'trajectories' 검색 결과

Python library for single-particle tracking (SPT) in video microscopy via the Crocker-Grier algorithm. Locate particles (fluorescent spots, colloids, vesicles, cells) per frame, link into trajectories, filter short tracks, and compute MSD for diffusion analysis. 2D/3D with subpixel accuracy; reads TIF stacks, AVI, image series via pims. Use for quantitative SPT and diffusion coefficient extraction from fluorescence or brightfield video.

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3Dmol.js WebGL molecular visualization emitted as self-contained HTML. Render structures (PDB/SDF/XYZ/MOL2/cube) with stick, sphere, cartoon, line, and surface styles; animate trajectories with a frame-delay (interval, ms) control; and animate vibrational normal modes via vibrate() from per-atom dx/dy/dz displacements or from precomputed frames. Output standalone HTML that loads 3Dmol from a CDN, with optional play/pause and speed controls. Use for transition-state imaginary-mode animations, MD or reaction-path playback, docking poses, and orbital/density isosurfaces. For static 2D chemical structure drawings use rdkit-chemdraw-cdxml; for 2D statistical plots use matplotlib or plotly.

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Testing strategies, patterns, and tools for robotics software. Use this skill when writing unit tests, integration tests, simulation tests, or hardware-in-the-loop tests for robot systems. Trigger whenever the user mentions testing ROS nodes, pytest with ROS, launch_testing, simulation testing, CI/CD for robotics, test fixtures for sensors, mock hardware, deterministic replay, regression testing for robot behaviors, or validating perception/planning/control pipelines. Also covers property-based testing for kinematics, fuzz testing for message handlers, and golden-file testing for trajectories.

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Think and work like an expert Critical Care Researcher. Use when a task calls for Critical Care Researcher judgment. Reasons from acute physiology trajectories, modular organ dysfunction, and cluster-aware trial design through APACHE/SAPS/SOFA scoring, Berlin/Sepsis-3/KDIGO definitions, MIMIC-IV phenotyping, and target-trial emulation with clone-censor-weighting while treating immortal-time bias, cluster contamination, competing risks from early death, and sepsis-phenotype cohort inflation as first-class failure modes.

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Think and work like an expert Developmental Psychologist. Use when a task calls for Developmental Psychologist judgment. Reasons from developmental trajectories, measurement invariance, and familial-environmental context through age-normed instruments (Bayley, WPPSI/WISC, CBCL, MacArthur-Bates CDI), false-belief and violation-of-expectation paradigms, and lme4/lavaan growth models while treating verbal-demand confounding of theory of mind, informative attrition, adult-normed task misapplication, and parent-versus-direct-assessment divergence as first-class failure modes.

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Think and work like an expert Atmospheric Chemist. Use when a task calls for Atmospheric Chemist judgment. Reasons from coupled photochemical OH-NOx-VOC radical budgets, heterogeneous aerosol uptake, and NOx-limited versus VOC-limited regimes through MCM/F0AM and GEOS-Chem/CMAQ/WRF-Chem models, OH-reactivity closure, PMF on AMS factors, HYSPLIT trajectories, and EKMA isopleths while treating chamber wall losses, instrument cross-sensitivities and inlet artifacts, and emission-inventory error as first-class failure modes.

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Use when designing space missions, computing launch windows, optimizing trajectories, analyzing payload constraints, or planning mission phases and contingencies. Use when "mission design, launch window, trajectory optimization, mission timeline, mass budget, propellant budget, mission phases, porkchop plot, C3, gravity assist, low thrust, mission architecture, LEO, GEO, interplanetary, " mentioned.

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Use when computing orbits, planning maneuvers, propagating trajectories, or analyzing orbital perturbations for spacecraft or celestial bodies. Use when "orbit, trajectory, maneuver, delta-v, Hohmann, Keplerian, perturbation, J2, TLE, orbital elements, semi-major axis, eccentricity, inclination, RAAN, spacecraft propagation, Lambert solver, interplanetary, " mentioned.

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Builds QA harnesses and agent evals. Use when evaluating tool, trace, red-team, regression, multi-agent, or carried-workspace coding trajectories. Not for metric design (ai-evals).

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Measures AI coding impact and extension robustness. Use when tracking delivery, quality trajectories, cost, experience, pilots, scorecards, or leadership reporting.

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Build a learning progression showing prerequisite-to-mastery steps for a target skill or understanding. Use when sequencing content, designing diagnostics, or mapping prerequisite gaps.

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Classical orbital mechanics from Kepler to Hohmann. Covers the six orbital elements, Kepler's three laws, vis-viva, orbit types (circular, elliptical, parabolic, hyperbolic), transfer orbits, gravity assists, the two-body problem, and practical methods for computing ephemerides. Use when reasoning about planet motion, spacecraft trajectories, comet orbits, exoplanet transits, or binary star dynamics.

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Invariant set attracting nearby trajectories

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Deconstructs customer cohort behavior with retention curves, LTV progression, and vintage-over-vintage comparison analysis. Use when analyzing cohort data, assessing customer quality, or modeling lifetime value trajectories.

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Run and analyze molecular dynamics simulations with OpenMM and MDAnalysis. Set up protein and protein-ligand systems with PDBFixer, choose force fields and water models (AMBER14, CHARMM36m, ff19SB, GAFF2, TIP3P), solvate and add ions, run energy minimization, NVT/NPT equilibration and production MD on GPU, then analyze trajectories for RMSD, RMSF, radius of gyration, hydrogen bonds, native contacts, PCA and free energy surfaces. Use this skill for protein stability under mutation, ligand binding-mode and residence-time questions, conformational sampling, membrane proteins, and disordered ensembles. Also trigger on OpenMM, MDAnalysis, mdtraj, Simulation.step, LangevinMiddleIntegrator, PDBFixer, DCD or XTC trajectory, RMSD analysis, or production MD.

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Audit a student information system for dropout risk prediction. Analyzes attendance patterns, grade trajectories, behavioral indicators, early warning model accuracy, intervention tracking, equity in risk scoring, and socioeconomic factor integration..

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Analyzes time-series and longitudinal RNA-seq for differential expression and trajectory structure. Covers DESeq2 LRT with reduced models, time as factor vs continuous vs natural splines, maSigPro (Nueda 2014 for RNA-seq), ImpulseDE2 with explicit impulse-model failure modes, DREAM for repeated measures via linear mixed models, pseudoreplication avoidance, conditional vs marginal modeling, and trajectory clustering with DPGP, Mfuzz (with Schwämmle 2010 fuzzifier estimation), and splines+k-means. Use when modeling time-course or longitudinal expression, choosing factor vs spline, handling repeated measures from the same subject, avoiding pseudoreplication, clustering temporal trajectories, or selecting between dedicated time-course tools and pairwise+LRT.

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Analyze task trajectories to propose reusable SKILL.md candidates from successful patterns

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Expert-level orbital mechanics covering Keplerian orbits, orbital maneuvers, Hohmann transfers, rendezvous, interplanetary trajectories, and perturbations.

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Use when diagnosing agent benchmarks or runtime failures from trajectories instead of aggregate pass rates. Builds shared action-observation decision landscapes, identifies productive cores and trap regions, profiles Access/Trap/Repair behavior, and designs conservative trap-triggered recovery experiments.

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Analyzes time-series and longitudinal RNA-seq for differential expression and trajectory structure. Covers DESeq2 LRT with reduced models, time as factor vs continuous vs natural splines, maSigPro (Nueda 2014 for RNA-seq), ImpulseDE2 with explicit impulse-model failure modes, DREAM for repeated measures via linear mixed models, pseudoreplication avoidance, conditional vs marginal modeling, and trajectory clustering with DPGP, Mfuzz (with Schwämmle 2010 fuzzifier estimation), and splines+k-means. Use when modeling time-course or longitudinal expression, choosing factor vs spline, handling repeated measures from the same subject, avoiding pseudoreplication, clustering temporal trajectories, or selecting between dedicated time-course tools and pairwise+LRT.

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Analyzes time-series and longitudinal RNA-seq for differential expression and trajectory structure. Covers DESeq2 LRT with reduced models, time as factor vs continuous vs natural splines, maSigPro (Nueda 2014 for RNA-seq), ImpulseDE2 with explicit impulse-model failure modes, DREAM for repeated measures via linear mixed models, pseudoreplication avoidance, conditional vs marginal modeling, and trajectory clustering with DPGP, Mfuzz (with Schwämmle 2010 fuzzifier estimation), and splines+k-means. Use when modeling time-course or longitudinal expression, choosing factor vs spline, handling repeated measures from the same subject, avoiding pseudoreplication, clustering temporal trajectories, or selecting between dedicated time-course tools and pairwise+LRT.

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Design RL simulation sandboxes, trajectory datasets, QLoRA/LoRA adaptation plans, eval harnesses, and rollback unhooks for agents learning specific coding behaviors beyond the base model. Use when building or reviewing tool-use training loops, offline trajectories, reward functions, self-improvement harnesses, or Port Daddy agent behavior curricula. NOT for generic ML tutorials, full model training operations, or production deployment without eval gates and safety unhooks.

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