Results for “task”

Train, evaluate, export, quantize, and run inference for a TAO OneFormer model that performs panoptic, instance, and semantic segmentation using task-conditioned queries.

Browse available agent personas and compose ad-hoc teams of up to 5 agents to work in parallel on a task, then synthesize their results.

Perform time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search using a scikit-learn compatible Python toolkit.

Builds reliable browser automation skills by iteratively running a browsing task, reading the trace, and improving the navigation strategy until it passes consistently.

Evaluates open-ended generation quality of multilingual LLMs across brainstorming, planning, and long-form tasks, using AYA and DOLLY datasets with qualitative fluency and quality scoring.

Use this skill for test strategy, E2E automation, visual regression, flaky test control, QA evidence. Trigger when the task involves agent profile work related to Qa Automation Agent, implementation, audits, debugging, strategy, or validation.

Fine-tune, evaluate, run inference, and export Cosmos-Embed1 video-text embedding models for tasks like text-to-video retrieval and semantic deduplication.

Debug and optimize AI agents by analyzing reasoning traces, context degradation, tool confusion, instruction drift, repeated task failures, and performance regressions.

Analyzes draft prompts, identifies intent and gaps, matches ECC components, and outputs an optimized prompt for the user to paste and run. Advisory only, never executes the task.

Run ML workloads on Modal's serverless GPU cloud: deploy models as auto-scaling APIs, run batch jobs, and schedule tasks with pay-per-second GPU pricing.

Evaluates the throughput and resource allocation efficiency of RIS-aided mobile edge computing systems by measuring the total computation task bits successfully completed under varying network conditions.

This skill should be used when the user asks to "/do-agent", asks for "Execute complex tasks using multi-agent architecture with context protection", or needs the workflow previously provided by the /do-agent slash command.

Create, update, and manage GitHub issues using MCP tools. Use this skill when users want to create bug reports, feature requests, or task issues, update existing issues, add labels/assignees/milestones, set issue fields (dates, priority, custom fields), set issue types, manage issue workflows, link issues, add dependencies, or track blocked-by/blocking relationships. Triggers on requests like "create an issue", "file a bug", "request a feature", "update issue X", "set the priority", "set the start date", "link issues", "add dependency", "blocked by", "blocking", or any GitHub issue management task.

Analyze draft prompts to identify intent, scope, and missing context, then generate an optimized prompt with ECC component recommendations. Advisory only — never executes the task.

Integrate Xquik into apps, scripts, data pipelines, or AI agents for X API tasks like tweet search, user lookup, follower export, media actions, and webhook verification.

Automate Google Search Console tasks including search performance analysis, URL inspection, sitemap management, and indexing status checks via Composio's Rube MCP toolkit.

Selects the right Gemma model for a task, recommends deployment tooling (Gradio, Transformers.js, Vertex AI, MLX), and applies optimizations like MTP and QAT.

Presentation creation, editing, and analysis. When Claude needs to work with presentations (.pptx files) for: (1) Creating new presentations, (2) Modifying or editing content, (3) Working with layouts, (4) Adding comments or speaker notes, or any other presentation tasks

Presentation creation, editing, and analysis. When Claude needs to work with presentations (.pptx files) for: (1) Creating new presentations, (2) Modifying or editing content, (3) Working with layouts, (4) Adding comments or speaker notes, or any other presentation tasks

Use this skill for turning issues into implementation plans, branches, code changes, tests, and PR descriptions. Trigger when the task involves programming work related to Issue To PR Agent, implementation, audits, debugging, strategy, or validation.

Use this skill for WebXR, AR previews, model-viewer, mobile sensors, permissions, fallback UX. Trigger when the task involves 3d web work related to XR AR Web Experiences, production implementation, audits, debugging, strategy, or validation.

Static human-browsable catalog of the Zephyr RTOS skill set. Skill selection is handled by the zephyr-agent-skills entry point and by zephyr-cli skills suggest; this index is a reference listing only, not a task skill.

Run machine learning and generative AI tasks directly in BigQuery SQL using built-in functions for forecasting, anomaly detection, key driver analysis, and text generation.

Enables AI agents to make policy-gated payments using the x402 HTTP payment protocol with per-task budgets, spending controls, and non-custodial wallets.

Build clinical deep-learning pipelines with PyHealth: load EHR, signal, and imaging datasets, define prediction tasks, instantiate models, train with the PyHealth Trainer, and compute clinical metrics.

Enables visual instruction tuning and image-based conversations using open-source vision-language models. Supports multi-turn image chat, visual question answering, and image understanding tasks.

Automates the full lifecycle of agent skills: extracts reusable constraints from user feedback, merges preferences with version bumps, and retrieves relevant skills for new tasks.

Use when data operation or statistical analysis tasks do not need full program implementation, and the agent should operate Insyra through CLI/REPL, .isr scripts, or DSL workflows, including environment workflows, reproducible command pipelines, and command selection guidance.

Load pre-trained models from Hugging Face Hub, run pipeline inference, generate text, and fine-tune models on NLP, vision, audio, and multimodal tasks using the Transformers library.

Replaces GIoU with Complete IoU (CIoU) loss in PyTorch object tracking or detection tasks, combining overlap area, center-point distance, and aspect-ratio similarity for improved bounding-box regression.

Benchmarks long-context language models across 20 sub-tasks spanning 3k–200k tokens, covering retrieval, reasoning, summarization, and instruction understanding, with exact-match accuracy as the primary metric.

Byzantine fault-tolerant consensus and distributed coordination. Queen-led hierarchical swarm management with multiple consensus strategies. Use when: distributed coordination, fault-tolerant operations, multi-agent consensus, collective decision making. Skip when: single-agent tasks, simple operations, local-only work.

Activate when users mention Stata commands, .do files, regressions, econometrics, stored results, graphs, dataset inspection, replication, or Stata errors. Route the task through mcp-stata tools and the specialized research skills instead of treating it as plain text coding.

Benchmarks systems on the Abstraction and Reasoning Corpus (ARC) by requiring inference of abstract transformation rules from few input-output grid demonstrations and application to novel test cases, reporting the fraction of tasks solved.

Inspect screenshots, photos, diagrams, image paths, and image URLs when the task depends on visible content. Use when the prompt lacks actual image content, native inspection fails, or the user requests inspect_image; prefer the MCP tool, then the installed CLI.

You are an expert in designing collaborative AI agent teams with CrewAI. You think in terms of roles, responsibilities, and delegation. You design clear agent personas with specific expertise, create well-defined tasks with expected outputs, and orchestrate crews for optimal collaboration.