Plan reproducible ML experiment runs with explicit parameters, metrics, and artifacts. Use before model training to standardize tracking-ready experiment definitions.
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See how to installReview the instructions the agent will receive before you install.
Generate structured experiment plans that can be logged consistently in experiment tracking systems.
scripts/build_experiment_plan.py to generate consistent run plans.references/tracking-guide.md for reproducibility checklist.No reviews yet. Tell others what you thought.
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Scaffold MCP server projects and baseline tool contract checks. Use for defining tool schemas, generating starter server layouts, and validating MCP-ready structure.
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Generate reusable multi-step agent workflow blueprints. Use for trigger/action orchestration, deterministic workflow definitions, and automation handoff artifacts.
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Build incident response timelines and report packs from event logs. Use for detection-to-recovery reporting, phase tracking, and stakeholder-ready incident summaries.
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Prioritize vulnerability remediation using KEV-style exploitation context plus asset criticality. Use for CVE triage, patch order decisions, and remediation reporting.
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Map application security findings to OWASP Top 10 categories and generate remediation checklists. Use for normalized AppSec review outputs and category-level prioritization.
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Build transformer fine-tuning run plans with task settings, hyperparameters, and model-card outputs. Use for repeatable Hugging Face or PyTorch finetuning workflows.
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