'lmi' 검색 결과

Evaluate, configure, and migrate workloads to AWS Lambda Managed Instances (LMI). Triggers on: Lambda Managed Instances, LMI, capacity provider, multi-concurrency Lambda, dedicated instance Lambda, EC2-backed Lambda, cold start elimination, Graviton Lambda, instance type for Lambda, scheduled scaling for LMI, Lambda cost optimization with Reserved Instances or Savings Plans. Also trigger when users describe high-volume predictable workloads seeking cost savings, want to scale LMI capacity on a schedule, or compare Lambda vs EC2 for steady-state traffic. For standard Lambda without LMI, use the aws-lambda skill instead.

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Use this skill for MolMIM, NVIDIA's BioNeMo NIM microservice for small-molecule latent-space generation and optimization. Invoke for MolMIM, molecular embeddings, hidden states, latent decoding, sampling around a seed SMILES, CMA-ES guided molecule generation, QED or plogP optimization, hosted NVIDIA API calls, or local Docker deployment.

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Evaluates, configures, and migrates workloads to AWS Lambda Managed Instances (LMI). Runs Lambda Event Functions (standard Lambda) on EC2 instances in the user's account while AWS manages provisioning, patching, scaling, routing, and load balancing. Triggers when queries mention Lambda Managed Instances, LMI, capacity providers, multi-concurrent execution environments, EC2-backed Lambda, persistent Lambda instances, PerExecutionEnvironmentMaxConcurrency, CapacityProviderConfig, cold start elimination via dedicated instances, migrating Lambda Event Functions to managed instances, or cost comparison between Lambda Event Functions and LMI with Savings Plans or Reserved Instances. Also covers long-running and asynchronous workloads and the 90-minute (5400s) function timeout for asynchronous and event-source-mapping (SQS, Kinesis, DynamoDB Streams / ESM) invocations, including how to raise the function timeout up to 90 minutes / 5400s and the related duration limits.

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Track the full Toulmin epistemic ancestry of every claim produced by a multi-agent swarm. Each agent output is tagged with an argument chain (claim / data / warrant / relationship) derived from SwarmTracer spans and LineageEdge records, so operators can zoom into any sub-claim and see precisely which agents asserted it, what triggered each assertion, and how contradictions propagated or were synthesised away. The SwarmTracer pattern — wrapping the SwarmAgentExecutor to capture spans, record messages, and build a lineage graph — is the concrete runtime substrate. Argumentative lineage lifts that graph into the Toulmin vocabulary so reasoning provenance is both machine-queryable and human-interpretable.

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Tone down interfaces that feel visually overwhelming by refining colors, typography, spacing, decoration, and motion. Preserve important emphasis and recognizable character while creating a calmer layout that remains readable and easy to use.

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• Softening overly vivid colors
• Reducing competing bold text and large elements
• Simplifying excessive decoration and animation

Generate argument structure scaffolds using Toulmin, PEEL, or CER frameworks for a specific claim or question. Use when teaching argumentative or analytical writing across any subject.

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Generate argument structure scaffolds using Toulmin, PEEL, or CER frameworks for a specific claim or question. Use when teaching argumentative or analytical writing across any subject.

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Turn articles, notes, or a topic into an explainer video without filming a presenter. The workflow plans the story, creates text and diagram visuals, adds narration when needed, and prepares an MP4 in HyperFrames.

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• Turning an article into an explainer video
• Explaining a concept with diagrams and text
• Creating a narrated how-to video

Analyze drug concentration and response data to calculate exposure, fit models of drug movement and effects, and simulate dosing schedules. Produces exploratory research results with explicit assumptions and diagnostic checks.

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• Calculate exposure and half-life from concentration data
• Compare models of drug concentration over time
• Simulate dosing schedules for research

Inspect Mac performance to find processes using excessive CPU, GPU, memory, or energy. Combine measurements with explanations of known system processes to diagnose slowness, heat, and startup load, then recommend actions without changing system settings.

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• Find the cause of a slow Mac
• Investigate loud fans and overheating
• Check memory pressure and swap activity

Reduce oversized LifeOS instruction files while preserving distinct rules. Remove obsolete entries first, then propose approved merges, shorter wording, or relocation to reference files, with size measurements and reversible commits documenting the changes.

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• Reducing an oversized CLAUDE.md
• Consolidating overlapping operational rules
• Cleaning up accumulated proposal entries

Develop fiction from existing ideas, notes, or partial drafts. Organize themes, character changes, plot, setting, relationships, reader knowledge, and prose, then use writer-approved plans to guide drafting and revision from short stories to book series.

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• Turning scattered novel notes into a story plan
• Exploring character changes and themes
• Drafting scenes from an approved story bible

Test an idea, strategy, or investment against 11 future time horizons, from six months to 50 years. Compare scenarios with probability ranges, identify structural risks, and review or update the underlying world models.

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• Testing an investment thesis across time horizons
• Reviewing long-term risks to a business model
• Updating future scenarios after major events

Collect and update 68 US economic and social indicators, then summarize trends over ten, five, two, and one years. Produce a report covering relationships between indicators, unusual patterns, and topics that warrant further research.

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• Refreshing US economic and social indicators
• Comparing short- and long-term economic trends
• Examining relationships across prices, jobs, and housing

Design web interfaces, build prototypes, and integrate changes into existing apps while respecting their visual rules and components. Choose direct code generation or Claude Code design commands, with an experimental browser canvas option for visual review.

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• Improve a landing page within an existing app
• Design a dashboard using existing visual rules
• Sync a codebase with Claude Design

Find concrete LifeOS improvements in Anthropic updates, practitioner examples, and internal reflections. Check each candidate against the current setup, previous decisions, and compatibility, then present actionable recommendations tied to specific files or components.

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• Checking Anthropic updates for LifeOS improvements
• Finding improvements in internal reflections
• Researching a candidate feature in more detail

Operate Chrome or Brave using signed-in browser sessions to verify deployed pages, reproduce bugs, inspect page structure, and examine errors or network traffic. An optional macOS bridge extends control to native applications and operating system input.

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• Verify page appearance after deployment
• Reproduce blank pages and disappearing content
• Inspect console errors and network traffic

Install LifeOS into an existing AI agent environment, then use a guided interview to capture current circumstances and goals for the Pulse dashboard. The skill also handles installation checks, updates, and removal.

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• Set up LifeOS in an existing AI agent environment
• Capture current circumstances and goals through an interview
• Check installation problems and suggested fixes

Structure problem solving around clear goals, competing explanations, experiments, and measurable results. Use the cycle to diagnose bugs, validate features, compare prompts, or evaluate an early product, then choose the next step based on evidence.

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• Diagnosing a bug with a quick investigation
• Comparing prompt structures through experiments
• Defining success criteria for a feature

Improve code, prompts, skills, or agent definitions through repeated changes and evaluation. Measure numerical targets such as latency or size, or use an LLM to judge pass-or-fail criteria, keeping changes that improve the result.

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• Improving page load performance
• Reducing build output size
• Refining prompts against evaluation criteria

Explore explanations for poorly understood phenomena through repeated hypothesis generation and selection. Compare causal mechanisms, test what happens when components change, and retain distinct candidates with predictions, rival comparisons, and clearly stated unresolved questions.

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• Developing hypotheses for an unexplained phenomenon
• Comparing competing causal explanations
• Identifying predictions that distinguish theories

Create and improve prompts for agents, workflows, and AI evaluations. Reusable Handlebars templates, which insert data into a fixed structure, help generate consistent instructions from YAML or JSON inputs for use in other tasks.

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• Improving prompts for recurring tasks
• Generating agent briefs from templates
• Creating AI evaluation and scoring prompts

Investigate incidents and recurring defects with structured methods such as Five Whys and Fishbone cause maps. Identify multiple contributing conditions, build a blame-free account of what happened, and propose practical changes to prevent similar failures.

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• Review a production outage
• Investigate recurring defects
• Compare failing and working environments

Use cmux on macOS to organize visible teams of AI agents, send prompts, inspect terminal output, and monitor completion. Set up role-based teams or have several agents investigate the same problem; live operation remains unproven in the supplied material.

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• Launch a team to investigate an intermittent test failure
• Have multiple agents investigate the same bug
• Send follow-up prompts to a running agent