Total 55,220 skills, AI & Machine Learning has 9163 skills
Showing 12 of 9163 skills
Answer a question across a corpus of contract documents with verified citations. Use when the user asks what a contract says, which contracts have a clause, what changed between amendments, or any question that needs reading and citing across a set of contract files. The corpus must be on the local filesystem (see README).
Plug-in vision for text-only models. Use whenever the user shares an image (local path, screenshot, photo, chart, document scan, or image URL) and the active model cannot see images or has no vision tool. Runs the modlens CLI to convert the image into structured JSON evidence: OCR text, layout, semantics, visual clues. Also use when the user asks how to install, configure, or switch modlens providers (Gemini API key, OpenAI-compatible endpoints, Claude API or Claude Code CLI).
Dispatch implementation tasks to agent teammates in git worktrees. Triggers: 'delegate', 'dispatch tasks', 'assign work', or /delegate. Spawns teammates, creates worktrees, monitors progress. Supports --fixes flag. Do NOT use for single-file changes or polish-track refactors.
Restore and read workflow state after a context break — re-inject workflow phase, task progress, and behavioral guidance into the current session, reconcile state against git reality, and verify whether a workflow exists. Use when the user says 'resume', 'rehydrate', 'where were we', or runs /rehydrate, or when the agent has drifted after context compaction. Do NOT use for saving or mutating state (that is /checkpoint).
Author, audit, and improve Grafana SKILL.md files against Anthropic's published Agent Skills guidance and the four-dimension rubric the grafana/skills CI gate uses (conciseness, actionability, workflow clarity, progressive disclosure). Applies the canonical SKILL.md structure (YAML frontmatter + body + references/ + scripts/ + assets/), the "pushy description" trigger pattern, the three-level progressive-disclosure model, and the validate-fix-rerun feedback loop. Use when creating a new skill in this repo, when reviewing a skill PR, when a skill's Tessl review score is below 75 (the merge gate), when a skill's description isn't getting picked up by agents, when restructuring a long SKILL.md into a bundle, or when the user asks how to write, improve, optimize, audit, or fix a skill - even if they don't say "skill" explicitly (e.g. "this isn't triggering", "Tessl scored this 72", "split this doc").
Inspect the availability of model serving on a completed Itô compute booking and, when the canonical backend becomes available, hand off an explicitly confirmed serving manifest. Use after ito-compute has booked GPU nodes and the user asks for an OpenAI-compatible endpoint, ito-serve, hosted Kimi, or self-hosted open-weights inference. ECC implements no serving stack of its own.
Use when the user wants to measure or set up evals/checks for one of their skills — how fast it is, whether its output is valid, whether it fires when expected, or whether its opening classification/routing gate labels inputs correctly.
Audit AI agent skills for security risks before installation or periodically. Works on Claude Code, OpenClaw, and all platforms. Detect prompt injection, data exfiltration, malicious commands, obfuscated code, privilege abuse, supply chain risks, memory poisoning, trust exploitation, and behavioral manipulation. Use before installing third-party skills from any marketplace.
Agent-callable Alpaca trading tools — place and manage stock, crypto, and options orders, read account balances, positions, and portfolio history, look up assets and market hours, and read watchlists. Use when the user mentions Alpaca or wants to trade or inspect a brokerage account, even if they don't name Alpaca explicitly.
Converts cuTile GPU kernels (@ct.kernel) to Triton (@triton.jit). Handles standard in-repo conversion, debugging (cudaErrorIllegalAddress, shape mismatch, numerical mismatch), and mapping cuTile idioms (ct.load/ct.store, ct.Constant, ct.launch) to Triton equivalents. Covers dual-kernel layout flags (e.g. transpose=True/False + autotune grid via META) per translations/advanced-patterns.md. Use when converting, porting, or translating cuTile kernels to Triton, or debugging existing Triton translations.
Choose the right MoE token dispatcher (`alltoall`, DeepEP, or HybridEP) for the hardware, EP degree, and optimization stage. Summarizes patterns from DSV3, Qwen3, Qwen3-Next, and VLM bring-up work.
Generate a source-backed starting `trtllm-serve --config` YAML for basic aggregate single-node PyTorch serving, aligned with checked-in TensorRT-LLM configs and deployment docs. Preserves explicit latency / balanced / throughput objectives. Excludes disaggregated, multi-node, and non-MTP speculative configs.