---
title: "Agent Plugins Directory, page 80 of 81"
description: "| Name | Description | Repo |"
canonical: https://agentpluginsdirectory.com/directory/page/80
last-updated: 2026-10-03
---

# Agent Plugins Directory, page 80 of 81

| Name | Description | Repo |
| --- | --- | --- |
| [stem-cell-biologist](https://agentpluginsdirectory.com/plugins/stem-cell-biologist) | Human pluripotent stem cell maintenance, ISSCR-aligned QC (pluripotency markers, tri-lineage differentiation, genomic drift, mycoplasma), and directed differentiation troubleshooting. | K-Dense-AI/scientific-agents |
| [stratigrapher](https://agentpluginsdirectory.com/plugins/stratigrapher) | Reasons from the material-strata-versus-conceptual-time distinction, accommodation-and-supply systems tracts, and confidence-tiered correlation through ICS/NACS codes, sequence surfaces (SB, MFS, TS), wireline and seismic well ties, and biostratigraphic FAD/LAD plus U-Pb and chemostratigraphic tie points, while treating diachronous facies contacts, seismic tuning artifacts, and reworked or caved fossils as first-class failure modes. | K-Dense-AI/scientific-agents |
| [string-theorist](https://agentpluginsdirectory.com/plugins/string-theorist) | Reasons from worldsheet CFT anomaly cancellation, moduli stabilization, and effective-field-theory consistency through KKLT/LVS flux compactifications, KLT/CHY/BCFW amplitude methods, and AdS/CFT large-N holography while treating tadpole mismatches, runaway moduli, unstabilized de Sitter uplifts, and conjecture-as-theorem swampland overclaims as first-class failure modes. | K-Dense-AI/scientific-agents |
| [structural-biologist](https://agentpluginsdirectory.com/plugins/structural-biologist) | Reasons from the phase problem, CTF, and gold-standard FSC; refines with CCP4/PHENIX/RELION/cryoSPARC; validates with MolProbity and OneDep while treating preferred orientation, twinning, and radiation damage as first-class failure modes. | K-Dense-AI/scientific-agents |
| [structural-engineer](https://agentpluginsdirectory.com/plugins/structural-engineer) | Reasons from equilibrium, load-path continuity, ductility, and code-mandated safety formats through ASCE 7 load combinations, ETABS/SAP2000 models with independent hand checks, AISC 360/341 and ACI 318 capacity design, and ASCE 41 evaluation, while treating soft-story drift, brittle connection and anchor breakout failures, neglected serviceability, and progressive collapse as first-class failure modes. | K-Dense-AI/scientific-agents |
| [structural-geologist](https://agentpluginsdirectory.com/plugins/structural-geologist) | Reasons from stress, strain, kinematics, and Mohr-Coulomb failure through stereonet fault-slip analysis, area-balanced cross sections in Move, quartz/calcite paleopiezometry on EBSD-indexed CPO, and geodetic-plus-trench slip-rate estimates while treating heterogeneous-fault paleostress inversion, map-pattern vergence errors, seismic processing artifacts as false faults, and outcrop-face shear-sense bias as first-class failure modes. | K-Dense-AI/scientific-agents |
| [superconductivity-scientist](https://agentpluginsdirectory.com/plugins/superconductivity-scientist) | Reasons from BCS/Eliashberg/GL order parameters, pairing symmetry, and vortex physics; validates Tc with Meissner/χ/C triads, phase-sensitive Josephson tests, ARPES/STM gaps, and EPW; uses SuperCon/3DSC and IEC 61788 Ic standards while treating filamentary transitions, pseudogap misreads, DAC flux trapping, and HTS quench detection gaps as first-class failure modes. | K-Dense-AI/scientific-agents |
| [supramolecular-chemist](https://agentpluginsdirectory.com/plugins/supramolecular-chemist) | Reasons from noncovalent binding free energies (ΔG = ΔH − TΔS), host-guest complementarity, and cooperative assembly through ITC, NMR titration with global fitting (Bindfit, SupraFit), Job's method, and SCXRD while treating wrong-stoichiometry K fits, kinetic traps mistaken for thermodynamic products, ITC dilution-dominated heats, and crystal packing assumed to dominate solution as first-class failure modes. | K-Dense-AI/scientific-agents |
| [surface-chemist](https://agentpluginsdirectory.com/plugins/surface-chemist) | Reasons from interfacial thermodynamics, Langmuir/BET/D-R adsorption, and Young, Dupré wetting through XPS (ISO 15472/18118, AdC vacuum-level alignment, SESSA), contact-angle SFE (OWRK/vOCG, ASTM D7490), QCM-D viscoelastic modeling, ToF-SIMS, SAMs, and ISO 20579 handling while treating adventitious carbon, charging, siloxane contamination, Cassie, Wenzel states, and tip convolution as first-class failure modes. | K-Dense-AI/scientific-agents |
| [surface-engineering-specialist](https://agentpluginsdirectory.com/plugins/surface-engineering-specialist) | Reasons from tribological system design, Archard wear, Stribeck regimes, and Pourbaix/galvanic coupling; selects PVD/CVD/PEO/conversion stacks with HiPIMS etch and interlayers; validates with ISO 20502 scratch, ASTM G99/G133, G119 tribocorrosion, and ISO 14577 nanoindentation while treating delamination stress, arc macroparticles, pinhole galvanics, and cross-cut misuse on hard films as first-class failure modes. | K-Dense-AI/scientific-agents |
| [surface-physicist](https://agentpluginsdirectory.com/plugins/surface-physicist) | Reasons from surface thermodynamics, adsorption coverage, work function, and probe escape depth through XPS/ARPES, LEED I(V) and CTR analysis, STM/AFM, TPD with Redhead analysis, and DFT slabs while treating adventitious-carbon contamination, differential charging, electron-beam and tip-induced damage, and UHV-to-operando extrapolation as first-class failure modes. | K-Dense-AI/scientific-agents |
| [surgeon-scientist](https://agentpluginsdirectory.com/plugins/surgeon-scientist) | Reasons from anatomy, pathophysiology, and the IDEAL stage of surgical innovation through IDE/IND pathways, NSQIP/STS registry risk-adjustment, CUSUM learning-curve analysis, and ischemia-timed biobank SOPs while treating unrisk-adjusted case series, indication-confounded surgeon-preference comparisons, cold-ischemia biomarker artifact, and conflated learning-curve and surgeon-volume effects as first-class failure modes. | K-Dense-AI/scientific-agents |
| [surveyor-geomatics-engineer](https://agentpluginsdirectory.com/plugins/surveyor-geomatics-engineer) | Reasons from datum/epoch/geoid and NSRS 2022 migration, CSF grid, ground, Baarda/3D least squares, NGS 92/ALTA RPP/ASPRS RMSE and IHO S-44 TPU; treats prism constants, BIM Helmert, and mixed CRS as first-class failure modes. | K-Dense-AI/scientific-agents |
| [sustainability-scientist](https://agentpluginsdirectory.com/plugins/sustainability-scientist) | Reasons from measurable capitals, planetary-boundary safe operating space, and absolute-versus-intensity impact through GHG Protocol Scope 1/2/3 accounting, GRI/ISSB/ESRS disclosure, MCI circularity and MFA, and IPCC SSP/IEA/NGFS scenario analysis, while treating greenwashing offsets without additionality or permanence, Scope 3 EEIO collapse, masked SDG trade-offs and leakage, and intensity gains hiding absolute growth as first-class failure modes. | K-Dense-AI/scientific-agents |
| [synthetic-biologist](https://agentpluginsdirectory.com/plugins/synthetic-biologist) | Reasons from biological parts, the DBTL cycle, chassis context, and the central dogma as a wiring diagram through Golden Gate and Gibson assembly, SBOL/SBML encoding, RPU/MEFL-calibrated characterization, and FBA models while treating metabolic burden, genetic instability, plasmid loss, and resource competition as first-class failure modes. | K-Dense-AI/scientific-agents |
| [systems-biologist](https://agentpluginsdirectory.com/plugins/systems-biologist) | Reasons from network motifs, separation of structure from dynamics, mass-balance constraints, and multi-layer measurement coupling through COBRApy/FBA-pFBA-FVA, ODE/Boolean simulation (COPASI, BoolNet, CellNOpt), and MOFA+/mixOmics integration while treating batch artifacts, gap-filled reactions, parameter non-identifiability, and transcript-flux conflation as first-class failure modes. | K-Dense-AI/scientific-agents |
| [systems-engineer](https://agentpluginsdirectory.com/plugins/systems-engineer) | Reasons from stakeholder needs, ISO/IEC/IEEE 15288 life-cycle processes, and V-model verification/validation through bidirectional requirements trace (DOORS/Polarion), MBSE SysML digital threads, ICD interface control, DSM integration sequencing, MCDA trade studies, and FMEA/FTA/STPA risk, while treating scope creep, gold plating, traceability gaps, and ICD mismatches as first-class failure modes. | K-Dense-AI/scientific-agents |
| [systems-neuroscientist](https://agentpluginsdirectory.com/plugins/systems-neuroscientist) | Reasons across circuits, Neuropixels/calcium imaging, behavior, optogenetics/chemogenetics, connectomics, and multi-timescale animal models, with rigor on sync, controls, and causal claims. | K-Dense-AI/scientific-agents |
| [taxonomist-systematist](https://agentpluginsdirectory.com/plugins/taxonomist-systematist) | Reasons from nomenclature: taxonomy separation and ICZN/Madrid Code typification (holotype/lectotype/neotype) through integrative delimitation (morphology, bPP/ASAP/BOLD), monograph and checklist workflows; uses ZooBank/IPNI/MycoBank, Darwin Core/GBIF IPT/COL, TaxonWorks/Specify, and BHL protologues while treating barcode-only species, syntype heterogeneity, inapplicable-state coding errors, and eDNA-only names as first-class failure modes. | K-Dense-AI/scientific-agents |
| [telecommunications-engineer](https://agentpluginsdirectory.com/plugins/telecommunications-engineer) | Reasons from Shannon: Hartley capacity (C = B log2(1+S/N)), dB link-budget accounting, and FSPL-plus-ITU-R propagation physics through 3GPP Rel-15/16/17 NR PHY and 5G numerology (15/30/60/120 kHz SCS), IEEE 802.11ax/6E air interfaces, ITU-R P.1546/P.1812/P.452 planning, TS 38.141 test models with VSA EVM/ACLR, IEC 62037 two-tone PIM, and Y.1731/Y.1564 Ethernet OAM while treating passive intermodulation, co-channel/adjacent-channel interference, backhaul GTP bottlenecks masquerading as air-interface failure, and IEEE 1588v2/GPS sync loss as first-class failure modes. | K-Dense-AI/scientific-agents |
| [theoretical-chemist](https://agentpluginsdirectory.com/plugins/theoretical-chemist) | Reasons from Hamiltonians, partition functions, and flux through dividing surfaces using validated potential energy surfaces, variational transition state theory with Eckart and small-curvature tunneling (Polyrate), and master-equation falloff solvers (MESMER, MultiWell), while treating spurious saddle imaginary modes, spin contamination, recrossing, and silent single-surface MD across conical intersections as first-class failure modes. | K-Dense-AI/scientific-agents |
| [theoretical-computer-scientist](https://agentpluginsdirectory.com/plugins/theoretical-computer-scientist) | Reasons from explicit models (TM, circuit, communication, query) and resource measures; audits Karp/parsimonious/gap/fine-grained reductions against ETH/SETH/#ETH and PCP/UGC/APX barriers; uses Complexity Zoo, ECCC/arXiv cs.CC, Coq/Lean/DRAT, Williams algorithms-for-lower-bounds, and Yao/IC lower bounds while treating wrong reduction direction, non-parsimony, APSP, 3SUM conflation, oracle overclaim, and natural-proofs misuse as first-class failure modes. | K-Dense-AI/scientific-agents |
| [theoretical-physicist](https://agentpluginsdirectory.com/plugins/theoretical-physicist) | Reasons from symmetries, conservation laws, effective field theory, and limiting cases through Feynman-diagram and on-shell amplitude tools, renormalization-group flow, lattice and tensor-network numerics, and the conformal bootstrap, while treating gauge-dependent artifacts, unitarity and Ward-identity violations, scheme dependence, and lattice discretization errors as first-class failure modes. | K-Dense-AI/scientific-agents |
| [thermodynamics-engineer](https://agentpluginsdirectory.com/plugins/thermodynamics-engineer) | Reasons from energy conservation, entropy generation, state properties, and exergy quality through cycle modeling on T-s/h-s diagrams, IAPWS-IF97/REFPROP/CoolProp property models, LMTD/ε-NTU and pinch analysis, and ASME PTC/AHRI acceptance protocols, while treating efficiency-above-Carnot claims, pinch violations, compressor surge, and inconsistent HHV/LHV bases as first-class failure modes. | K-Dense-AI/scientific-agents |
| [thin-film-scientist](https://agentpluginsdirectory.com/plugins/thin-film-scientist) | Reasons from nucleation and growth modes, film stress, interfacial adhesion, and conformality across topography through spectroscopic ellipsometry, XRR, Stoney wafer-curvature, XPS/RBS, and standards like ASTM E2244 and ISO 9211 while treating columnar porosity, barrier pinholes, reactive-sputter hysteresis drift, and uncalibrated-QCM thickness error as first-class failure modes. | K-Dense-AI/scientific-agents |
| [tissue-engineer](https://agentpluginsdirectory.com/plugins/tissue-engineer) | Reasons from the TE triad, Krogh transport limits, and Engler mechanobiology through perfusion bioreactors, dECM constructive remodeling, ASTM F2150/F1635 characterization, and ARRIVE/ISO 10993/21560 translation, treating hypoxic cores, acellular controls, and biological-vs-technical replicate inflation as first-class failure modes. | K-Dense-AI/scientific-agents |
| [topologist](https://agentpluginsdirectory.com/plugins/topologist) | Reasons from continuity, compactness, connectedness, homotopy, and manifold structure through invariants and tools like π₁ via Seifert-van Kampen, cellular/simplicial homology with ∂²=0 and Smith-normal-form torsion, Mayer-Vietoris, and SnapPy/GUDHI computation, while treating lost-Hausdorffness in quotients, torsion missed by rational coefficients, visual deformation without a homotopy or Reidemeister proof, and barcodes interpreted without filtration stability as first-class failure modes. | K-Dense-AI/scientific-agents |
| [toxicologist](https://agentpluginsdirectory.com/plugins/toxicologist) | Reasons from dose: response, ADME/TK, MOA/AOP, and exposure context; separates hazard from risk; derives BMDL/DNEL/RfD PODs and interprets OECD/ICH batteries with vehicle, strain, S9, and histopath artifacts as first-class failure modes. | K-Dense-AI/scientific-agents |
| [traffic-engineer](https://agentpluginsdirectory.com/plugins/traffic-engineer) | Reasons from LWR/CTM flow, HCM delay/v/c/LOS, K-D-PHF-DDHV volumes, ITE TGM 12th/MTIASD TIAs, Synchro/HCS/SIDRA/VISSIM workflows, and HSM SPF+CMF+EB safety, treating unc calibrated models, naive before, after crashes, and LOS-without-v/c as first-class failure modes. | K-Dense-AI/scientific-agents |
| [translational-researcher](https://agentpluginsdirectory.com/plugins/translational-researcher) | Reasons from T0: T4 stage gates, murine vs NHP translatability, PK/PD allometric bridging, MRSD/MABEL FIH dose selection, BEST/CLIA/CAP biomarker tiers, and CONSORT/SPIRIT/STROBE reporting while treating target-wrong, model-wrong, non-predictive biomarker, and preclinical irreproducibility as first-class failure modes. | K-Dense-AI/scientific-agents |
| [transportation-engineer](https://agentpluginsdirectory.com/plugins/transportation-engineer) | Reasons from four-step and activity-based travel demand (CUBE/Visum/EMME), AASHTO Green Book geometry, HCM capacity, MPO LRTP and NEPA 23 CFR 771 project development, and multimodal corridor MOEs while treating unvalidated TDM forecasts and capacity-without-demand balance as first-class failure modes. | K-Dense-AI/scientific-agents |
| [tribologist](https://agentpluginsdirectory.com/plugins/tribologist) | Reasons from contact mechanics, lubricant rheology, Stribeck-regime and λ ratio through Hamrock-Dowson EHL film estimates, pin-on-disk/four-ball/SRV/FZG bench tests and SEM-EDS/ferrography scar analysis while treating scuffing, rolling-contact pitting, three-body abrasion and DLC adhesive transfer as first-class failure modes. | K-Dense-AI/scientific-agents |
| [tunnel-underground-engineer](https://agentpluginsdirectory.com/plugins/tunnel-underground-engineer) | Reasons from ground-structure-water-air interaction, convergence-support interaction, and face-stability limit states through Q/RMR/GSI classification, Hoek-Brown numerical models (PLAXIS, FLAC), Peck settlement troughs, and DAUB-ITA/NFPA 502 standards while treating face blowout, squeezing, invert heave, and TBM jam in mixed face as first-class failure modes. | K-Dense-AI/scientific-agents |
| [turbomachinery-engineer](https://agentpluginsdirectory.com/plugins/turbomachinery-engineer) | Reasons from Euler work transfer, velocity triangles, decomposed loss correlations, and map-based off-design margin through meanline-to-throughflow-to-RANS/URANS analysis, Campbell and unbalance rotordynamics, and ASME PTC 10/PTC 6 and API 617 testing while treating surge and rotating stall, clearance rubs and tip leakage, pump cavitation below NPSHr, and seal cross-coupling instability as first-class failure modes. | K-Dense-AI/scientific-agents |
| [urban-infrastructure-planner](https://agentpluginsdirectory.com/plugins/urban-infrastructure-planner) | Reasons from comp plan: FLUM: zoning consistency through Euclidean/form-based overlays, CIP/TIP, STIP and ISO 55001/IIMM asset portfolios, ArcGIS Urban parcel workflows, and CEJST/AFFH/Justice40 equity screening while treating FLUM, zoning mismatch, CRS topology errors, and unfunded backlog as first-class failure modes. | K-Dense-AI/scientific-agents |
| [vaccinologist](https://agentpluginsdirectory.com/plugins/vaccinologist) | Vaccine development expert for platform and adjuvant selection, validated immunogenicity (HAI/PRNT/OPA), CoP and immunobridging, VE/effectiveness trial design, CBER lot release, and Brighton AEFI reporting. | K-Dense-AI/scientific-agents |
| [vacuum-science-technology-engineer](https://agentpluginsdirectory.com/plugins/vacuum-science-technology-engineer) | Reasons from molecular flux (P = n k_B T), conductance-limited effective pumping speed, and surface outgassing through He mass-spectrometer leak detection, rate-of-rise tests, RGA fingerprinting, and Molflow+ conductance modeling while treating virtual leaks, H₂ permeation, ion-gauge contamination, and hydrocarbon backstreaming as first-class failure modes. | K-Dense-AI/scientific-agents |
| [veterinarian](https://agentpluginsdirectory.com/plugins/veterinarian) | Reasons from species-specific physiology and pharmacology (Plumb's, AMDUCA, MDR1/PRiME), WSAVA 2024/AAHA/ISCAID 2025/CAPC guidelines, IDEXX/Cornell/eClinpath diagnostics, CMPS-SF/FGS pain and RECOVER 2024 CPR, and One Health zoonosis reporting while treating cat NSAID/acetaminophen toxicity, subclinical bacteriuria, greyhound lab artifacts, and human-dose extrapolation as first-class failure modes. | K-Dense-AI/scientific-agents |
| [veterinary-epidemiologist](https://agentpluginsdirectory.com/plugins/veterinary-epidemiologist) | Reasons from herd-level units, Rogan: Gladen prevalence, R₀/SIR transmission models, and WOAH freedom-from-disease surveillance through outbreak line lists, SaTScan clusters, cluster field trials (REFLECT), and STROBE-Vet/AHSURED reporting while treating pseudo-replication, test-biased apparent prevalence, reporting-intensity clusters, and mis-specified generation intervals as first-class failure modes. | K-Dense-AI/scientific-agents |
| [veterinary-microbiologist](https://agentpluginsdirectory.com/plugins/veterinary-microbiologist) | Reasons from pre-analytic specimen quality, CLSI VET01 AST, MALDI-TOF/PCR/WGS, and ISCAID significance thresholds through ACVM/AAVLD/WOAH workflows, NARMS/Vet-LIRN AMR surveillance, and One Health zoonoses while treating wound-swab contaminants, PCR-without-viability, human breakpoints on veterinary isolates, DTM false positives, MRSP biofilm, and Brucella BSL-3 exposure as first-class failure modes. | K-Dense-AI/scientific-agents |
| [vibration-dynamics-engineer](https://agentpluginsdirectory.com/plugins/vibration-dynamics-engineer) | Reasons from FRF/coherence, MAC, and damping identification through impact/shaker EMA, spectral ODS, FFT windowing, Campbell/critical-speed rotordynamics, and ISO 20816/API 610 diagnostics while treating double-hit, mass-loading, ODS, mode conflation, oil whirl/whip, and leakage as first-class failure modes. | K-Dense-AI/scientific-agents |
| [virologist](https://agentpluginsdirectory.com/plugins/virologist) | Reasons from Baltimore groups, replication-cycle kinetics, and ICTV/MSL41 taxonomy; runs plaque/TCID50/PRNT, MIQE qPCR, ARTIC Illumina/Nanopore surveillance, antiviral TOA, VLP platforms, and BEI Resources while treating DI particles, subgenomic RNA, pseudovirus cytotoxicity, and IFN/MHC evasion as first-class failure modes. | K-Dense-AI/scientific-agents |
| [viticulturist-enologist](https://agentpluginsdirectory.com/plugins/viticulturist-enologist) | Reasons from source: sink canopy balance, terroir as water/nitrogen-mediated ripening, sugar, acid, phenolic trajectories, glucophilic AF and Oenococcus MLF, molecular SO₂ at pH, FOSS/OIV analytics, ISO 4120/QDA sensory, and NDVI selective harvest while treating stuck ferment (YAN/fructose), smoke glycoside release, pH-blind SO₂, and mineral-terroir folklore as first-class failure modes. | K-Dense-AI/scientific-agents |
| [vlsi-chip-design-engineer](https://agentpluginsdirectory.com/plugins/vlsi-chip-design-engineer) | Reasons from PPA tradeoffs, timing slack, on-chip variation, and foundry rule decks through MCMM STA with OCV/POCV in PrimeTime/Tempus, UPF power intent, SpyGlass/JasperGold CDC, and Calibre DRC/LVS while treating clock-domain crossings, post-CTS hold violations, IR drop, and TT-only signoff as first-class failure modes. | K-Dense-AI/scientific-agents |
| [volcanologist](https://agentpluginsdirectory.com/plugins/volcanologist) | Reasons from mush reservoirs, volatile exsolution, and conduit fragmentation through WOVOdat/GVP unrest synthesis, MultiGAS, DOAS CO₂/SO₂, melt-inclusion thermobarometry, GACOS/ERA5 InSAR, LP/VLP/VOISS-Net seismology, BET_EF probabilistic forecasting, and LaMEVE recurrence while treating atmospheric InSAR artefacts, MI H₂O diffusion loss, open-vent gas misread, and deterministic eruption countdowns as first-class failure modes. | K-Dense-AI/scientific-agents |
| [water-resources-engineer](https://agentpluginsdirectory.com/plugins/water-resources-engineer) | Watershed hydrology through HEC-HMS into HEC-RAS floodplain and stormwater BMP design, catchment balance, DSS coupling, FEMA products, and quantity/quality detention with defensible calibration. | K-Dense-AI/scientific-agents |
| [water-resources-scientist](https://agentpluginsdirectory.com/plugins/water-resources-scientist) | Reasons from hydrologic-cycle mass and energy balance, green-versus-blue water, and nonstationarity through MODFLOW, SWAT/HEC-RAS, WEAP, Budyko closure, and multi-objective KGE/NSE calibration while treating equifinality, unaccounted return flows and stream depletion, single-drought-year safe yield, and efficiency-rebound effects as first-class failure modes. | K-Dense-AI/scientific-agents |
| [weed-scientist](https://agentpluginsdirectory.com/plugins/weed-scientist) | Reasons from the weed seed bank, population dynamics, and herbicide mode-of-action biology through log-logistic dose-response (GR50/GR90 in R drc), replicated RCB field trials with susceptible checks, molecular resistance assays (ALS sequencing, EPSPS copy number), and HRAC/WSSA-based MOA rotation while treating drift and carryover, tank-mix antagonism and water-quality failures, and late-escape seed rain as first-class failure modes. | K-Dense-AI/scientific-agents |