Total 50,537 skills, AI & Machine Learning has 8483 skills
Showing 12 of 8483 skills
Analyze datasets by running clustering algorithms (K-means, DBSCAN, hierarchical) to identify data groups. Use when requesting "run clustering", "cluster analysis", or "group data points". Trigger with relevant phrases based on skill purpose.
Use free SearXNG web search APIs for agent-friendly, privacy-first, and high-volume search tasks.
Use this skill when the user wants to debug, diagnose, or systematically iterate on an experiment that already exists, or when they need a structured experiment log for tracking runs, hypotheses, failures, results, and next steps during active research. Apply it to underperforming methods, training that will not converge, regressions after a change, inconsistent results across datasets, aimless experimentation without progress, and questions like 'why doesn't this work?', 'no progress after many attempts', or 'how should I investigate this failure?'. Also use it for setting up practical experiment logging/record-keeping that supports debugging and iteration. Do not use it for designing a brand-new experiment pipeline or full experiment program (use experiment-pipeline), generating research ideas, fixing isolated coding/syntax errors, or writing retrospective summaries into research memory/notes/knowledge bases.
SCORPION v2.0 — Momentum Event Consensus. Complete rewrite. Uses leaderboard_get_momentum_events (real-time threshold crossings) to detect when 2+ quality SM traders cross momentum thresholds on the same asset/direction within 60 minutes. Confirmed by market concentration + volume. Enters with the momentum. Replaces the v1.1 whale-mirroring scanner (406 trades, -24.2% ROI, stale position data).
ORCA v1.1 — Hardened dual-mode emerging movers scanner. Every lesson from 5+ days of live trading across 22 agents baked into the code. v1.1 adds the DSL state template directly in scanner output — eliminating the dsl-profile.json override bugs that broke Fox, Grizzly, Jackal, and every Wolf-based agent. XYZ equities banned at scan level. Leverage 7-10x enforced. Stagnation TP mandatory. 10% daily loss limit. 2-hour per-asset cooldown. Conviction-scaled Phase 1 timing per-signal. The agent cannot override any of these — they are in the scanner, not instructions.
Validates optimization plan via parallel multi-agent review (Codex + Gemini) before execution. GO/NO-GO verdict.
The basics of how to program GPUs using Mojo. Use this skill in addition to mojo-syntax when writing Mojo code that targets GPUs or other accelerators. Use targeting code to NVIDIA, AMD, Apple silicon GPUs, or others. Use this skill to overcome misconceptions about how Mojo GPU code is written.
Help to write Mojo code using current syntax and conventions. Always use this skill when writing any Mojo code, including when other Mojo-specific skills (e.g., mojo-gpu-fundamentals) also apply. Use when writing Mojo code, translating projects to Mojo, or otherwise generating Mojo. Use this skill to overcome misconceptions with how Mojo is written.
Use when the user wants to generate speech, voiceover, or text-to-audio. Converts text to AI voice via Giggle.pro TTS API. Triggers: generate speech, text-to-speech, TTS, voiceover, read this text aloud, synthesize speech.
Write, debug, and optimize Triton and Gluon GPU kernels using local source code, tutorials, and kernel references. Use when the user mentions Triton, Gluon, tl.load, tl.store, tl.dot, triton.jit, gluon.jit, wgmma, tcgen05, TMA, tensor descriptor, persistent kernel, warp specialization, fused attention, matmul kernel, kernel fusion, tl.program_id, triton autotune, MXFP, FP8, FP4, block-scaled matmul, SwiGLU, top-k, or asks about writing GPU kernels in Python.
Benchmark any agent skill to measure whether it actually improves performance. Use when the user wants to evaluate, test, or compare a skill against baseline, or when they mention "benchmark", "eval", "skill performance", or "does this skill help". Runs isolated eval sessions with and without the skill, grades outputs via layered grading (deterministic checks + LLM-as-judge), analyzes behavioral signals, and generates a comparison report with a USE / DON'T USE verdict.
Sub-millisecond VM sandboxes for AI agents using copy-on-write KVM forking via Zeroboot