Read, create, or edit Microsoft Word .docx files — extract/summarize text and tables, generate reports/letters/memos with headings, tables, images, TOC and page numbers, do find-and-replace, or apply tracked changes (redlines) and comments. Use whenever the user has a .docx or wants a Word deliverable. Not for PDF…
Read, extract (text/tables), create, merge/split/rotate, watermark, encrypt, fill, and render-to-image .pdf files. Use whenever the user uploads a .pdf or asks to produce, edit, or pull data out of one.
Read, create, or edit PowerPoint .pptx decks — build slides from an outline, extract slide text/speaker notes, edit shapes/tables/charts, replace images, or export to PDF/images. Use whenever a .pptx (or .ppt) file is an input or output, or the user mentions a deck, slides, or a presentation.
Tune and review Langfuse autoscaling for web, web-iso, and web-ingestion. Use for Terraform scale settings, RPM targets, scaling bounds, task counts, cost/performance tradeoffs, or Datadog evidence in the infrastructure repo.
Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Codex's capabilities with specialized knowledge, workflows, or tool integrations.
Configure and troubleshoot Turborepo monorepos. Use for turbo.json, task pipelines, dependsOn, caching, filters, affected packages, CI optimization, environment variables, package boundaries, or shared internal packages.
Field-tested methodology and concrete recipes for training and operating large-scale LLM/VLM/multi-modal models end to end - choosing and benchmarking accelerators, storage and network; SLURM/Kubernetes orchestration; maximizing training throughput and fitting models in memory; diagnosing and surviving training…
Configure this repo for the engineering skills — set up its issue tracker, triage label vocabulary, and domain doc layout. Run once before first use of the other engineering skills.
Build and wire an AtomicAgent[InSchema, OutSchema] — schemas, AgentConfig, SystemPromptGenerator, provider client, history, hooks, optional context providers. Use when the user asks to "create an agent", "add another agent", "build an AtomicAgent", "wire up an agent", "make a planner/router/extractor agent", or runs…
Build a BaseTool[InSchema, OutSchema] subclass — input/output schemas, BaseToolConfig, run() (and optional runasync()), env-driven secrets, typed failure outputs. Use when the user asks to "add a tool", "create a tool", "wrap an API as a tool", "build a BaseTool", "make a calculator/search/weather tool", or runs…
Guide for the Atomic Agents Python framework — schemas, agents, tools, context providers, prompts, orchestration, and provider configuration. Use when code imports from atomicagents, defines an AtomicAgent, BaseTool, or BaseIOSchema, or the user asks about multi-agent orchestration or LLM-provider wiring in an…
Add or update xLLM unit tests in the repository. Use when Codex needs to create a new C++/CUDA/NPU/MLU unit test, place a test under tests/, wire it into CMake with cctest, update an existing test target, choose platform gates, or validate test naming and dependencies against current xLLM test conventions.
Review code changes for quality, security, performance, and correctness following project-specific standards. Use when reviewing pull requests, examining git diffs, or when the user asks for a code review. This skill should be used proactively — when the user asks for a review without specifying commits, automatically…
Use when the user wants to add, modify, debug, or review an xLLM TileLang Ascend kernel or specialization, including Python kernel definitions, generated Ascend-C source, runtime wrapper dispatch, TileLang CMake wiring, and NPU tests.
Discover daily arXiv papers for LLM/Agent topics, rank candidates with keyword and institution filters, and prepare a small selected paper list for llm-paper-daily style workflows.
Operates a device or appliance (like a desklamp, microwave, or fridge) to interact with another object. Use when the task requires using a tool on a target item (e.g., "look at laptop under the desklamp", "heat potato with microwave"). Locates both the device and target object, co-locates them, and executes the…
Use when the agent must collect and track multiple instances of the same object type in ALFWorld (e.g., "put two cellphone in bed"). This skill maintains a count of collected versus needed objects, guides systematic searching through receptacles, and ensures each found object is placed at the target before searching…
Navigates to a suspected location and identifies a target object. Use when your goal requires finding a specific object (e.g., "potato", "plate") and its location is not immediately known. Moves to a relevant receptacle (like a fridge or cabinet), checks its contents, and outputs the object's location or confirms its…
End-to-end recipe for adding a new task under examples/ — the three pieces that have to line up (task.yaml, seed/, and grader/), what to put in each, the TaskGrader API surface, the coral validate → smoke-test loop, and the common mistakes (repopath pointing at the wrong dir, score direction backwards, hidden answer…
Write a note to {shareddir}/notes/ that future agents can actually act on. Use after every coral eval, when a heartbeat (reflect / consolidate / pivot) asks for a note, or when you discover a grader / build / runtime issue that future agents will hit. Covers 4 note variants (experiment / infra / focus / synthesis)…
Autonomously create, test, and optimize skills by detecting reusable patterns in your own work. Use when you notice repeated tool sequences, recurring code patterns across attempts, or insights that should be captured as a packaged skill. Also use to benchmark and iterate on existing skills.