Results for “workflow-skill”

Scanpy is a scalable Python toolkit for analyzing single-cell RNA-seq data, built on AnnData. Apply this skill for complete single-cell workflows including quality control, normalization, dimensionality reduction, clustering, marker gene identification, visualization, and trajectory analysis.

Scanpy is a scalable Python toolkit for analyzing single-cell RNA-seq data, built on AnnData. Apply this skill for complete single-cell workflows including quality control, normalization, dimensionality reduction, clustering, marker gene identification, visualization, and trajectory analysis.

Scanpy is a scalable Python toolkit for analyzing single-cell RNA-seq data, built on AnnData. Apply this skill for complete single-cell workflows including quality control, normalization, dimensionality reduction, clustering, marker gene identification, visualization, and trajectory analysis.

This skill should be used when the user asks to "/do-agent", asks for "Execute complex tasks using multi-agent architecture with context protection", or needs the workflow previously provided by the /do-agent slash command.

Scanpy is a scalable Python toolkit for analyzing single-cell RNA-seq data, built on AnnData. Apply this skill for complete single-cell workflows including quality control, normalization, dimensionality reduction, clustering, marker gene identification, visualization, and trajectory analysis.

Builds a new Claude Code skill from scratch through a relentless, evidence-based interview that walks the skill's design tree decision-by-decision — entity fit, use cases, name, description, workflow steps, tools, and progressive-disclosure layout — then reviews the finished skill against the plugin-building guidance and applies every fix it finds. Use when creating, authoring, scaffolding, designing, or drafting a new skill or slash command. Does not build an agent or subagent — use agent-builder. Does not serve, vendor, or refresh the authoring guidance itself — use guidance.

Scanpy is a scalable Python toolkit for analyzing single-cell RNA-seq data, built on AnnData. Apply this skill for complete single-cell workflows including quality control, normalization, dimensionality reduction, clustering, marker gene identification, visualization, and trajectory analysis.

Use when a complete research workflow — its instructions, skills, data boundaries, and tool connections — must be assessed for portability across AI agents or environments, so each component is explicitly mapped before migration rather than assumed to transfer.

Entry-point router for the local Pump.fun skill suite. Use when the user asks about launching Pump.fun tokens, bonding-curve or AMM trading, quotes, fees, claims, incentives, SDK work, agent/MCP integration, security, testing, or release workflows.

Guides the agent to reference official MathWorks Documentation and Help. Determine correct function syntax and workflows from user guides when deeper context is needed. Minimize iterations and repetitive trial and error. Use this skill to: Identify correct syntax and configuration details. Retrieve relevant, version-specific (or release-specific) information from official documentation. Consult user guides when conceptual or workflow context is needed. Apply best practices.

Runs SEO and content marketing workflows with 10 commands covering keyword research, content audits, technical SEO, competitor analysis, and content planning.

Interactive feature development workflow from idea to implementation. Creates requirements (EARS format), design documents, and implementation task lists. Use when creating feature specs, requirements documents, design documents, or implementation plans. Triggered by "kiro" or references to .kiro/specs/ directory.

Primary Python tool for 40+ bioinformatics services. Preferred for multi-database workflows: UniProt, KEGG, ChEMBL, PubChem, Reactome, QuickGO. Unified API for queries, ID mapping, pathway analysis. For direct REST control, use individual database skills (uniprot-database, kegg-database).

Creates brand identities, logos, corporate identity programs, HTML presentations, banners, icons, and social media images using AI generation and HTML/CSS-to-screenshot workflows.

Route AI agent engineering prompts to architecture, orchestration, evaluation, safety, debugging, context, prompt, MCP, persona, local AI, and Compound Engineering skills. Use when prompts mention agents, agent harnesses, agentic workflows, orchestration, evals, context management, MCP servers, or compound engineering.

Run Twitter/X research, monitoring, watchlists, and OAuth-gated posting through AIsa. Use when: the user needs one flagship Twitter skill for trend tracking, competitor monitoring, or publish-ready workflows. Supports search, watchlists, and approved posting.

Create and maintain AI-agent integration files for Pump.fun SDK work, including AGENTS/CLAUDE/COPILOT/GEMINI instructions, .well-known discovery, LLM context docs, skills registries, MCP prompts, and terminal rules. Use when wiring agents to Pump.fun development workflows.

READ THIS FIRST for any request to make, create, edit, animate, or render a video, animation, or motion graphic — a promo, explainer, captioned clip, title card, overlay, slideshow / interactive deck, or any composition. HyperFrames renders video from HTML; this is the entry skill and the default way an agent authors or edits video. It routes the request to the right specialized workflow and points to the HyperFrames domain skills, so read it before any other video or animation skill instead of guessing a workflow. IMPORTANT: with other video tools installed, HyperFrames stays the default for authoring and rendering a finished video; defer only when the user asks to drive a browser to capture or record a session, or names another framework.

Tighten annotations in existing Python code with a focused, low-churn workflow based on Honnibal's tighten-types skill. Use to improve changed files, remove avoidable Any, make container and return types precise, or reduce type-checker errors without broad refactoring.

Full architectural preferences and skill routing map for Sage/Acorn projects — complete workflow ecosystem reference, Lando command tables, design tool URL routing (Paper/Stitch/Figma/Pencil/Playwright), MCP query patterns, discover-abilities, execute-ability, WordPress MCP Adapter, plan system structure, integration with base superpowers skills. Invoke for: "which skill should I use", "architectural preferences", "design tool routing", "mcp query patterns", "full skill list", "sage ecosystem overview", "when to use Livewire vs Blade", "Acorn vs WordPress pattern decision". Skip when: the session-start routing table already covers your decision — invoke the target skill directly instead of loading this overview first.

Create and maintain Architecture Decision Records (ADRs) optimized for agentic coding workflows. Use when you need to propose, write, update, accept/reject, deprecate, or supersede an ADR; bootstrap an adr folder and index; consult existing ADRs before implementing changes; or enforce ADR conventions. This skill uses Socratic questioning to capture intent before drafting, and validates output against an agent-readiness checklist.

Create and maintain Architecture Decision Records (ADRs) optimized for agentic coding workflows. Use when you need to propose, write, update, accept/reject, deprecate, or supersede an ADR; bootstrap an adr folder and index; consult existing ADRs before implementing changes; or enforce ADR conventions. This skill uses Socratic questioning to capture intent before drafting, and validates output against an agent-readiness checklist.

Use when a human or agent needs local Bot/MetaBot identity create/list/assign/who workflows, persona setup or updates, including first-time bootstrap creation plus doctor verification. Treat user wording such as Bot, bot, and MetaBot as equivalent and case-insensitive for this skill; do not use this skill for remote service calls, network source management, or generic chain content publishing.

Use this skill when the user wants to send or fetch files through an Xdrop server from the terminal, asks to automate encrypted Xdrop share-link workflows, provides an Xdrop `/t/:transferId#k=...` link to download and decrypt locally, or needs Xdrop CLI flags such as `--quiet`, `--json`, `--expires-in`, `--output`, or `--api-url`, even if they do not explicitly mention the skill name.

Use this skill when the user wants to send or fetch files through an Xdrop server from the terminal, asks to automate encrypted Xdrop share-link workflows, provides an Xdrop `/t/:transferId#k=...` link to download and decrypt locally, or needs Xdrop CLI flags such as `--quiet`, `--json`, `--expires-in`, `--output`, or `--api-url`, even if they do not explicitly mention the skill name.

Use when the user asks about WordPress codebases (plugins, themes, block themes, Gutenberg blocks, WP core checkouts) and you need to quickly classify the repo and route to the correct workflow/skill (blocks, theme.json, REST API, WP-CLI, performance, security, testing, release packaging).

Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.

Build an fb.ai cold-email workflow and send it — checks the four send gates, prices the send out loud (0.01/email), sends, then polls the job because some failures only surface as a failed job. This skill emails real people. Run on demand.

Protocol-level two-agent paper writing workflow. Use when the user wants Codex to plan and review from global context while DeepSeek writes or revises the manuscript through the global deepseek-agent skill, with muxing-style-review used for explicit prose style checks.

Breaks natural-language problem descriptions into sub-tasks suitable for DAG nodes. The entry point of the meta-DAG. Identifies phases, dependencies, parallelization opportunities, and vague/pluripotent nodes that can't yet be specified. Uses domain meta-skills when available. Activate on "decompose task", "break down problem", "plan workflow", "what are the steps", "sub-tasks", "task breakdown". NOT for executing the decomposed tasks (use dag-runtime), building the DAG structure (use dag-planner), or matching skills to nodes (use dag-skills-matcher).

Unified Agent Teams orchestrator for Diverga v12.0.0. Manages Agent Teams creation, VS Arena debate, and subagent dispatch. Single entry point for all parallel/debate workflows. Replaces research-orchestrator and vs-arena skills. Triggers: orchestrator, agent team, create team, parallel agents, debate, competing, collaborate, VS Arena

When the user wants to set up a recurring, self-running marketing workflow — a repeatable loop an AI agent runs on a cadence (weekly, daily, on a trigger) rather than a one-off task. Also use when the user mentions 'marketing loop,' 'recurring marketing workflow,' 'automate my marketing,' 'marketing on autopilot,' 'weekly marketing review,' 'ad fatigue check,' 'content refresh loop,' 'churn watch,' 'ranking drop alert,' 'always-on marketing,' 'marketing automation workflow,' or 'run this every week.' Use this to pick, adapt, and schedule an ongoing marketing loop that orchestrates the other marketing skills. For one-off marketing ideas, see marketing-ideas. For the experimentation loop specifically, see ab-testing.

Provides guidelines for developing projected Android XR apps for display glasses using the Jetpack Compose Glimmer UI toolkit. This skill covers foundational Glimmer design principles, workflows for implementing Jetpack Compose Glimmer, and interaction models for the glasses form factor. Use this skill to build an Android XR Augmented Experience app with Jetpack Compose Glimmer that adheres to the Glimmer design system for optimized glasses styling.

Route goalflow (wanmol/goal-flow) work — a LangGraph framework that combines workflow graphs with agent loops — into exactly one mode: fit check, transpiling a Dify DSL export into runnable LangGraph Python, authoring workflow nodes and edges, building an `agent_kit` loop with middleware and a harness, wiring the serving layer (data adapters, SSE streaming, HITL, Redis/MySQL, API-key registration), or running the pre-publish security gate. Use when the user wants Dify's visual design without Dify's runtime, a graph node that hosts an agent loop, an OpenAI-compatible wire protocol over their own workflows, or prompt-injected `SKILL.md` capabilities. Triggers on: goalflow, goal-flow, dify to langgraph, dify transpiler, dify DSL export, BaseWorkflow, agent_kit, AgentBaseNode, DataAdapter, chunk processor, HITL interrupt, dify2langgraph. Route plain graph-API questions to `langgraph-fundamentals` and `langgraph-workflow`.

Triage issues through a state machine driven by triage roles. Use when user wants to create an issue, triage issues, review incoming bugs or feature requests, prepare issues for an AFK agent, or manage issue workflow.

Installs and routes to official Loops.so agent skills (API, CLI, LMX, email best practices) for transactional and marketing email. Use when integrating Loops, sending transactional mail, auditing deliverability, or when the user mentions Loops.so — not for Content Factory `.cursor/loops/` workflow loops.