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OpenClaw CI의 대기와 실행 환경 부족 원인을 조사합니다. 측정 결과에 따라 실행기 등록 수, 병렬 실행 수, 테스트 분할과 작업 배치를 조정합니다.

• OpenClaw CI 대기 원인 조사
• 집중 시간대 실행기 등록 수 추산
• 병렬 실행 수와 테스트 분할 검토

대규모 기능의 설계 제안을 의존 관계와 통과 기준이 있는 작업으로 나눕니다. 여러 에이전트의 조사, 구현, 테스트, 검토부터 통합과 최종 검증까지 정리합니다.

• 큰 설계 제안을 검증 가능한 작업으로 나눕니다
• 여러 에이전트에 의존 관계가 있는 일을 배정합니다
• 변경 브랜치를 검토하고 순서대로 통합합니다

실시간 화면과 데이터 전달의 지연, 데이터 최신성, 처리 대기를 따로 측정합니다. 이벤트 발생부터 화면 표시까지 경로를 개선하고 운영 환경에서 결과를 확인합니다.

• 실시간 화면의 지연 조사
• 전달 데이터의 최신성 확인
• 대기열 적체 줄이기

Plan, train, evaluate, and deploy neural networks using Flow Nexus cloud infrastructure. Coordinate training across multiple computing environments, tune training settings, and manage model versions, predictions, and ongoing performance monitoring.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing models for classification or time series
• Coordinating training across cloud environments
• Comparing model performance and tuning training

Calculate portfolio weights from a covariance matrix, which describes how asset returns move together, and expected returns using Conjugate Gradient. Save the weights with the solver used, numerical error, and timing so the calculation can be checked later.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Calculating portfolio weights from supplied return data
• Comparing Conjugate Gradient with the legacy calculation
• Keeping records of weights and calculation details

Run demanding neural-trader backtests, parameter comparisons, and model training in an Anthropic Managed Agent cloud container. Estimate costs, test inputs on a small run, collect performance metrics and CSV files, and verify signed results before storage.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Testing trading strategies over several years
• Comparing many parameter combinations
• Training LSTM or Transformer models in the cloud

Classify market conditions as bullish, bearish, ranging, or volatile to guide strategy selection and historical testing. Distinguish synthetic demonstrations from analysis of actual price data, with confidence levels and historical comparisons where available.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing market conditions for a stock symbol
• Choosing a strategy suited to market conditions
• Checking market conditions before a backtest

Turn an approved specification into pseudocode, a plain outline of program logic, and an architecture defining module responsibilities and API inputs and outputs. After user confirmation, proceed with implementation and unit tests based on those designs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turning an approved specification into pseudocode
• Defining module boundaries and dependency rules
• Designing API inputs, outputs, and error responses

Scan selected market symbols for unusual price movements and rank potential trading signals. Combine anomaly scores, measures of departure from normal behavior, with strategy filters, historical pattern matches, and predictions about changing market conditions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Scan selected symbols for unusual price movements
• Filter trading signals by a saved strategy
• Compare detected anomalies with historical patterns

Add or update a focused, reusable test when fixing bugs or changing behavior. Keep it in the project's existing test structure, verify the relevant change, and maintain coverage as the expected behavior evolves.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding a regression test for a bug fix
• Updating tests when expected behavior changes
• Delivering a reusable test suite required by a specification

Manage ComfyUI image generation from the command line. Validate workflows, which define generation steps, submit them for execution, monitor queued jobs, inspect available models, and download images from completed runs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Validating and submitting image generation workflows
• Checking or interrupting queued generation jobs
• Listing available models and node types

Use PyTDC to discover therapeutic machine-learning datasets, create task-appropriate training and test splits, and evaluate predictions against curated benchmarks. Also score selected molecules with supported scoring functions, with explicit download plans and bounded outputs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find datasets for therapeutic machine-learning tasks
• Plan and create task-appropriate data splits
• Evaluate predictions with benchmark-specific metrics

Add structured logs, performance metrics, and traces that follow requests across Python services. Establish consistent log fields and request IDs so production failures can be investigated using recorded activity and service health signals.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding searchable JSON logs
• Collecting service metrics with Prometheus
• Following requests across multiple services

Move slow Python operations into background jobs so requests can return promptly. Use task queues and workers, the processes that execute queued work, with retry rules, duplicate handling, time limits, and persistent job status.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Send emails and notifications in the background
• Generate reports and data exports asynchronously
• Process uploads and media conversions

Build reusable Terraform modules for AWS, Azure, GCP, and OCI. Organize cloud infrastructure as code with documented inputs, validation, usage examples, provider version constraints, and Terratest tests for consistent provisioning.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create reusable cloud infrastructure modules
• Standardize Terraform conventions across a team
• Connect network and database modules

Turn past classrooms and chat history into a reusable personal skill for course creation. It examines varied examples, checks recurring patterns and exceptions, and asks for corrections before saving instructions that reflect the user's workflow and quality expectations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing patterns across past course creation
• Turning preferred workflows into reusable instructions
• Checking inferred preferences against examples

Assess whether stolen Kerberos tickets, credentials used for network authentication, can enable access to other systems during authorized testing. Guide ticket extraction and reuse, review detection indicators, and document evidence for security reports.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Authorized testing of access through reused tickets
• Checking detection of Kerberos ticket theft
• Collecting evidence for lateral movement reports

Create or update saved Dynamic Workflows, JavaScript scripts that organize repeatable agent tasks. Capture task order, dependencies, and final results in a reusable file, with a slash command for running the workflow.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Saving a repeated task as a reusable workflow
• Updating the steps in an existing workflow
• Organizing parallel tasks and dependent steps

Prepare internal audit materials for ISO/IEC 42001:2023, the AI management system standard. Identify gaps, link AI risks to controls, and build a 12-month audit plan with coverage priorities and auditor independence checks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prioritizing gaps before certification
• Linking AI risks to Annex A controls
• Planning a year of internal audits

Coordinate compliance work across multiple standards and regulations. Use a company profile to identify relevant frameworks, map shared controls, prepare mock audits, and build a unified evidence checklist showing where documents can be reused.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Starting a compliance program across multiple frameworks
• Planning an annual audit calendar
• Preparing for a certification audit

Support startup AI strategy by comparing API use, fine-tuning—additional training of an existing model—and in-house development. Assess three-year costs, regulatory risk, self-hosting breakeven points, and hiring priorities to inform leadership decisions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare API use, fine-tuning, and in-house development
• Assess regulatory risks for an AI use case
• Calculate when self-hosting becomes cost-effective

Size support and operations teams using demand ranges and queueing calculations, which estimate how work waits for service. Assess workload imbalance and service target risks, then build a quarterly hiring plan that accounts for training time and attrition.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Estimate staffing for support and operations queues
• Check workload imbalance and service target risks
• Build quarterly hiring plans with ramp time and attrition

Use the Archon CLI to launch AI workflows on projects, inspect their results, and approve, reject, cancel, or resume runs. The skill also guides setup, configuration changes, workflow creation, and improvements to workflow prompts.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Running an AI workflow to address an issue
• Asking Archon to review a pull request
• Inspecting, approving, or resuming a run

Diagnose GKE TPU slice creation failures caused by shared nodes. Check slice status, partition labels, and monitoring data to identify conflicting resource groups, then provide resolution steps for the user to apply.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating node overlap behind SliceCreationFailed
• Finding slices that share nodes across partition shapes
• Checking whether resource conflicts existed at incident time