monitor-experiment
Monitor running experiments, check progress, collect results. Use when user says "check results", "is it done", "monitor", or wants experiment output.
Monitor running experiments, check progress, collect results. Use when user says "check results", "is it done", "monitor", or wants experiment output.
Use when experiments complete to judge what claims the results support, what they don't, and what evidence is still missing. Codex MCP evaluates results against intended claims and routes to next action (pivot, supplement, or confirm). Use after experiments finish — before writing the paper or running ablations.
Analyze ML experiment results, compute statistics, generate comparison tables and insights. Use when user says "analyze results", "compare", or needs to interpret experimental data.
Monitor running experiments, check progress, collect results. Use when user says "check results", "is it done", "monitor", or wants experiment output.
Use when experiments complete to judge what claims the results support, what they do not, and what evidence is still missing. A secondary Codex agent evaluates results against intended claims and routes to the next action (pivot, supplement, or confirm). Use after experiments finish - before writing the paper or running ablations.
Guide for adding or updating slime tests and CI wiring. Use when tasks require new test cases, CI registration, test matrix updates, or workflow template changes.
Records completed work progress to .trellis/workspace/ journal files after human testing and commit. Captures session summaries, commit hashes, and updates developer index files for future session context. Use when a coding session is complete, after the human has committed code, or to persist session knowledge for future AI sessions.
Records completed work progress to .trellis/workspace/ journal files after human testing and commit. Captures session summaries, commit hashes, and updates developer index files for future session context. Use when a coding session is complete, after the human has committed code, or to persist session knowledge for future AI sessions.
Collect volatile forensic evidence from a compromised system following order of volatility, preserving memory, network connections, processes, and system state before they are lost.
Indicator lifecycle management tracks IOCs from initial discovery through validation, enrichment, deployment, monitoring, and eventual retirement. This skill covers implementing systematic processes f
Acquire and analyze mobile device data using Cellebrite UFED and open-source tools to extract communications, location data, and application artifacts.
Autonomous experiment loop that tries ideas, measures results, keeps what works, and discards what doesn't. Use when the user asks to optimize a metric, run an experiment loop, improve performance iteratively, or automate benchmarking.
Plan reproducible ML experiment runs with explicit parameters, metrics, and artifacts. Use before model training to standardize tracking-ready experiment definitions.
Design, log, compare, and score prompt experiments so users can systematically improve outputs instead of guessing.
Systematically improve code through structured analysis-mutation-evaluation loops. Adapted from ALMA (Automated meta-Learning of Memory designs for Agentic systems). Use when iterating on code quality, optimizing implementations, debugging persistent issues, or evolving a design through multiple improvement cycles. Replaces ad-hoc "try and fix" with disciplined reflection, variant tracking, and principled selection of what to change next.
Track book availability on Libby/OverDrive libraries. Search library catalogues, manage a watchlist, and get notified when books are added. Use for "libby", "check libby", "libby watchlist", "is book on libby", "book available", "overdrive", "library catalogue", "ספרייה", "ספרים".
Design and simulate adaptive clinical trials with interim analyses, sample size re-estimation, and early stopping rules. Evaluate Type I error control, power, and expected sample size via Monte Carlo simulation before trial initiation.
Analyze laboratory alumni career trajectories and outcomes to provide data-driven career guidance for current students and postdocs. Tracks industry vs academia distribution, identifies career pathways, and generates personalized recommendations based on degree level and research interests.
Generate ARRIVE 2.0 compliant animal research protocols with structured experimental design, sample size calculations, and reporting checklists. Ensures transparency, reproducibility, and ethical compliance in in vivo studies.
Sort chemicals by compatibility for safe laboratory storage. Prevents dangerous reactions by segregating incompatible chemicals (acids, bases, oxidizers, flammables) and provides storage recommendations compliant with safety regulations.
Use when cleaning clinical trial data, preparing data for FDA/EMA submission, standardizing SDTM datasets, handling missing values in clinical studies, detecting outliers in lab results, or converting raw CRF data to CDISC format. Cleans and standardizes clinical trial data for regulatory compliance with audit trails.
Monitor and summarize competitor clinical trial status changes from ClinicalTrials.gov. Trigger: When user asks to track clinical trials, monitor trial status changes, get updates on specific trials, or analyze competitor trial activities. Use cases: Pharma competitive intelligence, trial monitoring, status tracking, recruitment updates, completion alerts.
IoT monitoring simulation to predict CO2 tank depletion and prevent weekend gas outages in cell culture facilities. Monitors cylinder pressure, calculates consumption rates, and provides early warnings for timely replacement.