Catalog
All AI skills
22 skills. Every one is readable here; downloads and live use need a subscription.
evaluation
Build evaluation frameworks for agent systems. Use when testing agent performance systematically, validating context engineering choices, or measuring improvements over time.
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.
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.
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.
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.
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.