---
title: "Agent Plugins Directory, page 79 of 81"
description: "| Name | Description | Repo |"
canonical: https://agentpluginsdirectory.com/directory/page/79
last-updated: 2026-10-03
---

# Agent Plugins Directory, page 79 of 81

| Name | Description | Repo |
| --- | --- | --- |
| [quantitative-biologist](https://agentpluginsdirectory.com/plugins/quantitative-biologist) | Reasons from SBML/PEtab ODE models, structural and profile-likelihood identifiability, Bayesian inference (Stan/PyMC/AMICI), and live-cell pipelines (Cellpose/TrackMate/PhotoFiTT, REMBI); treats sloppiness, phototoxicity, and segmentation-tracking artifacts as first-class failure modes. | K-Dense-AI/scientific-agents |
| [quantum-chemist](https://agentpluginsdirectory.com/plugins/quantum-chemist) | Reasons from the Schrödinger equation through HF, MP2/CCSD(T)/CBS, and multireference (CASSCF/CASPT2); uses ORCA/Psi4/Gaussian with GMTKN55/WTMAD-4 validation, T1/D1 diagnostics, Helgaker CBS extrapolation, and BSSE/spin-contamination checks while treating SCF near-degeneracy, intruder states, and global-vs-local multireference masking as first-class failure modes. | K-Dense-AI/scientific-agents |
| [quantum-computing-scientist](https://agentpluginsdirectory.com/plugins/quantum-computing-scientist) | Reasons from qubits as noisy open systems through T1/T2, gate fidelity, RB/GST/XEB, and quantum volume to surface-code QEC; compiles with Qiskit/Cirq, applies ZNE/PEC/readout mitigation, and treats crosstalk, transpilation depth, and calibration drift as first-class failure modes. | K-Dense-AI/scientific-agents |
| [quantum-information-scientist](https://agentpluginsdirectory.com/plugins/quantum-information-scientist) | Reasons from operational entropies (smooth min-entropy, coherent information), Stinespring/Choi channel pictures, and the achievability-versus-converse split through dual-certified SDP/NPA bounds, EAT/GEAT finite-key security proofs, Stim/PyMatching/BP+OSD circuit-level QEC simulation, classical shadows and GST, and ETSI/ISO QKD evaluation standards while treating asymptotic key rates passed off as performance, uncertified superadditivity numerics, code distance mistaken for circuit distance, MLE-tomography bias, and CHSH violations mistaken for security proofs as first-class failure modes. | K-Dense-AI/scientific-agents |
| [quantum-optics-scientist](https://agentpluginsdirectory.com/plugins/quantum-optics-scientist) | Reasons from field quadratures, atom-photon coupling (g, κ, γ), and heralding efficiency budgets through g⁽²⁾ Hanbury Brown-Twiss measurement, balanced homodyne tomography, HOM interference, and SNSPD/APD detector calibration while treating afterpulsing-faked antibunching, LO phase drift erasing squeezing, accidentals and dark counts, and unaddressed Bell-test loopholes as first-class failure modes. | K-Dense-AI/scientific-agents |
| [quantum-physicist](https://agentpluginsdirectory.com/plugins/quantum-physicist) | Reasons from Hilbert-space density operators, commutation relations, and Lindblad open-system dynamics through randomized benchmarking, gate-set and process tomography, Bell-CHSH tests, and Stim/PyMatching surface-code decoding while treating crosstalk, leakage, calibration drift, and measurement backaction as first-class failure modes. | K-Dense-AI/scientific-agents |
| [quaternary-scientist](https://agentpluginsdirectory.com/plugins/quaternary-scientist) | Reasons from dated landform-sediment-proxy associations, multi-method chronology, and ice-age cyclicity (MIS, orbital forcing) through radiocarbon/OSL/cosmogenic dating, Bayesian age models (OxCal, Bacon, IntCal20), tephrochronology, and GIA models while treating uncalibrated 14C years, incomplete OSL bleaching, cosmogenic inheritance, and no-analog pollen assemblages as first-class failure modes. | K-Dense-AI/scientific-agents |
| [radiation-oncology-physicist](https://agentpluginsdirectory.com/plugins/radiation-oncology-physicist) | Reasons from absorbed dose, fluence, beam geometry, and constraint-driven plan quality through TG-51/TRS-398 reference dosimetry, TPS engines (AAA, Acuros XB, Monte Carlo), DVH metrics, and gamma-based patient-specific QA while treating stale CT-to-density tables, couch-shift sign errors, MLC leaf-bank swaps, and small-field output mishandling as first-class failure modes. | K-Dense-AI/scientific-agents |
| [radio-astronomer](https://agentpluginsdirectory.com/plugins/radio-astronomer) | Reasons from the van Cittert, Zernike relation, the RIME measurement equation, the radiometer equation, and ν⁻²/λ² plasma propagation through CASA/WSClean/DDFacet imaging, Perley, Butler flux scaling, MT-MFS and w-stacking, RM synthesis, single-dish T_A*/T_mb calibration, and PRESTO/PSRCHIVE/PINT pulsar and FRB workflows while treating missing short spacings and negative bowls, self-calibration ghosts and flux suppression, RFI masquerading as dispersed bursts, polarization leakage and beam squint, and flaring flux calibrators as first-class failure modes. | K-Dense-AI/scientific-agents |
| [radiochemist](https://agentpluginsdirectory.com/plugins/radiochemist) | Reasons from radionuclide half-life, specific activity, radiochemical purity, and dosimetry through analytical/prep HPLC with radiodetector, iTLC, HPGe γ-spectroscopy, OLINDA/MIRD, and USP <823>/EANM release specs while treating defluorination, transchelation of ⁶⁸Ga/⁸⁹Zr, ⁹⁹ᵐTc colloid and ⁶⁸Ge breakthrough as first-class failure modes. | K-Dense-AI/scientific-agents |
| [radiologist](https://agentpluginsdirectory.com/plugins/radiologist) | Reasons from modality: question fit, contrast kinetics, and ACR Appropriateness Criteria (1 to 9); applies BI-RADS, LI-RADS, PI-RADS, and Lung-RADS with Fleischner/incidental-findings algorithms; integrates PACS/RIS/DICOM/IHE workflows, CTDIvol/DLP/DIR dose stewardship, and critical-results communication while treating perceptual misses, satisfaction of search, and CT/MRI/PET artifacts as first-class failure modes. | K-Dense-AI/scientific-agents |
| [reaction-engineering-specialist](https://agentpluginsdirectory.com/plugins/reaction-engineering-specialist) | Reasons from rate laws, stoichiometry, residence-time distributions, and coupled heat-rate balances through LHHW and Michaelis-Menten kinetics, Thiele/effectiveness and Weisz-Prater diffusion criteria, tracer RTD models, and RC1/ARC calorimetry while treating thermal runaway, hot spots, catalyst deactivation, channeling, and k_L-a mass-transfer masking as first-class failure modes. | K-Dense-AI/scientific-agents |
| [regenerative-medicine-scientist](https://agentpluginsdirectory.com/plugins/regenerative-medicine-scientist) | Reasons from potency assurance, 361 vs 351/ATMP pathways, USP <1043> ancillary tiers, G-Rex/closed CAR-T manufacture, MSC matrix potency, and ISO 10993/dECM scaffolds while treating comparability-without-bioassay and CFU-F-as-potency as first-class failure modes. | K-Dense-AI/scientific-agents |
| [regulatory-affairs-scientist](https://agentpluginsdirectory.com/plugins/regulatory-affairs-scientist) | Reasons from CTD/eCTD Modules 1 to 5 traceability, FDA Type B/EOP and EMA PRIME/scientific-advice strategy, ICH Q8, Q12 lifecycle CMC, and expedited pathways (BTD/RMAT/accelerated approval); treats RTF, clinical-hold CMC gaps, ignored meeting minutes, and eCTD validation failures as first-class failure modes. | K-Dense-AI/scientific-agents |
| [rehabilitation-scientist](https://agentpluginsdirectory.com/plugins/rehabilitation-scientist) | Reasons from ICF/disablement models, COSMIN MCID/MDC triangulation, TIDieR-Rehab/CONSORT 2025 trial design, gait lab and PROMIS outcomes, motor-learning mechanisms, and RE-AIM implementation science; treats natural recovery, therapist allegiance, and lab-vs-function confounds as first-class failure modes. | K-Dense-AI/scientific-agents |
| [reinforcement-learning-researcher](https://agentpluginsdirectory.com/plugins/reinforcement-learning-researcher) | Reasons from MDP/POMDP structure, Bellman contraction and the deadly triad, and policy-gradient variance through Gymnasium 1.x/MuJoCo v5/ALE v5 protocols, CleanRL/SB3/JAX (MJX, MuJoCo Playground) stacks, rliable IQM with stratified bootstrap CIs, Minari offline datasets, and GRPO/RLVR post-training while treating truncation-as-termination bootstrap bugs, seed and hyperparameter selection bias, reward hacking, and offline extrapolation error as first-class failure modes. | K-Dense-AI/scientific-agents |
| [reliability-engineer](https://agentpluginsdirectory.com/plugins/reliability-engineer) | Reasons from failure mechanisms, time-to-failure distributions, censored field data, and stress-strength interference through FMECA, physics-of-failure models (Coffin-Manson, Arrhenius, Peck), Weibull and Crow-AMSAA growth analysis, and demonstration tests while treating mixture populations, wrong acceleration models, common-cause failures, and lab-pass-equals-field-proof as first-class failure modes. | K-Dense-AI/scientific-agents |
| [remote-sensing-scientist](https://agentpluginsdirectory.com/plugins/remote-sensing-scientist) | Reasons from sensor physics, atmospheric state, surface BRDF, and sampling geometry through Sen2Cor/LaSRC/6S atmospheric correction, sub-pixel coregistration, SAR radiometric terrain correction, and Olofsson area-adjusted accuracy while treating misregistration, NDVI saturation, BRDF anisotropy, mixed pixels, and spatial label leakage as first-class failure modes. | K-Dense-AI/scientific-agents |
| [renewable-energy-scientist](https://agentpluginsdirectory.com/plugins/renewable-energy-scientist) | Reasons from resource-to-energy conversion, P50/P90 yield risk, LCOE/LCA boundaries, IEC monitoring, and grid constraints while troubleshooting PV soiling/PID/clipping, wind wakes/icing/yaw, hydro drought, geothermal scaling, and biomass feedstock variability. | K-Dense-AI/scientific-agents |
| [reproductive-biologist](https://agentpluginsdirectory.com/plugins/reproductive-biologist) | Reasons from the HPG axis, gametogenesis, embryo development, and endometrial receptivity through WHO 6th semen analysis, LC-MS/MS hormone assays, EmbryoScope morphokinetics, PGT-A, and ASRM/ESHRE guidelines while treating mis-timed cycle-day sampling, incubator CO2/pH drift, sperm DNA fragmentation, and embryo mosaicism as first-class failure modes. | K-Dense-AI/scientific-agents |
| [research-software-engineer](https://agentpluginsdirectory.com/plugins/research-software-engineer) | Reasons from Software Carpentry and FAIR4RS through SemVer releases, CITATION.cff/SPDX metadata, pytest/Hypothesis CI gates, Docker/Apptainer on Slurm, and maintainability discipline for citable, reproducible research code. | K-Dense-AI/scientific-agents |
| [restoration-ecologist](https://agentpluginsdirectory.com/plugins/restoration-ecologist) | Reasons from SER International Standards (reference models, six ecosystem attributes, five-star recovery, restorative continuum), BACI/BARI monitoring, INSR seed provenance and provisional seed zones, FQA/cover-weighted metrics, and passive, active, assisted recovery while treating revegetation-as-restoration, chronosequence pseudoreplication, and year-3 cover photos as first-class failure modes. | K-Dense-AI/scientific-agents |
| [rf-microwave-engineer](https://agentpluginsdirectory.com/plugins/rf-microwave-engineer) | Reasons from power-wave S-parameters, Friis noise-figure cascades, and Rollett/mu stability through ADS/AWR harmonic balance, HFSS/Sonnet EM, Smith-chart matching, and TRL/SOLT-calibrated VNA/spectrum bench work while treating reference-plane errors, LO leakage and IF feedthrough, conditional instability, and uncorrelated sim-versus-measured gain as first-class failure modes. | K-Dense-AI/scientific-agents |
| [rna-biologist](https://agentpluginsdirectory.com/plugins/rna-biologist) | Reasons like a senior RNA biologist across transcription and nascent assays, splicing, m6A, CLIP/eCLIP, RNA-seq, ribosome profiling, and GENCODE/MANE annotation, with rigor, troubleshooting, and reporting norms. | K-Dense-AI/scientific-agents |
| [robotics-engineer](https://agentpluginsdirectory.com/plugins/robotics-engineer) | Reasons from DH/PoE kinematics, Jacobian singularities, and computed-torque/impedance control through ROS 2, MoveIt/OMPL, Nav2/SLAM/AMCL, hand-eye AX=XB, Isaac/Gazebo sim-to-real, and ISO 10218/ISO TS 15066 safety while treating tf/frame errors, encoder drift, backlash, and reality-gap overclaim as first-class failure modes. | K-Dense-AI/scientific-agents |
| [robotics-scientist](https://agentpluginsdirectory.com/plugins/robotics-scientist) | Reasons from the closed sensor-to-actuator loop, kinematic reachability, and dynamic feasibility (friction cones, actuator saturation) through DH/PoE kinematics, RRT*/CHOMP planning, MPC and whole-body control, SLAM, and ROS 2 rosbag logging while treating TF-frame and timestamp mismatches, the sim-to-real gap, grasp slip, and ISO 10218/15066 force-limit violations as first-class failure modes. | K-Dense-AI/scientific-agents |
| [sedimentologist](https://agentpluginsdirectory.com/plugins/sedimentologist) | Reasons from grain-scale hydraulics, facies associations, and base-level accommodation through measured sections, Folk & Ward granulometry, Bouma divisions, ichnofacies, and core-log-seismic ties while treating diagenetic overprint, bioturbation-destroyed laminae, fining-upward and shale-equals-deep-water defaults, and single-outcrop overextrapolation as first-class failure modes. | K-Dense-AI/scientific-agents |
| [seismologist](https://agentpluginsdirectory.com/plugins/seismologist) | Reasons from elastic wave theory and Earth models through detection, HypoDD/iLoc location, moment tensors, ambient-noise and receiver-function imaging, PSHA/OpenQuake hazard, and NEIC-style operational products (ShakeMap, PAGER, EEW) with explicit artifact and magnitude-type discipline. | K-Dense-AI/scientific-agents |
| [semiconductor-device-engineer](https://agentpluginsdirectory.com/plugins/semiconductor-device-engineer) | Reasons from electrostatics, capacitance-current MOSFET physics, interface-trap behavior, and self-heating through I-V/C-V extraction ladders, Sentaurus TCAD calibrated to silicon splits, BSIM-CMG compact modeling, and JEDEC reliability stress while treating uncalibrated TCAD, unstated constant-current Vt references, ignored BTI partial recovery, and self-heating-distorted DC Ron as first-class failure modes. | K-Dense-AI/scientific-agents |
| [semiconductor-materials-scientist](https://agentpluginsdirectory.com/plugins/semiconductor-materials-scientist) | Reasons from band structure, defect energetics, and process, structure, property links; grows and characterizes bulk and epitaxial semiconductors (Si, III, V, SiC, GaN, 2D) via MOCVD/MBE/HVPE, Hall/DLTS/XRD/RSM/ECCI/TEM/SIMS, and DFT defect levels while treating compensation, Fermi-level pinning, threading dislocations, and polytype mixing as first-class failure modes. | K-Dense-AI/scientific-agents |
| [semiconductor-physicist](https://agentpluginsdirectory.com/plugins/semiconductor-physicist) | Reasons from ε_n(k), effective-mass tensor, and 2D subband DOS through Hall/multiband fits, mobility scattering analysis, Lang DLTS (E_T, σ, N_T), and quantum-well intersubband spectroscopy while treating compensation, rate-window artifacts, and DFT gap error as first-class failure modes. | K-Dense-AI/scientific-agents |
| [sensor-engineer](https://agentpluginsdirectory.com/plugins/sensor-engineer) | Reasons from transduction physics and error budgets through MEMS IMU Allan variance (ARW, bias instability, rate random walk), six-position and temperature calibration, piezoresistive/capacitive pressure validation, and photodiode, TIA NEP/SNR while treating vibration rectification, mag distortion, and aliased decimation as first-class failure modes. | K-Dense-AI/scientific-agents |
| [separation-processes-engineer](https://agentpluginsdirectory.com/plugins/separation-processes-engineer) | Reasons from VLE/LLE thermodynamics, FUG shortcuts, and NRTL/PR property packages through Aspen RadFrac, CGCC/pinch integration, membrane Robeson bounds, chromatography van Deemter scale-up, and MSZW crystallization while treating wrong BIPs, jet flood/entrainment, concentration polarization, and lab-to-plant MSZW as first-class failure modes. | K-Dense-AI/scientific-agents |
| [signal-processing-engineer](https://agentpluginsdirectory.com/plugins/signal-processing-engineer) | Reasons from the sampling theorem, LTI system functions H(z), and sufficient statistics for detection through Parks-McClellan filter design, Welch and multitaper spectral estimation, matched filters and CFAR detection, and bit-true fixed-point verification while treating aliasing, leakage and scalloping, IIR limit cycles, and detector leakage as first-class failure modes. | K-Dense-AI/scientific-agents |
| [single-cell-biologist](https://agentpluginsdirectory.com/plugins/single-cell-biologist) | Reasons from assay chemistry, sample-level replication, cell-state manifolds, and metadata provenance; treats ambient RNA, doublets, dissociation stress, batch, and pseudoreplication as core failure modes. | K-Dense-AI/scientific-agents |
| [sleep-scientist](https://agentpluginsdirectory.com/plugins/sleep-scientist) | Reasons from Borbély Process S/C homeostatic: circadian integration, AASM v3 PSG scoring (1A/1B hypopnea rules), DLMO/forced desynchrony phase assays, MSLT/ICSD-3 hypersomnolence criteria, Cole-Kripke/Sadeh actigraphy, NSRR/SHHS cohorts, and CBT-I/CPAP trial design while treating first-night effect, actigraphy wake misclassification, 3% vs 4% AHI shifts, and consumer wearable stage overclaim as first-class failure modes. | K-Dense-AI/scientific-agents |
| [soft-matter-physicist](https://agentpluginsdirectory.com/plugins/soft-matter-physicist) | Reason from kT and mesoscale structure; couple rheology (TA Instruments, Anton Paar), scattering (SANS/SAXS/DLS/XPCS), and PIV to Flory-Huggins, de Gennes scaling, jamming, and active-matter hydrodynamics. | K-Dense-AI/scientific-agents |
| [soil-ecologist](https://agentpluginsdirectory.com/plugins/soil-ecologist) | Reasons from soil food webs (nematode EI/SI/CI), PLFA phenotypes, amoA/nirK/nifH qPCR, and 16S/ITS/metagenomics through gross 15N pool dilution and C/N priming while treating tillage, compaction, and fire recovery, compositional bias, and DNA-activity gaps as first-class failure modes. | K-Dense-AI/scientific-agents |
| [soil-fertility-scientist](https://agentpluginsdirectory.com/plugins/soil-fertility-scientist) | Reasons from plant-available nutrient supply, pH-governed availability, and CEC/base saturation through Mehlich-3 extraction, buffer-pH lime calculations with ECCE/ENM, 4R stewardship, and regional extension calibration while treating uncalibrated cross-extractant comparison, no-till stratification, environmental P/nitrate loss, and N mineralization-immobilization leakage as first-class failure modes. | K-Dense-AI/scientific-agents |
| [soil-scientist](https://agentpluginsdirectory.com/plugins/soil-scientist) | Reasons from CLORPT genetic horizonation, matric-potential water flow, and colloid exchange chemistry through Munsell pedon description, USDA Soil Taxonomy and WRB keys, buffer-pH lime calculation, and HYDRUS/RUSLE2/PHREEQC modeling while treating wrong-extractant nutrient values (Mehlich-3 vs Olsen), map-unit-as-pedon substitution, and PTF-output-as-measured-K_sat as first-class failure modes. | K-Dense-AI/scientific-agents |
| [solar-physicist](https://agentpluginsdirectory.com/plugins/solar-physicist) | Reasons from magnetic field topology, plasma beta, reconnection, and radiative transfer through DEM inversion, NLFFF/PFSS extrapolation, coronal seismology, and WSA-ENLIL forecasting while treating single-channel AIA temperature claims, HMI disambiguation ambiguity at the PIL, limb projection artifacts, and Parker-spiral connectivity uncertainty as first-class failure modes. | K-Dense-AI/scientific-agents |
| [space-weather-scientist](https://agentpluginsdirectory.com/plugins/space-weather-scientist) | Reasons from Dungey coupling and prolonged southward Bz through ICME vs. CIR/SIR drivers; uses OMNI/CDAWeb, L1 RTSW, WSA-Enlil, CCMC/CAMEL validation, SuperMAG SYM-H, GloTEC/IRI, and NOAA G/S/R scales while treating sheath-vs-cloud Bz, catalog false alarms, and Dst timing artifacts as first-class failure modes. | K-Dense-AI/scientific-agents |
| [spectroscopist](https://agentpluginsdirectory.com/plugins/spectroscopist) | Reasons from selection rules, line shapes as convolutions of intrinsic and instrument broadening, and Beer-Lambert linearity through UV-Vis, fluorescence, IR/Raman, NMR, CD, EPR, and XAS/XPS with calibration standards (polystyrene 1601 cm⁻¹, TMS/DSS, C 1s 284.8 eV) while treating inner filter effects, baseline artifacts inventing peaks, Fermi resonances, and X-ray beam damage as first-class failure modes. | K-Dense-AI/scientific-agents |
| [spintronics-physicist](https://agentpluginsdirectory.com/plugins/spintronics-physicist) | Reasons from spin-orbit coupling, spin diffusion length, exchange and DMI, and spin-dependent transport through MTJ/TMR characterization, ST-FMR and harmonic-Hall torque measurement, nonlocal spin valves, and Valet-Fert and MuMax3/OOMMF modeling, while treating barrier pinholes and shunt paths, ordinary-versus-anomalous Hall and ISHE confusion, Oersted-field and Joule-heating artifacts, and incomplete magnetization switching as first-class failure modes. | K-Dense-AI/scientific-agents |
| [sports-scientist](https://agentpluginsdirectory.com/plugins/sports-scientist) | Reasons from periodization and session-RPE/ACWR/GPS load monitoring, VALD force-plate readiness, SWC/TE decision bands, and IOC/STROBE-SIIS/CERT/CONSORT reporting while treating pseudoreplication, Hawthorne reactivity, and single-metric injury claims as first-class failure modes. | K-Dense-AI/scientific-agents |
| [statistical-physicist](https://agentpluginsdirectory.com/plugins/statistical-physicist) | Reasons from ensembles and partition functions through finite-size scaling (Binder cumulant, data collapse), Wolff/cluster MC, and RG/MCRG to Jarzynski, Crooks fluctuation theorems; uses ALPS, NetKet, WHAM, and ED/DMRG while treating critical slowing down, subleading FSS humps, and poor work-histogram overlap as first-class failure modes. | K-Dense-AI/scientific-agents |
| [statistician](https://agentpluginsdirectory.com/plugins/statistician) | Reasons from estimands, generative-model assumptions, and a budgeted Type I/II error tradeoff through analysis plans (SAP, ICH E9(R1) estimands), mixed models, multiple imputation under MCAR/MAR/MNAR, and Benjamini-Hochberg FDR while treating naive post-selection SEs, unadjusted multiplicity, ignored clustering in survey PSUs, and sequential peeking as first-class failure modes. | K-Dense-AI/scientific-agents |
| [stellar-astrophysicist](https://agentpluginsdirectory.com/plugins/stellar-astrophysicist) | Reasons from hydrostatic and thermal balance, convective-boundary physics (Schwarzschild/Ledoux, α_MLT, overshoot), nuclear bottlenecks, and p/g/mixed-mode seismology through MESA/GYRE resolution ladders, multi-grid isochrone fitting (MIST, PARSEC, BaSTI), corrected scaling relations, 1D-LTE-vs-3D-NLTE abundance analysis, and eclipsing-binary and Gaia benchmarks while treating unconverged default-control models, asteroseismic surface effects and scaling-relation biases, NLTE-biased spectroscopic log g, grid-to-grid age systematics, and unrecognized binary or merger products as first-class failure modes. | K-Dense-AI/scientific-agents |