Results for “checkpoints”

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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Use when a skill is brittle, drifting, repeatedly failing, or needs a Reliable Skill Contract. Trigger for phrases like skill lifeguard, reliable skill, self-maintaining skill, negative examples, verification checkpoints, drift signals, replay hooks, or failure log to skill patch. Audits or patches skills so high-value workflows include explicit forbidden behaviors, checkpoints, machine-checkable done conditions, replay or smoke hooks, and drift detection.

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Ultimate MCP server for Civitai — search models, browse top images with prompts, download LoRAs/Checkpoints, analyze trends. Use when user asks about AI models, LoRAs, checkpoints, Stable Diffusion, Flux, image generation prompts, or Civitai.

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Explore a feature or CLI, repair verified defects, review adversarially, and leave one existing or new PR ready for its authorized reviewer. Use for "explore X", "test and ship X", "run the pipeline on X", "stress-test X", or "take X through the full pipeline" when exploration and integration matter. Supports lean autonomous, structured economy (default), and explicit hard-gated runs. Routine checkpoints do not require user approval; actual founder, L4, security, schema and merge gates always apply. Not for a one-file edit with no exploration need. Uses scripts/state.sh for resumable checkpoints.

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Autonomer Langlauf-Modus ohne laufende Rückfragen — Startbedingungen (Ziel, Scope, Budgets, Checkpoints), Entscheidungsstufen und Abbruchregeln für unbeaufsichtigte Läufe; erhöht die Ausdauer, nicht die Berechtigung. Nutzen, wenn ausdrücklich ein längerer autonomer Lauf gewünscht ist.

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Guide for the Tinker CLI — managing training runs, checkpoints, downloading weights, and publishing to HuggingFace. Use when the user asks about CLI commands, listing runs, managing checkpoints from the terminal, or uploading to HF Hub.

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Authors reproducible bioinformatics pipelines with Snakemake - rules wired by output-file pattern, wildcards and expand() for sample fan-out, checkpoints for runtime-unknown outputs, resource/retry escalation, and conda/container software deployment on HPC and cloud. Use when deciding rule-based (Snakemake) vs channel/dataflow (Nextflow) authoring; wiring rules by OUTPUT-file pattern rather than imperative order; using wildcards + expand() for sample fan-out and constraining them to stop silent mis-routing; adding checkpoints when the set of outputs is unknown until a step runs (dynamic DAG); diagnosing why a job reran (or did not) under the mtime-plus-provenance trigger set; escalating memory on retry for OOM-killed jobs; and porting a Snakemake 7 `--cluster`/remote-provider command to the Snakemake 8+ executor-plugin and storage-plugin model (snakemake-executor-plugin-slurm) with `--software-deployment-method`.

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Verify completed code changes in six stages: build, type checks, lint, tests with coverage, searches for possible secrets, and diff review. Produces a pass/fail report with issues to fix and readiness for a pull request.

• Verifying code after completing a feature
• Checking for problems after refactoring
• Reviewing readiness before creating a pull request

Change an existing feature that works but needs different behavior. The skill updates tests to reflect the new requirements, adjusts the implementation, and coordinates review with approval checkpoints before committing the changes.

• Changing an existing alert threshold
• Adjusting a working feature to new requirements
• Updating tests before changing behavior

Coordinate code refactoring, restructuring code while preserving its behavior. Check existing tests before changes, work in small steps, review the result, and pause for approval of the plan and before committing.

• Extracting a module without changing behavior
• Removing duplicate or unused code
• Reducing nesting and clarifying names

Set up large language model pretraining with TorchTitan and PyTorch. Configure training across multiple GPUs, combine four ways of splitting the workload, enable Float8 calculations on H100 hardware, and manage checkpoints for saving and resuming training.

• Pretraining Llama 3.1 8B on a single node
• Launching training across multiple nodes with SLURM
• Configuring Float8 training for H100 GPUs

Evaluate large language models on shared tests of knowledge, reasoning, and code generation with lm-evaluation-harness. Save scores and individual predictions, compare models in tables, and track performance across saved stages of model training.

• Evaluating models on MMLU and GSM8K
• Building comparison tables for multiple models
• Tracking scores across training checkpoints

Helps organize PyTorch model training with LightningModule and Trainer, including validation, logging, model saving, and early stopping. It also guides distributed training across multiple GPUs and settings for reduced-precision computation.

• Converting existing PyTorch training code to Lightning
• Adding validation metrics and model checkpoints
• Setting up training across multiple GPUs

Implement and debug large-model training with PyTorch FSDP, which splits training state across devices. Consult documentation and code patterns for sharding choices, mixed precision, and checkpoints when building or reviewing distributed training code.

• Implement distributed training for large models
• Compare FSDP wrapping and sharding patterns
• Review mixed-precision and checkpoint examples

Set up GPU servers on Lambda Labs for machine learning training and inference. Follow workflows for SSH access, persistent storage, Python API operations, and distributed training that spreads work across multiple GPUs or servers.

• Set up a GPU server for long training jobs
• Fine-tune a large language model across multiple GPUs
• Keep datasets and checkpoints between sessions

Delegate implementation, bug fixes, and pull request reviews to Blackbox AI through the Hermes terminal. Run tasks across multiple AI models, evaluate their outputs, monitor longer jobs, and resume work from saved checkpoints.

• Delegating feature implementation and bug fixes
• Reviewing pull requests for bugs and security issues
• Comparing implementations from multiple AI models

Turn clear requirements into small implementation tasks with acceptance criteria, verification steps, dependencies, and estimated scope. Produce a written plan and ordered task list, with checkpoints and opportunities for parallel work identified before coding begins.

• Breaking a large feature into implementable tasks
• Ordering work by dependencies
• Defining completion criteria and verification steps

Create workflows that pause for approval and resume later, or scripts that distribute work across multiple agents. Choose a saved template for ongoing processes or a JavaScript script to collect structured results from parallel tasks.

• Build a workflow with approval checkpoints
• Split review work across multiple agents
• Collect structured results from research tasks

Track objectives across multiple conversations by defining milestones, saving progress and findings, and planning each session. Review completion criteria and flag schedule risks, scope changes, or dependencies that may require a different approach.

• Track progress on a feature spanning several weeks
• Carry research findings into the next session
• Review delays and scope changes in a migration project

Learn to create, discover, invoke, secure, evaluate, package, and port Agent Skills through one interactive lesson at a time. Practical checkpoints and quizzes track demonstrated progress in a local file, so later sessions resume where you stopped.

• Learn to build and invoke a minimal Agent Skill
• Understand discovery and automatic routing
• Check permission, sandbox, and trust boundaries

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.

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

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.

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

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.

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

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.

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