'scheduling' 검색 결과

Review past local Cursor sessions, identify recurring tasks, and propose changes to a project skill. Changes are tested on separate validation tasks and staged for review before adoption, with backups of existing files.

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• Turn recurring Cursor tasks into a reusable skill
• Review proposed edits and validation scores before adoption
• Try the workflow without provider calls using mock

Review communications research by searching existing libraries and notes before academic databases. Produce a paper comparison table covering methods, results, and limitations, then summarize major approaches, evidence strength, and remaining research gaps.

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• Surveying research on wireless and satellite communications
• Finding related work from saved papers and notes
• Comparing assumptions, methods, and limitations across papers

Finds Lark contacts by name or email and retrieves open_id, an identifier used in later actions. It also looks up profile details, personal status, and signatures, and searches for bots or agents visible to the current user.

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• Identifying a colleague before messaging or scheduling
• Looking up a profile from a known open_id
• Checking colleagues’ personal status and signatures

Combine Lark calendar events and unfinished tasks into a daily or weekly briefing. Review meetings in time order, spot scheduling conflicts and free slots, and check task deadlines before starting work or joining a standup.

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• Reviewing today's schedule and unfinished tasks
• Checking tomorrow's meetings and task deadlines
• Preparing a morning standup briefing

Diagnose unreachable or unhealthy OpenShell gateways and sandbox startup failures. Identify the underlying execution platform, then examine logs, authentication, certificates, and configuration with the matching tools to narrow down the cause.

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• Investigate gateway connection and health check failures
• Find why a sandbox remains pending or fails to start
• Check Docker or Podman runtime and image problems

Guide MNN Metal backend development and optimization, from finding performance bottlenecks to changing GPU computation, combining operators, and adjusting execution scheduling. A task-based document index directs work toward relevant implementation rules, benchmarks, and correctness checks.

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• Finding bottlenecks in Metal inference
• Adding or modifying Metal kernels
• Combining operators into one GPU dispatch

Improve MNN's Vulkan backend by separating CPU scheduling bottlenecks from GPU computation costs. It guides operator and shader changes, new feature integration with compatibility fallbacks, and device testing to verify both correctness and end-to-end performance.

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• Finding CPU or GPU bottlenecks in Vulkan inference
• Optimizing a specific operator or shader
• Integrating cooperative matrix or subgroup features

Find why MNN CPU inference is slow by examining threads, scheduling, memory layout, and the processor instruction path actually used. Operator-level measurements and comparisons with other inference frameworks help identify which layer needs improvement across ARM, x86_64, and RISC-V.

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• Investigate slow CPU operators or LLM inference
• Diagnose thread count and memory usage anomalies
• Locate the cause of a performance regression

Set up recurring agent work for issues, tickets, or small fixes. The skill tests one real item first, then defines a repeatable procedure, tracks work to prevent duplicates, and holds outputs for human review.

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• Recurring preparation of fixes for Sentry issues
• Drafting replies to Front tickets
• Preparing small performance improvement PRs

Set up recurring tasks in Superset with an agent prompt, a schedule or event trigger, and a chosen execution workspace. After explicit confirmation, create the automation, check its saved settings, and review a real first run.

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• Scheduling a daily or weekly agent task
• Starting agent work when a GitHub pull request opens
• Triggering tasks from selected Slack reactions

Run machine learning workloads on Modal's cloud GPUs without managing servers. Guides Python configuration for training, inference, batch processing, and API deployment, including automatic scaling, persistent storage, scheduled jobs, and troubleshooting.

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• Running GPU inference without managing servers
• Deploying a model as an automatically scaling API
• Processing large batches in parallel

Guides PyTorch model training and fine-tuning, from choosing architectures to configuring optimizers and learning rates. Provides troubleshooting steps for unstable loss and memory shortages, plus methods for improving GPU throughput and comparing experiments.

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• Choose model architectures for the available data type and scale
• Configure optimizers and learning-rate schedules
• Investigate loss spikes, NaNs, and GPU memory shortages

Set up Loop Engineering through a single command-line tool, choosing a workflow pattern and agent tool. Start in report-only mode, check setup health and readiness, and provide next actions and first-run instructions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Introducing a daily issue and priority review workflow
• Setting up workflows for stalled pull requests or failing CI
• Checking the health of an existing Loop Engineering setup

Build weekly or monthly content calendars around goals, audience habits, and fixed events. Get dated topics, formats, platforms, and suggested posting times, with a balanced content mix and room for timely updates.

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• Build a weekly or monthly posting calendar
• Plan posts around a launch or event
• Balance educational, promotional, and community content

Manage Hugging Face Hub through the hf command line tool. Download and upload models or datasets, organize cloud storage, manage authentication and local caches, run scheduled jobs, and deploy or maintain model inference services.

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• Download or publish models and datasets
• Sync training checkpoints to cloud storage
• Inspect and clean the local cache

Add recurring jobs to a Convex app by scheduling internal functions in convex/crons.ts. The skill guides interval selection, keeps each run small and safe to repeat, and checks that the schedule appears in the dashboard.

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• Add recurring jobs to a Convex app
• Choose intervals for scheduled internal functions
• Make recurring handlers safe to run again

Turn plain-language app ideas into Convex backend designs and code. Cover data structure, authentication, live updates, file storage, scheduled tasks, and LLM features, with type checking and deployment verification before treating the backend as complete.

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• Building a backend from an app idea
• Adding authentication and file uploads
• Designing live collaboration or multiplayer features

Route Convex development and operations requests to the relevant specialist skills and current procedures, covering app setup, backend features, code reviews, authorization checks, production monitoring, data migrations, and cost analysis.

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• Start a new full-stack app with Convex
• Add authentication, billing, or search to an existing app
• Review backend code and authorization

Use WorldMonitor’s architecture as a reference for building dashboards. Find examples of TypeScript panels, data services, grid layouts, API proxies, and refresh scheduling, with guidance on simplifying these patterns for a personal monitoring project.

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• Build a panel following WorldMonitor conventions
• Study patterns for scheduled data refreshes
• Review dashboard themes and grid layouts

Implement polling, or repeated data checks, that slows down after failures and returns to its normal interval after success. The TypeScript pattern also pauses hidden tabs, staggers overdue refreshes, and manages multiple update tasks independently.

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• Keep dashboard data updated through temporary API failures
• Pause polling while a browser tab is hidden
• Manage different refresh intervals for multiple data sources

Analyze an existing codebase to identify reusable design patterns in components, services, APIs, styling, and refresh scheduling. Produces structured descriptions with representative code, conventions, dependencies, and adaptation notes for reuse or later conversion into skills.

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• Understanding the structure of a large codebase
• Extracting reusable component and service designs
• Following an existing project's conventions in a new project

Build a custom fuzzer, a tool that tests code with many generated inputs, using LibAFL in Rust. Combine input mutation, execution feedback, scheduling, and target execution components when standard fuzzers offer insufficient control.

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• Build a fuzzer for a nonstandard target or architecture
• Implement custom input mutation and feedback strategies
• Prototype a new fuzzing research technique

Prepare briefs for public-equity investment meetings, including management calls and investment committees. Rank key questions, identify what to listen for, plan follow-ups to vague answers, and organize evidence requests and actions around the investment decision.

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• Preparing priority questions for management or IR meetings
• Turning earnings analysis into a call plan
• Organizing investment committee or portfolio review discussions

Build a realistic Linear cycle plan using priorities, dependencies, estimates, unfinished work, and team capacity. Separate essential work from optional additions, flag blockers and missing estimates, and wait for approval before changing issues, estimates, or owners.

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• Plan the next cycle around team capacity
• Review unfinished work carried into a cycle
• Separate essential work from optional additions