Export raw OpenTelemetry traces from an AI application to Confident AI's Observatory. TRIGGER when the user wants to send OpenTelemetry or OTLP traces/spans from an LLM app, agent, RAG pipeline, or chatbot to Confident AI; configure the Confident AI OTLP endpoint; set confident.span. or confident.trace. attributes…
Instrument an AI application with DeepEval's native tracing so its behavior is visible in Confident AI. TRIGGER when the user wants to add DeepEval tracing or @observe to an LLM app, agent, RAG pipeline, or chatbot; wire a framework, model-provider, or vector-database integration (LangGraph, LangChain, OpenAI Agents…
DeepEval evaluation workflow for AI agents and LLM applications. TRIGGER when the user wants to evaluate or improve an AI agent, tool-using workflow, multi-turn chatbot, RAG pipeline, or LLM app; add evals; generate datasets or goldens; use deepeval generate; use deepeval test run; send results to Confident AI…
Design NPC and enemy decision-making with finite state machines, behavior trees, steering behaviors, and A pathfinding — engine-neutral algorithms that pair with the detected engine's navigation API. Use when building enemy AI, an FSM or behavior tree, steering/flocking, or pathfinding, or when the user mentions state…
Find and actually LOOK at real photographs — keyless image-search APIs, downloaded to disk so they render as images. Use before building any place, material, vehicle, sky or lighting condition, and again when judging your own screenshots.
Set up player input in Unreal Engine 5 with Enhanced Input: Input Actions, Input Mapping Contexts, modifiers and triggers, adding the mapping context, and binding actions by ETriggerEvent. Use when wiring movement/look/jump input, creating IA/IMC assets, binding in C++ or Blueprints, or when the user mentions Enhanced…
Audit an AI agent benchmark for hackability. Detects evaluation vulnerabilities like missing isolation, leaked answers, eval() on untrusted input, prompt injection in LLM judges, weak scoring, logic gaps, and trust of untrusted output. Use when analyzing whether a benchmark can be gamed or exploited.
Use this skill when adding authentication, handling user input, working with secrets, creating API endpoints, or implementing payment/sensitive features. Provides comprehensive security checklist and patterns.
Audit or apply subtractive cleanup for accumulated agent-created test bloat, verification theater, and defensive or fallback bloat while preserving independent external behavior. Invoke explicitly for semantic simplification, not generic refactoring.
Guide the user to open a feedback issue for the simple-output-styles plugin. Use when the user wants to report feedback on an output style, the evals, the documentation, the installation, licensing, or wants to request a new style. Gathers the Claude Code version and the active style, then prepares a prefilled GitHub…
Help the user pick an output style from the simple-output-styles plugin and set it globally. Recommends actionable-clarity, or walks through the full list with the pros and cons of each style.
Adapt an agent to run on BenchFlow (ACP) and verify it behaves identically inside the eval harness vs standalone — closing the eval↔prod gap. Use when adding a new agent to this repo or checking an existing one for eval/prod behavior parity.
Build a training dataset for fine-tuning, distillation, or continued pretraining. Use when the user wants to turn logs/CSV/JSONL into fine-tuning data, label data with an LLM, distill a teacher model's outputs, generate synthetic training examples from nothing, chunk raw domain text for CPT, deduplicate a dataset…
The tunelab front door — decides whether a task needs fine-tuning at all, by running EXPERIMENTS on the user's data, not just interviewing. Use whenever the user wants to fine-tune, distill, or train a small/local model, cut their LLM API bill, replace frontier calls with something cheaper or faster, build a…