lab-tools
9
implement-audit-trail
規制環境のRプロジェクトに監査証跡機能を実装します。ロギング、来歴追跡、 電子署名、データ整合性チェック、21 CFR Part 11準拠を対象とします。 RプロジェクトにelectronicRecords準拠（21 CFR Part 11）が必要な場合、 分析中の誰が何をいつ行ったかを追跡する必要がある場合、データ来歴追跡の実装時、 または規制提出用の改ざん防止分析ログの作成時に使用します。
pjt222
research
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lab-tools
9
label-training-data
Label Studioまたは同様のツールを使用して体系的なデータラベリングワークフローを設定する。 品質管理を実装し、アノテーター間一致度を測定し、ラベラーチームを管理し、ラベル付き データをML訓練パイプラインに統合する。ラベル付き訓練データを必要とする教師あり MLプロジェクトを開始する時、モデル性能がラベル付き例の不足により制限されている時、 テキスト・画像・音声・動画のラベリングを行う時、または能動学習を実装して最も 価値のある例を優先する時に使用する。
pjt222
research
lab-tools
9
troubleshoot-print-issues
Diagnose and fix common 3D printing failures through systematic symptom analysis. Covers adhesion, stringing, layer shifts, warping, and under/over-extrusion issues. Use when a print fails during the first layer or partway through, finished prints have quality defects (stringing, blobs, gaps), dimensional accuracy issues occur (warping, elephant foot), layer adhesion fails, or new material or hardware changes are causing inconsistent results.
pjt222
research
lab-tools
9
maintain-hand-tools
Die 8 essentiellen Gartenhandwerkzeuge durch Schaerfen, Griffpflege, Rostvorbeugung und saisonale Lagerung warten. Umfasst Bypass-Gartenschere, Hori-Hori, Handgabel, Pflanzkelle, Astsaege, Schleifstein, Giesskanne und Bodenrechen. Anwenden nach jeder Gartensitzung fuer schnelle Reinigung, monatlich waehrend der Wachstumssaison zum Schaerfen und Oelen, am Saisonende zur Winterlagerungsvorbereitung, vor dem Fruehling zur Bereitschaftspruefung oder wann immer ein Werkzeug stumpf oder rostig wirkt.
pjt222
research
lab-tools
9
evaluate-levitation-mechanism
Evaluate and compare levitation mechanisms for a given application through a structured trade study. Covers magnetic (passive diamagnetic, active feedback, superconducting), acoustic (standing wave, phased array), aerodynamic (hovercraft, air bearings, Coanda effect), and electrostatic (Coulomb suspension, ion traps) mechanisms. Use when selecting the most appropriate levitation approach for transport, sample handling, display, bearings, or precision measurement applications.
pjt222
research
lab-tools
9
label-training-data
Einrichten systematic data labeling workflows using Label Studio or similar tools. Implement quality controls, measure inter-annotator agreement, manage labeler teams, and integrate labeled data into ML training pipelines. Verwenden wenn starting a supervised ML project that requires labeled training data, when model performance is limited by insufficient labeled examples, when labeling text, images, audio, or video, or when implementing active learning to prioritize the most valuable examples.
pjt222
research
lab-tools
9
track-ml-experiments
Set up MLflow tracking server for experiment management, configure autologging for popular ML frameworks, compare runs with metrics and visualizations, and manage artifacts in remote storage backends for reproducible machine learning workflows. Use when starting a new ML project that requires experiment tracking, migrating from manual logs to automated tracking, comparing multiple training runs systematically, or building reproducible ML workflows with full lineage tracking.
pjt222
research
lab-tools
9
track-ml-experiments
Set up MLflow tracking server for experiment management, configure autologging for popular ML frameworks, compare runs with metrics and visualizations, and manage artifacts in remote storage backends for reproducible machine learning workflows. Use when starting a new ML project that requires experiment tracking, migrating from manual logs to automated tracking, comparing multiple training runs systematically, or building reproducible ML workflows with full lineage tracking.
pjt222
research
lab-tools
9
run-experiment
Use when running a new experiment. Follows the two-phase protocol from LAB.md.
cybertronai
research
scientific-computing
9
model-markov-chain
Diskrete oder kontinuierliche Markov-Ketten erstellen und analysieren, einschliesslich Uebergangsmatrix-Konstruktion, Zustandsklassifikation, Berechnung stationaerer Verteilungen und mittlerer Erstpassagezeiten. Verwenden beim Modellieren eines gedaechtnislosen Systems mit beobachteten Uebergangszaehlungen oder -raten, beim Berechnen von langfristigen stationaeren Wahrscheinlichkeiten, beim Bestimmen erwarteter Treffzeiten oder Absorptionswahrscheinlichkeiten, beim Klassifizieren von Zustaenden als transient oder rekurrent oder beim Aufbau einer Grundlage fuer Hidden Markov Models oder Reinforcement Learning MDPs.
pjt222
research
scientific-computing
9
simulate-stochastic-process
Simulate stochastic processes (Markov chains, random walks, SDEs, MCMC) with convergence diagnostics, variance reduction, and visualization. Use when generating sample paths for estimation, prediction, or visualization; when analytical solutions are intractable; running Monte Carlo estimation needing convergence guarantees; validating analytical results against empirical simulation; or sampling from complex posteriors via MCMC.
pjt222
research
scientific-computing
9
model-markov-chain
Build and analyze discrete or continuous Markov chains including transition matrix construction, state classification, stationary distribution computation, and mean first passage times. Use when modeling a memoryless system with observed transition counts or rates, computing long-run steady-state probabilities, determining expected hitting times or absorption probabilities, classifying states as transient or recurrent, or building a foundation for hidden Markov models or reinforcement learning MDPs.
pjt222
research
scientific-computing
9
simpy
A process-based discrete-event simulation framework. Use for modeling queuing systems, supply chains, manufacturing processes, network simulation, project management, and any system where events occur at specific points in time. Load when working with discrete event simulation, process modeling, resource allocation, virtual time, simpy.Environment, simpy.Resource, or event-driven simulation.
tondevrel
research
scientific-computing
9
model-markov-chain
Construir y analizar cadenas de Markov discretas o continuas incluyendo construcción de matriz de transición, clasificación de estados, cálculo de distribución estacionaria y tiempos medios de primer paso. Usar al modelar un sistema sin memoria con conteos o tasas de transición observados, al calcular probabilidades de estado estacionario a largo plazo, al determinar tiempos de golpe esperados o probabilidades de absorción, al clasificar estados como transitorios o recurrentes, o al construir una base para modelos ocultos de Markov o MDPs de aprendizaje por refuerzo.
pjt222
research
scientific-computing
9
pymatgen-core
Use for pymatgen core objects and structure manipulation: Element/Specie/Composition, Lattice/Site/Structure/Molecule, oxidation states, structure edits, transformations, and serialization.
Hongyu-yu
research
scientific-computing
9
analyze-diffusion-dynamics
Analyze the dynamics of diffusion processes using stochastic differential equations, Fokker-Planck equations, first-passage time distributions, and parameter sensitivity analysis. Use when deriving probability density evolution for a continuous-time diffusion process, computing mean first-passage times for bounded diffusion, analyzing how drift and diffusion parameters affect process behavior, or validating closed-form solutions against stochastic simulation.
pjt222
research
domain-utilities
9
hunting-for-dns-based-persistence
使用被动 DNS 数据库、SecurityTrails API 和 DNS 审计日志分析，狩猎 DNS 劫持、悬空 CNAME 记录、通配符 DNS 滥用和未授权区域修改等 DNS 持久化机制。
killvxk
tools