-experiment slash command. Composes with feature-flags-architect (kill switches as abort triggers) and kubernetes-operator (common chaos targets).

Use when coordinating product work across the 12 bundled product sub-skills (RICE, OKRs, UX research, design tokens, competitive teardown, analytics, experiments, discovery, roadmaps, spec-to-repo, landing pages, SaaS scaffolding) or the 4 standalone product-team plugins (user stories, Apple HIG, code-to-PRD, research summarizer). Triggers on 'help me prioritize', 'plan a product experiment', 'we ship features nobody uses', 'run the discovery loop', 'is our OST sound'. Forks context to route to one sub-skill via a deterministic signal router and returns a digest; can also drive a continuous-discovery loop (Torres cadence tracker + OST linter as machine gates) or a full goal→plan→execute→verify→close run through the repo-wide agent-harness. Distinct from project-management (how to deliver vs what to build), marketing/landing (from-scratch pages), and engineering/agent-harness (the generic loop engine this orchestrator plugs into).

Design and write deterministic multi-agent workflow scripts (.js files in .claude/workflows/) for Claude Code's Workflow tool. Use when a user wants to build, create, author, scaffold, or run a custom Claude Code workflow, orchestrate sub-agents (fan-out, pipeline, loop, judge-panel), or automate a repeatable multi-step task across fresh-context agents.

Use when the user wants their agent setup graded from real conversation history, asks which installed skills are actually working, or wants evidence-backed skill edits — scores recent local Claude Code / Codex sessions against efficiency and code-quality rubrics, then drafts skill changes gated by a deterministic aggregator and renders one local shareable report.

Use when someone wants to decide whether a meeting is worth calling, price a meeting in dollars, build a timeboxed agenda with desired outcomes, or turn messy meeting notes into owned action items — or says "should this be a meeting", "/cs:meeting-prep", or "/cs:meeting-actions". Runs a cost gate (ASYNC / NOT-READY / MEET), builds a decision-first agenda, and extracts an owner + due-date checklist that flags every orphan.

Use when the user asks to design a RAG pipeline, choose a chunking strategy or embedding model, pick a vector database, or evaluate retrieval quality (precision@k, recall@k, NDCG). Examples: 'design a RAG system for our docs', 'what chunk size should I use for this corpus', 'evaluate my retriever against ground truth'. NOT for general LLM cost tuning (use llm-cost-optimizer) or agent loops over retrieval (use agenthub).