Catalog
All AI skills
66 skills. Every one is readable here; downloads and live use need a subscription.
antigravity-skill-orchestrator
A meta-skill that understands task requirements, dynamically selects appropriate skills, tracks successful skill combinations using agent-memory-mcp, and prevents skill overuse for simple tasks.
advanced-evaluation
This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.
agent-evaluation
Evaluate agent behavior with versioned cases and explicit verifiers. Use when comparing agent or prompt changes, reproducing failures, or running agent regression tests.
agent-evaluation-reporting
Use when summarizing agent evaluations where autonomous, assisted, failed, timed-out, or invalid outcomes must remain distinct and comparable.
evaluation
Build evaluation frameworks for agent systems. Use when testing agent performance systematically, validating context engineering choices, or measuring improvements over time.
hugging-face-evaluation
Add and manage evaluation results in Hugging Face model cards. Supports extracting eval tables from README content, importing scores from Artificial Analysis API, and running custom model evaluations with vLLM/lighteval. Works with the model-index metadata format.
llm-evaluation
Master comprehensive evaluation strategies for LLM applications, from automated metrics to human evaluation and A/B testing.
multi-agent-task-orchestrator
Route tasks to specialized AI agents with anti-duplication, quality gates, and 30-minute heartbeat monitoring
planning-and-task-breakdown
Breaks work into ordered tasks. Use when you have a spec or clear requirements and need to break work into implementable tasks. Use when a task feels too large to start, when you need to estimate scope, or when parallel work is possible.
task-intelligence
Protocolo de Inteligência Pré-Tarefa — ativa TODOS os agentes relevantes do ecossistema ANTES de executar qualquer tarefa solicitada pelo usuário.
ai-engineering-toolkit
6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.
architecture
Architectural decision-making framework. Requirements analysis, trade-off evaluation, ADR documentation. Use when making architecture decisions or analyzing system design.
azure-mgmt-arizeaiobservabilityeval-dotnet
Azure Resource Manager SDK for Arize AI Observability and Evaluation (.NET).
bdistill-behavioral-xray
X-ray any AI model's behavioral patterns — refusal boundaries, hallucination tendencies, reasoning style, formatting defaults. No API key needed.
design-ux
UX / usability audit — heuristic evaluation of INTERACTIVE UIs (not just visual polish). Load with design when a UI "feels off", "sucks to use", is hard to learn, needs an instruction wall, or before shipping an interactive tool/editor/app.
energy-procurement
Codified expertise for electricity and gas procurement, tariff optimisation, demand charge management, renewable PPA evaluation, and multi-facility energy cost management.
langfuse
Expert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production.
loop-library
Find, compare, adapt, and design bounded AI-agent feedback loops with explicit checks, stop rules, guardrails, and handoffs.
ot-ics
Authorized OT/ICS security assessment: Purdue-model zoning review, PLC/SCADA exposure, industrial protocol discovery, and passive-first evaluation discipline.
polars
Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.
receiving-code-review
Code review requires technical evaluation, not emotional performance.
run-deep-swe
Run reproducible DeepSWE coding-agent benchmark evaluations through OpenRouter and mini-swe-agent.
scikit-learn
Machine learning in Python with scikit-learn. Use for classification, regression, clustering, model evaluation, and ML pipelines.
uxui-principles
Evaluate interfaces against 168 research-backed UX/UI principles, detect antipatterns, and inject UX context into AI coding sessions.
yao-meta-skill
Create, refactor, evaluate, and package agent skills from workflows, prompts, transcripts, docs, or notes. Use for skill creation, reusable workflow packaging, skill improvement, evals, and team-ready distribution.
acceptance-orchestrator
Use when a coding task should be driven end-to-end from issue intake through implementation, review, deployment, and acceptance verification with minimal human re-intervention.
agentflow
Orchestrate autonomous AI development pipelines through your Kanban board (Asana, GitHub Projects, Linear). Manages multi-worker Claude Code dispatch, deterministic quality gates, adversarial review, per-task cost tracking, and crash-proof pipeline execution.
apify-ultimate-scraper
AI-driven data extraction from 55+ Actors across all major platforms. This skill automatically selects the best Actor for your task.
azure-resource-manager-durabletask-dotnet
Azure Resource Manager SDK for Durable Task Scheduler in .NET.
azure-storage-queue-py
Azure Queue Storage SDK for Python. Use for reliable message queuing, task distribution, and asynchronous processing.
behavioral-modes
AI operational modes (brainstorm, implement, debug, review, teach, ship, orchestrate). Use to adapt behavior based on task type.
closed-loop-delivery
Use when a coding task must be completed against explicit acceptance criteria with minimal user re-intervention across implementation, review feedback, deployment, and runtime verification.
commit
ALWAYS use this skill when committing code changes — never commit directly without it. Creates commits following Sentry conventions with proper conventional commit format and issue references. Trigger on any commit, git commit, save changes, or commit message task.
concise-planning
Use when a user asks for a plan for a coding task, to generate a clear, actionable, and atomic checklist.
conductor-revert
Git-aware undo by logical work unit (track, phase, or task)
create-issue-gate
Use when starting a new implementation task and an issue must be created with strict acceptance criteria gating before execution.
flowhunt-skill
Automation discovery audit skill. Walks through a 5-question workflow intake, then audits Gmail/Calendar/Slack/task trackers to identify automation opportunities. Use when a user wants to discover what processes in their business can be automated.
fp-backend
Functional programming patterns for Node.js/Deno backend development using fp-ts, ReaderTaskEither, and functional dependency injection
fp-errors
Stop throwing everywhere - handle errors as values using Either and TaskEither for cleaner, more predictable code
fp-refactor
Comprehensive guide for refactoring imperative TypeScript code to fp-ts functional patterns
fp-types-ref
Quick reference for fp-ts types. Use when user asks which type to use, needs Option/Either/Task decision help, or wants fp-ts imports.
full-output-enforcement
Use when a task requires exhaustive unabridged output, complete files, or strict prevention of placeholders and skipped code.
grok-build
Delegate well-specified implementation tasks to xAI's Grok Build CLI running headlessly while the orchestrating agent plans, writes task specs, reviews every diff, and owns the result.
hf-cloud-aws-context-discovery
Discover the effective local AWS profile, region, account, and caller identity before any AWS task without exposing credentials.
huggingface-best
Use when the user asks about finding the best, top, or recommended model for a task, wants to know what AI model to use, or wants to compare models by benchmark scores.
huggingface-tool-builder
Use this skill when the user wants to build tool/scripts or achieve a task where using data from the Hugging Face API would help. This is especially useful when chaining or combining API calls or the task will be repeated/automated.
image-generator
Generate and edit images using Gemini's Nano Banana Pro model (gemini-3-pro-image-preview). Use this skill when the user asks you to generate images, create visuals, edit photos, create logos, generate product mockups, or perform any image generation/editing task.
incremental-implementation
Delivers changes incrementally. Use when implementing any feature or change that touches more than one file. Use when you're about to write a large amount of code at once, or when a task feels too big to land in one step.