'specialized' 검색 결과

Guide knowledge distillation, where a smaller language model learns from a larger teacher. Covers training from teacher responses or prediction scores, adjusting training settings, and comparing outputs to support model compression and specialized training.

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• Compressing a large language model into a smaller model
• Training a specialized model from teacher-generated examples
• Comparing distillation methods and training settings

Implement and troubleshoot large-model training with DeepSpeed across multiple GPUs. The skill covers memory optimization, parallel training, and numerical precision choices, alongside examples for storage transfers and models that divide work among specialized components.

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• Review settings for distributed model training
• Tune transfers between NVMe storage and tensors
• Configure training with specialized model components

Look up how to configure and organize Omnigent agents. Use examples to choose an executor, the agent's runtime, arrange instructions and skills, add local tools or external services, and define specialized child agents.

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• Check agent directory layout and config.yaml settings
• Choose an executor and harness
• Add skills, custom tools, or MCP services

Design workflows where specialized AI agents share recurring product management tasks. Define each agent’s role, required information, handoffs, and human review points, then create monitoring and evaluation plans for the overall workflow.

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• Designing parallel agents for weekly competitive research
• Defining agent roles and information handoffs
• Planning quality checks and cost monitoring

Search the web and specialized subject areas, run multiple searches in parallel, and extract page text from URLs. Use the bundled command-line tools to gather current information, check claims, and read beyond search snippets.

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• Researching current news and documentation
• Checking claims against multiple sources
• Searching specialized finance or academic sources

Create or update Claude skills that capture specialized knowledge and repeatable workflows. Guides the organization of instructions, scripts, references, and templates, then covers initialization, validation, and packaging into a distributable skill file.

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• Turning recurring work into a reusable Claude skill
• Organizing internal knowledge and API instructions
• Revising an existing skill after practical use

Create or update Claude skills that capture specialized knowledge and repeatable workflows. Guides the organization of instructions, scripts, references, and templates, then covers initialization, validation, and packaging into a distributable skill file.

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• Turning recurring work into a reusable Claude skill
• Organizing internal knowledge and API instructions
• Revising an existing skill after practical use

Turn specialized knowledge and repeatable workflows into an Agent Skill. Work through concrete usage examples, write SKILL.md, organize reusable scripts and resources, and validate the package before producing a distributable .skill file.

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• Turn recurring work into a reusable skill
• Package internal knowledge and API instructions
• Revise an existing skill after real use

Coordinate a seven-stage literature review, from defining a research question to collecting and screening papers, building a searchable knowledge database, and producing PRISMA documentation. Specialized agents handle execution, with human approval at key decisions.

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• Managing a systematic literature review
• Building a research knowledge repository
• Tracking approval of paper selection criteria

Coordinate research from question development through literature searches, analysis, and publication with specialized agents. Supports quantitative, qualitative, and mixed methods studies, plus systematic reviews, with human approval at key decisions about research direction and methods.

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• Refining research questions and choosing a theoretical framework
• Planning a systematic review and approving screening criteria
• Designing a qualitative study using interviews

View a dashboard for a social science research environment with 24 specialized agents. Check configuration, project stage, human review settings, and connection readiness for paper retrieval services and other research tools.

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• Check research environment configuration
• Review project stage and human checkpoints
• Find missing API settings

Solves CTF challenges by performing first-pass triage, identifying the dominant category, and routing execution to the right specialized ctf-* skill. Use when the user gives you a challenge bundle, a remote service, a suspicious file, or only a vague challenge description and you must determine where to start. Do not use it when the category is already clear and a specialized skill can be invoked directly; this is the dispatcher and recon entrypoint, not the deepest reference for category-specific techniques.

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Guides general use of AWS messaging and streaming services. Covers Amazon SQS, Amazon SNS, Amazon EventBridge, Amazon MQ, Amazon Kinesis Data Streams, Amazon Data Firehose, Amazon Managed Service for Apache Flink, and Amazon Managed Streaming for Apache Kafka (MSK). Use when reasoning about messaging and streaming patterns. Also identifies which AWS service owns each customer communication channel: email (Amazon SES), and WhatsApp, SMS, MMS, RCS, voice and mobile push (the AWS End User Messaging family of services). Routes the request to the specialized skill for that channel. Defers to the channel's specialized skill when the user already named a specific channel. In general, use specific skills or documentation searches for detailed service-specific questions. Do NOT use for MSK or Managed Service for Apache Flink questions, prefer specific skills. Does not configure customer communication channels; defers to specific skills.

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Use when the user wants a research-paper figure Skill Factory: build, patch, package, or use reusable specialized paper-figure-making skills from lawful literature/corpus evidence. Generated skills must use a specialized-skill-first workflow, full-feasible local PDF coverage where available, startup-plan-only first replies, target-paper candidate/final image isolation, mandatory image-embedded visual-structure explanation, mandatory non-target concept/modeling example display for abstract visual decisions, saved subtype/style illustration atlases, ChatGPT web Create image / ChatGPT Images 2.0 rendering, Codex $imagegen-first rendering, sample-image transfer rules, all-step/current-position state footers, and a mandatory first-round diverse candidate board followed by P6b/P6c paper-local best-practice optimization before final prompt construction.

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AWS Serverless workflow skill. Use this skill when the user needs Specialized skill for building production-ready serverless and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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AWS Serverless workflow skill. Use this skill when the user needs Specialized skill for building production-ready serverless and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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AWS Serverless workflow skill. Use this skill when the user needs Specialized skill for building production-ready serverless and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Specialized hardware interfaces and system reliability for Zephyr RTOS. Covers LVGL GUI development, Audio I2S/Codecs, Watchdog timers, and Fault Injection. Trigger when building human-machine interfaces (HMI), audio devices, or high-reliability mission-critical systems.

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Orchestrate a broad Incan review using specialized reviewer roles, parallel slice reports, and a canonical merged review report. Use when the user asks for delegated review, multi-agent review, or when a broad dirty worktree should be reviewed by specialized passes rather than one generic reviewer.

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Use when the user wants a research-paper figure Skill Factory: build, patch, package, or use reusable specialized paper-figure-making skills from lawful literature/corpus evidence. Generated skills must use a specialized-skill-first workflow, full-feasible local PDF coverage where available, startup-plan-only first replies, target-paper candidate/final image isolation, mandatory image-embedded visual-structure explanation, mandatory non-target concept/modeling example display for abstract visual decisions, saved subtype/style illustration atlases, ChatGPT web Create image / ChatGPT Images 2.0 rendering, Codex $imagegen-first rendering, sample-image transfer rules, all-step/current-position state footers, and a mandatory first-round diverse candidate board followed by P6b/P6c paper-local best-practice optimization before final prompt construction.

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Evolve an existing team composition by refining its structure in-place or creating a specialized variant. Covers assessing the current team against template and coordination patterns, gathering evolution requirements, choosing scope (adjust members, change coordination pattern, split/merge teams), applying changes to the team file and CONFIG block, updating version metadata, and synchronizing the registry and cross-references. Use when a team's member roster is outdated, coordination pattern no longer fits, user feedback reveals workflow gaps, a specialized variant is needed alongside the original, or agents have been added or removed from the library affecting team composition.

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Knowledge base from the DoD Risk, Issue, and Opportunity (RIO) Management Guide for Defense Acquisition Programs (OUSD R&E, Sep 2023 incl. Change 2.2). Use for the defense-acquisition mechanics of program risk management: the RIO definitions (risk/issue/opportunity), the five-step risk process (plan-identify-analyze-mitigate-monitor), 1-5 likelihood/consequence scoring and the 5x5 risk matrix, the four mitigation options (accept/avoid/transfer/control), burn-down plans, issue management (probability=1), opportunity management toward should-cost, cross-program/interface risk, tailoring RIO to the six AAF acquisition pathways (UCA/MTA/MCA/Software/DBS/Services), specialized methods (RMF, MBCRA/Cyber Table Top, Agile metrics, FMECA, ITRA/DTRAM), and institutionalizing RIO via the PRMP, boards (RMB/JRMB/ROMB/RWG), tools, tiered roles, and WBS/IMP/IMS/EVM/TPM integration. This is the DoD-acquisition complement to nasa-risk (general CRM/RIDM theory) and dau-se-guidebook (the SE process frame). Does NOT teach general risk theory from scratch, and defers specialized risk regimes (system safety/ESOH, HSI, detailed cyber/RMF, spectrum) to their own DoD instructions and the relevant packs.

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Multi-agent autonomous startup system for Claude Code. Triggers on "Loki Mode". Orchestrates 100+ specialized agents across engineering, QA, DevOps, security, data/ML, business operations, marketing, HR, and customer success. Takes PRD to fully deployed, revenue-generating product with zero human intervention. Features Task tool for subagent dispatch, parallel code review with 3 specialized reviewers, severity-based issue triage, distributed task queue with dead letter handling, automatic deployment to cloud providers, A/B testing, customer feedback loops, incident response, circuit breakers, and self-healing. Handles rate limits via distributed state checkpoints and auto-resume with exponential backoff. Requires --dangerously-skip-permissions flag.

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Design and coordinate multi-agent systems where specialized agents work together to solve complex problems. Covers agent communication, task delegation, workflow orchestration, and result aggregation. Use when building coordinated agent teams, complex workflows, or systems requiring specialized expertise across domains.

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