'checkpoint' 검색 결과

Expert blueprint for player mortality loops: progress-index checkpoints, soul graves, ghost/spirit layers, I-frame restitution, world-progress persistence, and death analytics. Use when implementing respawn, shrine checkpoints, corpse-run retrieval, or second-chance mechanics. Keywords revival, respawn, checkpoint, soul grave, ghost mode, I-frames, death analytics, progress index.

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Assesses decision reversibility and risk at critical checkpoints. Use when a workflow reaches a high-stakes branch needing escalation check.

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Use this skill when designing the FSC client onboarding process — mapping document collection touchpoints, approval steps, compliance checkpoint sequencing, and welcome journey handoffs. Trigger keywords: client onboarding design, onboarding workflow requirements, document collection flow, compliance checkpoint, welcome journey, intake process design. NOT for configuring the Action Plan template itself — use admin/fsc-action-plans. NOT for OmniScript screen and branching requirements — use admin/omniscript-flow-design-requirements.

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Use when a SQLite database in WAL mode has a growing -wal file, repeated incomplete checkpoints, SQLITE_BUSY, disk-pressure risk, or reader slowdowns that may indicate checkpoint starvation.

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Decompose multi-step work into a revisable subgoal plan with stable step IDs, checkpointing, and plan-ahead execution. Load when a task needs explicit planning before action, subgoal decomposition, plan checkpointing, or ReCAP-style plan-ahead execution. Also triggers on "structured plan", "plan with steps", "subgoal graph", "revise the plan", "checkpoint plan", or multi-step tasks where naive one-shot execution would be fragile. Skips trivial single-step tasks. Pairs with dynamic-routing on failure. Distinct from process-decomposer (registry/triage) and problem-to-plan (doc deliverables).

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Runs Hyper-V VMs and Windows Sandbox. Trigger to create, start, stop, check or delete a VM (Gen2, secure boot, virtual TPM, settings proved); to take, list or restore a checkpoint; to test a plugin install on a clean machine from a golden checkpoint; to open a suspicious file or zip in a disposable sandbox with networking off; when Hyper-V says access denied; or say hypervrunner (audit, setup, vm, checkpoint, teardown, cleanroom, sandbox, status). Runs as a Hyper-V Administrators member, never self-elevated. Containers and WSL are dockerrunner's; whether code is trustworthy trustwarden's; secrets in returned files shieldwarden's.

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기능 추가와 버그 수정 등 개발 작업을 변경 규모에 따라 조사·계획·테스트·검토 단계로 배분합니다. 관련 스킬이 공유하는 절차에 계획 승인과 커밋 전 확인을 포함합니다.

• 기능 추가와 버그 수정 절차를 통일합니다
• 변경 규모에 맞는 단계를 선택합니다
• 설계 문서에서 MVP 개발을 진행합니다

PyTorch FSDP를 활용한 대규모 모델의 분산 학습 구현과 오류 조사를 돕습니다. 공식 문서 기반 자료와 코드 예제로 분할 전략, 혼합 정밀도, 학습 상태 저장을 검토합니다.

• 대규모 모델의 분산 학습을 구현합니다
• FSDP 적용 방법과 분할 전략을 비교합니다
• 혼합 정밀도와 체크포인트 예제를 확인합니다

Save an agent session’s state, settings, memory, and task progress, then restore it in another conversation. List saved sessions, inspect their details, and remove old ones when managing checkpoints for ongoing work.

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• Pause work and resume it in a later conversation
• Save progress checkpoints during long tasks
• Inspect saved sessions and their details

Create mathematical animations in ManimGL using Python, with guidance for equations, graphs, 3D scenes, and camera movement. Includes interactive development workflows, examples, and templates for testing changes and rendering scenes to video.

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• Animate equations and mathematical derivations
• Visualize functions and graphs
• Build 3D scenes with camera movement

Breaks long-running goals into small, verifiable steps, tracks progress, and records evidence from actual operation. Checkpoints preserve what has been proven so work can resume after interruptions and continue toward explicit completion criteria.

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• Managing a long-running development goal
• Resuming work after an interruption
• Recording evidence for completion criteria

Recover context from local coding-agent transcripts and produce a checkpoint covering the objective, completed work, blockers, and next step. Explicit import requests support read-only recovery, with continuation governed by the current workspace rules and the user's request.

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• Recover context using a session ID
• Summarize unfinished work from local logs
• Review JSONL exports or Markdown handoffs

Build and evaluate single-agent reinforcement learning experiments, where an agent learns actions from rewards. Use Stable Baselines3 to choose algorithms, create environments, monitor training, and save models with the normalization data needed for consistent evaluation.

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• Set up a reproducible reinforcement learning experiment
• Create and validate a custom Gymnasium environment
• Monitor training and save checkpoints

Organize treatment decisions already supplied and verified by authorized licensed professionals into local JSON documents. Check document structure, source references, declared review status, and supplied dates to support documentation handoffs without making clinical decisions.

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• Organize verified clinician-authored treatment records
• Check source references and missing documentation fields
• List checkpoint dates already supplied by clinicians

Work with Waypoint models to analyze microbiome samples, the communities of microbes in a sample. Convert taxonomic abundance tables, generate numerical sample representations, configure further training for prediction tasks, and evaluate models on the Compass benchmark.

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• Convert microbiome abundance tables into Waypoint inputs
• Generate sample vectors for clustering and visualization
• Configure further training with sample labels

Build and check clinical machine learning workflows with PyHealth, from loading electronic health records, signals, or images to training and evaluation. Includes patient-level data splits, prediction task checks, model checkpoints, and medical code mapping.

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• Prototype mortality and readmission prediction models
• Check patient splits and data leakage
• Train and evaluate sleep staging models

Organize PyTorch models and data pipelines with PyTorch Lightning, automate training, save checkpoints, and track experiments. Includes templates and guidance for configuring training across multiple GPUs or TPUs.

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• Restructure PyTorch training code into LightningModules
• Build reusable data preparation and loading pipelines
• Configure checkpoints, early stopping, and experiment logs

Plan and review PufferLib reinforcement learning workflows, where agents learn through rewards. Covers version-specific environment checks, parallel execution, training and evaluation plans, plus checkpoint inspection before loading saved models.

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• Choose APIs for a specific PufferLib version
• Check environment behavior before training
• Create training and evaluation plans with separate seeds

Audit an AI agent's completion claims using existing logs, artifacts, approvals, and deployment records. Reconstruct the run and assess each claim separately, distinguishing proven results, partial support, contradictory evidence, and missing proof without rerunning the work.

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• Checking whether an agent's completion report has supporting evidence
• Reviewing tool calls, retries, and checkpoint recovery
• Matching approvals and deployments to the same artifact

Coordinate multiple skills to deliver a SaaS prototype, audit web security, build an AI agent, test browser behavior, or design a domain model. Verify each step before continuing and report completed work with supporting evidence.

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• Delivering a SaaS minimum viable product
• Running a web application security audit
• Building an AI agent system with checkpoints

Test how agent workflows recover from repeatable simulated failures, including tool timeouts, worker restarts, and lost memory. Produces a fault matrix, event timeline, and verdict showing whether state, safety boundaries, and retry budgets were preserved.

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• Test recovery from MCP or tool timeouts
• Verify checkpoint recovery after a worker restart
• Check outcomes when one parallel branch fails

Follow Conductor's development plans using test-driven development: write a failing test, implement the change, then improve the code. Manage phase verification, progress updates, and focused git commits as tasks move toward completion.

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• Implementing tasks from plan.md
• Following the test-driven development cycle
• Verifying completion at phase checkpoints

Creates a compact handoff from verified work status, including commits, branches, automated checks, blockers, decisions, and the next action. Saves a durable record so another session or the user can resume without reconstructing the conversation.

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• Handing off work before ending a session
• Moving ongoing work into a fresh conversation
• Saving a checkpoint during a long operation

Ask a more capable agent for a concrete execution plan for complex work. Request ordered steps, dependencies, checkpoints, and verification methods, then have the requesting agent assess the plan and carry out the work itself.

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• Break complex work into actionable steps
• Plan changes across multiple modules
• Define checkpoints and completion criteria