Skills
98,067 packaged capabilities and playbooks for agents. Install any of them with one command.
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Manage projects, tasks, and priorities effectively as a solopreneur. Use when organizing work, tracking progress, managing deadlines, coordinating with contractors or clients, or building project management systems. Covers task management methods, prioritization frameworks, project planning templates, tool selection, and personal productivity workflows. Trigger on "project management", "manage my work", "organize tasks", "project planning", "task management", "prioritization", "stay organized".
Manages a GitHub Project board + Issues as the source of truth for product backlog, tasks, stories, and bugs. Provides five flows: bootstrap (/track-init), plan an epic with hierarchy (/plan-epic), migrate flat boards to a hierarchy (/plan-migrate), track ad-hoc work (/track), and query (/backlog). Uses gh CLI for project operations and MCP tools for issue creation. Trigger: /track-init, /plan-epic <description>, /plan-migrate, /track <description>, /backlog, "qué tenemos pendiente?", "what's pending?", "acordate que hay que...", "remember to...", or when SDD generates tasks via sdd-tasks.
The meta-skill that powers all other AI tools. Prompt engineering for creative applications is the art and science of communicating with AI models to produce exactly what you envision—in images, video, audio, and text. This isn't just "write better prompts." It's understanding how different models interpret language, how to structure requests for different modalities, how to iterate systematically, and how to build prompt libraries that encode your creative vision. The best prompt engineers have developed intuition for what words trigger what responses in each model. This skill is foundational—it amplifies the effectiveness of every other AI creative skill. Master this, and you master the interface to all AI creation. Use when "prompt, prompting, prompt engineering, better prompts, prompt optimization, how to prompt, prompt strategy, prompt library, prompt template, make AI understand, prompt-engineering, prompting, meta-skill, ai-creative, foundational, optimization, iteration" mentioned.
Master prompt engineering for AI models: LLMs, image generators, video models. Techniques: chain-of-thought, few-shot, system prompts, negative prompts. Models: Claude, GPT-4, Gemini, FLUX, Veo, Stable Diffusion prompting. Use for: better AI outputs, consistent results, complex tasks, optimization. Triggers: prompt engineering, how to prompt, better prompts, prompt tips, prompting guide, llm prompting, image prompt, ai prompting, prompt optimization, prompt template, prompt structure, effective prompts, prompt techniques
Indirect prompt injection attacks against AI/LLM. Use when the target AI system processes external data sources (web pages, documents, emails, databases, API responses). Covers techniques such as indirect injection, tool chain hijacking, RAG poisoning and data exfiltration. The #1 vulnerability category in the OWASP LLM Top 10
Techniques for leaking AI/LLM system prompts. Use when the target is an LLM-based application and you want to obtain its system prompt, configuration information, tool list or knowledge base contents. A key first step for AI Bug Bounty, red team assessments and competitive analysis
Build, debug, or plan work with The Prompting Company through its API, MCP Server, CLI, or SDK entrypoints. Use when the user needs public routes, OpenAPI schema guidance, TypeScript SDK integration, CLI workflows, MCP setup, content APIs, app publishing APIs, public markdown access, simulations, visibility analytics, authentication, or API key scopes.
End-to-end guidance for protein design pipelines. Use this skill when: (1) Starting a new protein design project, (2) Need step-by-step workflow guidance, (3) Understanding the full design pipeline, (4) Planning compute resources and timelines, (5) Integrating multiple design tools. For tool selection, use binder-design. For QC thresholds, use protein-qc.
Design protein sequences using ProteinMPNN inverse folding. Use this skill when: (1) Designing sequences for RFdiffusion backbones, (2) Redesigning existing protein sequences, (3) Fixing specific residues while designing others, (4) Optimizing sequences for expression or stability, (5) Multi-state or negative design. For backbone generation, use rfdiffusion or bindcraft. For ligand-aware design, use ligandmpnn. For solubility optimization, use solublempnn.
Build a throwaway prototype to answer one design, logic, data-model, state-machine, or UI question before committing to production code. Use when the user asks to prototype, sanity-check an approach, mock up a UI, explore design options, compare flows, or reduce uncertainty with disposable code.
Use this skill for reinforcement learning tasks, including high-performance RL training, custom environment development, vectorized parallel simulation, multi-agent systems, or integration with existing RL environments (Gymnasium, PettingZoo, Atari, Procgen, etc.). Use it to implement PPO training, create PufferEnv environments, optimize RL performance, or develop policies with CNNs/LSTMs.
Automation tool for publishing content to WeChat Official Accounts. Core features: - Image-text posts: multi-image with captions, supports up to 9 images - Article posts: full Markdown formatting, preserving styles and layout - Automated flow: automatic login, filling and publishing via Chrome CDP - Smart compression: automatic compression when title or content exceeds limits - Theme support: default, grace and simple themes for article posts When to use: - The user explicitly asks to "publish to WeChat Official Account" or "send to the Official Account" - The user provides a markdown file or content that needs publishing - The user needs to publish image-text content in bulk When not to use: - The user is only asking how to publish (just give advice) - The user needs to edit an existing Official Account article (manual work) - The user needs to publish to another platform (use that platform's tool)
Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.
Python interface to OpenMS for mass spectrometry data analysis. Use for LC-MS/MS proteomics and metabolomics workflows including file handling (mzML, mzXML, mzTab, FASTA, pepXML, protXML, mzIdentML), signal processing, feature detection, peptide identification, and quantitative analysis. Apply when working with mass spectrometry data, analyzing proteomics experiments, or processing metabolomics datasets
Expert Python backend code reviewer that identifies over-complexity, duplicates, bad optimizations, and violations of best practices. Use when asked to review Python code quality, check for duplicate code, analyze module complexity, optimize backend code, identify anti-patterns, or ensure adherence to best practices. Ideal for preventing AI-generated code from creating unnecessary files instead of imports, finding repeated validation logic, and catching over-engineered solutions.
Qdrant vector database: collections, points, payload filtering, indexing, quantization, snapshots, and Docker/Kubernetes deployment. Use when managing Qdrant collections, performing vector searches with payload filters, configuring HNSW indexes or quantization, or deploying Qdrant clusters. Keywords: Qdrant, vector database, HNSW, quantization, semantic search.
Generate images with Alibaba Qwen-Image-2.0-Pro via inference.sh CLI. Professional text rendering, fine-grained realism, enhanced semantic adherence. Ideal for posters, banners, and text-heavy designs. Triggers: qwen image pro, qwen-image-pro, qwen 2 pro, alibaba image pro, dashscope pro, professional text rendering
Execute prompts as the Qwen seat through the Pi CLI on OpenRouter, with Qwen3.8 27B pinned per invocation (qwen/qwen3.8-2.4t-a95b, 256K-token context, the open-weight VLM of the Qwen3.8 Max family). Use when users explicitly request Qwen execution, when a workflow needs an independent Qwen seat, or when a cross-runner workflow wants a Qwen-backed perspective without leaving the current workspace.
Ultimate RabbitMQ expertise skill for production-grade message broker architecture, implementation, and operations. Top 0.01% knowledge covering: (1) Advanced messaging patterns - Dead Letter Exchanges, Delayed Messages, Priority Queues, Consistent Hash Exchange, Sharding, (2) High Availability - Clustering, Quorum Queues, Stream Queues, Federation, Shovel, (3) Performance Engineering - prefetch tuning, connection pooling, batch publishing, memory optimization, flow control, (4) Security - TLS/mTLS, OAuth2, LDAP, certificate rotation, (5) Monitoring - Prometheus metrics, custom health checks, anomaly detection, (6) Troubleshooting - memory alarms, network partitions, queue backlogs, consumer starvation, (7) Multi-tenancy - vhost design, resource limits, isolation patterns, (8) Event-driven architectures - CQRS, Event Sourcing, Saga patterns with RabbitMQ. Use when: building messaging systems, debugging RabbitMQ issues, optimizing performance, designing HA architectures, implementing advanced patterns, production hardening, capacity planning, migration strategies.
Inspect an agent codebase and write a markdown plan wiring Langfuse tracing/observability (session/trace boundaries, sub-agent handoffs, tool wrapping, naming/tagging vocabulary) plus a Ratel-value/agent-health dashboard build-spec. Use to instrument Langfuse, wire up tracing, set up dashboards, decide where Langfuse goes or what to put on the board, onboard a just-signed partner, or `/ratel-langfuse-integrate`. Writes to <repo>/.ratel/; never edits code or calls the Langfuse API. The Langfuse branch of /ratel-observability-assessment; redirects if the vendor is LangSmith; routes to /ratel-langfuse-analyze once traces flow.
Read a customer's live LangSmith runs, traces, threads, and feedback scores via the LangSmith MCP server (or langsmith SDK fallback), pattern-match against the shared finding catalog, and write a findings report split into Ratel-flavored improvements and stack-agnostic low-hanging fruit. Use to scan the LangSmith dashboard, dig into the run tree or failed runs, review this week's runs, audit their observability, see what the agent's doing wrong, or `/ratel-langsmith-analyze`. Writes to <repo>/.ratel/ (accumulates); fails fast without a reachable server or API key, honest-skips clean data, never edits code. Follow-up to /ratel-langsmith-integrate.
Inspect an agent codebase and write a markdown plan wiring LangSmith tracing/observability (run-tree boundaries, sub-agent handoffs, tool wrapping, run-naming/metadata vocabulary) plus a Ratel-value/agent-health dashboard build-spec. Use to instrument LangSmith, add @traceable/wrap_openai coverage, group runs into threads, design dashboards or Monitor charts, decide where LangSmith goes, onboard a just-signed partner, or `/ratel-langsmith-integrate`. Writes to <repo>/.ratel/; never edits code or calls the LangSmith API. The LangSmith branch of /ratel-observability-assessment; redirects if the vendor is Langfuse; routes to /ratel-langsmith-analyze once traces flow.
Create or update component documentation in Sentry's MDX stories format. Use when asked to "document a component", "add stories", "write component docs", "create an mdx file", "add a stories.mdx", or document a design system component. Generates structured MDX with live demos, accessibility guidance, and auto-generated API docs from TypeScript types.
Use when choosing a React component pattern — custom hooks, control props, compound components, headless components, render props, container/presentational, or other architectural patterns. Includes 13 patterns with decision guide and 2025 popularity ranking.
End-to-end real estate investment analysis skill. Use when users ask to analyze a property deal, run the numbers on a rental, evaluate real estate investments, build a pro forma, compare markets, calculate cap rate, cash-on-cash return, IRR, NOI, DSCR, equity multiple, or GRM. Also triggers on: BRRRR analysis, house hack evaluation, short-term rental (STR/Airbnb) analysis, commercial underwriting, multifamily deal analysis, syndication waterfall modeling, Monte Carlo simulation, sensitivity analysis, 1031 exchange planning, cost segregation, depreciation, real estate tax strategy, market comparison and scoring, rental property screening, deal screening, investor report generation, real estate financial modeling, rent-to-price analysis, development feasibility, value-add underwriting, API integration for real estate data (Census, Redfin, Rentcast, Mashvisor, AirDNA, ATTOM), or any real estate investment financial analysis task.
Redesigns an existing design as if a new requirement had been a founding assumption, then propagates it to every reference and delivers it in working increments. Use when the user explicitly asks to redesign for a requirement or from first principles. Never infer from a feature request. Not for redoing an attempt; use redoing-implementations.
Run a bounded red-teaming autoresearch loop to generate LLM guardrail training data. You (the agent running the skill) are the attacker and the judge: you craft attacks and label every response. The only model the harness calls is the target under test, over any OpenAI-compatible API (OpenRouter, Moonshot/Kimi, Fireworks, Ubicloud, OpenAI, or custom), with its key in `.red-team/.env`. Every attempt (pass and fail) is written to JSONL ready for fine-tuning guardrails in Llama Guard format. Use when asked to red-team or stress-test an LLM for harmful content, jailbreaks, prompt injection, or backdoor/trigger behavior; to build a safe/unsafe dataset; or to mine novel attacks at scale. Complements the hacker skill (code/infra exploitability): this skill targets model behavior and produces data.
[Deprecated] This is the required documentation for agents operating on the CloudBase Relational Database through MCP. It defines the canonical SQL management flow with `queryMysqlDatabase`, `manageMysqlDatabase`, `queryPermissions`, and `managePermissions`, including MySQL provisioning, destroy flow, async status checks, safe query execution, schema initialization, and permission updates. New environments should use PostgreSQL — see postgresql-development skill instead.
Use when releasing or upgrading the common-tools, dev-skills, or low-frequency-skill plugins, synchronizing Claude/Cursor/Codex manifests and marketplaces, updating CHANGELOG files, validating README and installation-document consistency, or generating/checking the skill-registry.json discovery manifest for Skill Router MCP. Use when the user requests plugin release, version upgrade, changelog, manifest, marketplace, README consistency, skill-registry.json, MCP skill registry, or registry stale validation.
Guide for connecting to remote servers via SSH, managing containers, and executing commands remotely. Covers SSH connection setup with multiple authentication methods (SSH key, sshpass, Paramiko, Fabric, tmux interactive), Docker container connection and command execution, file transfer, and troubleshooting. Use this skill whenever users need to: (1) Connect to a remote server or VPS via SSH, (2) Execute commands on remote machines, (3) Transfer files to/from remote servers, (4) Connect to Docker containers on remote hosts, (5) Set up SSH password authentication tools. Even if the user just says 'help me run something on my server' or 'connect to my machine', this skill applies.
Researches secondhand, resale, handmade, and musical-instrument marketplaces via the Crawlora API. Use to compare live listings and sellers, check sneaker or used-gear sale history, or source handmade and vintage goods with condition and pricing evidence kept distinct.
Append new field definitions to an in-progress research outline's `fields.yaml` — either from user-supplied input or from a web-search agent that proposes common dimensions in the domain. Use mid-`/research-outline` when you've realised the schema is missing dimensions (e.g. pricing, performance, ecosystem, governance) before running `/research-deep`, so deep agents fill the new fields on first pass instead of needing a re-run.
Look up current research information using parallel-cli search (primary, fast web search), the Parallel Chat API (deep research), or Perplexity sonar-pro-search (academic paper searches). Automatically routes queries to the best backend. Use for finding papers, gathering research data, and verifying scientific information. Note: query text is transmitted to api.parallel.ai (PARALLEL_API_KEY) and, for academic searches, to openrouter.ai (OPENROUTER_API_KEY).
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.