---
title: "Agent Plugins Directory, page 74 of 81"
description: "| Name | Description | Repo |"
canonical: https://agentpluginsdirectory.com/directory/page/74
last-updated: 2026-10-03
---

# Agent Plugins Directory, page 74 of 81

| Name | Description | Repo |
| --- | --- | --- |
| [energy-storage-battery-scientist](https://agentpluginsdirectory.com/plugins/energy-storage-battery-scientist) | Reasons from interfacial thermodynamics, ion transport, SEI/CEI dynamics, and cell engineering constraints (N/P and E/S ratio, mass loading) through galvanostatic cycling, dQ/dV, GITT and EIS/DRT, operando XRD, and PyBaMM/Newman models, while treating Li plating, lithium-inventory loss, transition-metal crossover, and coin-cell artifacts as first-class failure modes. | K-Dense-AI/scientific-agents |
| [energy-systems-engineer](https://agentpluginsdirectory.com/plugins/energy-systems-engineer) | Reasons from exergy, load duration curves, capacity factor, and grid boundary constraints through pinch analysis, hourly dispatch models (PLEXOS, HOMER Pro, SAM, PVsyst), spark-spread CHP screening, and IPMVP M&V while treating nameplate-vs-utilization confusion, average-vs-marginal grid emissions, unrealistic arbitrage spreads, and demand-charge ratchet resets as first-class failure modes. | K-Dense-AI/scientific-agents |
| [entomologist](https://agentpluginsdirectory.com/plugins/entomologist) | Reasons from tagmata, Comstock-Needham venation, and tarsal formula through trap-guild sampling (Malaise, pitfall, pan, light), host, parasitoid ecology, ICZN vouchers and genitalia keys, BOLD/GBIF/COL/iNaturalist triage, IUCN invertebrate caveats, CITES/COSE permits, Taylor/GLMM on the correct EU, and EIL/ET with IRAC MoA rotation while treating teneral, dimorphic, and cryptic mis-IDs as first-class failure modes. | K-Dense-AI/scientific-agents |
| [environmental-chemist](https://agentpluginsdirectory.com/plugins/environmental-chemist) | Reasons from thermodynamic partitioning (K_oc/K_ow, Henry's law), pathway-specific half-lives, and mass balance through GC-MS/LC-MS/MS/ICP-MS analysis, EPI Suite and fugacity fate models, and EPA SW-846 QA/QC while treating blank contamination, matrix suppression, censored sub-LOD data, and unscoped transformation products as first-class failure modes. | K-Dense-AI/scientific-agents |
| [environmental-engineer](https://agentpluginsdirectory.com/plugins/environmental-engineer) | Reasons from mass balances, reaction kinetics, source-pathway-receptor transport, and permit limits through BioWin/GPS-X, SWMM, AERMOD/CALPUFF, GAC/IX and activated-sludge design, and 40 CFR Part 136 QA/QC, while treating nitrifier washout, clarifier upset, PFAS breakthrough, and remediation rebound as first-class failure modes. | K-Dense-AI/scientific-agents |
| [environmental-health-scientist](https://agentpluginsdirectory.com/plugins/environmental-health-scientist) | Reasons from source, pathway, receptor chains, classical vs Berkson exposure error, and tiered biomonitoring (NHANES/BEs); runs STROBE-grade epi, IRIS/OEHHA/ATSDR risk assessment, AERMOD/CALPUFF, EPHT/EJSCREEN, and HIA while treating surrogate misclassification, mobility bias, and detection≠harm as first-class failure modes. | K-Dense-AI/scientific-agents |
| [environmental-microbiologist](https://agentpluginsdirectory.com/plugins/environmental-microbiologist) | Reasons from spatial patchiness, redox thermodynamic ceilings, and process-over-taxonomy guild function through DADA2/QIIME2 amplicons keyed to SILVA/GTDB/PR2/UNITE, metaSPAdes/MetaBAT2 MAGs vetted by CheckM/GUNC, and SIP/qSIP rate assays paired with IC/GC/ICP-MS chemistry, while treating extraction-batch effects, relic and extracellular DNA, primer-window bias, and core pseudoreplication as first-class failure modes. | K-Dense-AI/scientific-agents |
| [environmental-policy-analyst](https://agentpluginsdirectory.com/plugins/environmental-policy-analyst) | Reasons from statutory authority, baseline definition, and monetization boundaries through NEPA/ESA compliance, Circular A-4 RIAs, EPA SC-GHG and benefit transfer, IAM/IPCC scenario use, and APA regulatory comment while treating discount-rate dominance, weak transfer extrapolation, IAM structural uncertainty, and baseline inflation as first-class failure modes. | K-Dense-AI/scientific-agents |
| [environmental-scientist](https://agentpluginsdirectory.com/plugins/environmental-scientist) | Reasons from source-pathway-receptor linkages, multimedia partitioning, and dose-as-exposure through conceptual site models, fate models (MODFLOW/MT3DMS, AERMOD), SW-846 QA/QC chains, and ProUCL/Mann-Kendall statistics while treating censoring bias, conceptual-model error, well-construction artifacts, and seasonal confounding as first-class failure modes. | K-Dense-AI/scientific-agents |
| [enzymologist](https://agentpluginsdirectory.com/plugins/enzymologist) | Reasons from catalytic mechanism, kcat/Km, elementary rate constants, and active-site [E]t through Michaelis-Menten and global fitting in KinTek Explorer, stopped-flow/quench-flow, SPR/BLI/ITC, and STRENDA/EnzymeML reporting while treating substrate inhibition, morpheein equilibria, coupled-assay artifacts, and colloidal-aggregator inhibitor hits as first-class failure modes. | K-Dense-AI/scientific-agents |
| [epidemiologist](https://agentpluginsdirectory.com/plugins/epidemiologist) | Reasons from person-time, transmission dynamics, and population case definitions through epidemic curves, DAGs, renewal and SEIR models (EpiEstim, deSolve), SaTScan clustering, and STROBE/ORION/GRADE standards while treating confounding, collider stratification from test-positive conditioning, reporting-delay and testing-intensity artifacts, and superspreading overdispersion as first-class failure modes. | K-Dense-AI/scientific-agents |
| [epigeneticist](https://agentpluginsdirectory.com/plugins/epigeneticist) | Reasons from chromatin state, DNA methylation, histone marks, accessibility, and 3D genome topology through ChIP/CUT&RUN, ATAC-seq, WGBS/EM-seq, Hi-C, and dCas9-DNMT3A/KRAB perturbation while treating cell-composition shifts, batch confounding, antibody nonspecificity, Tn5 bias, and incomplete bisulfite conversion as first-class failure modes. | K-Dense-AI/scientific-agents |
| [ethologist](https://agentpluginsdirectory.com/plugins/ethologist) | Reasons from Tinbergen's four questions and versioned species vs experimental ethograms; scores with BORIS (Cohen's κ per behavior), Altmann focal/scan budgets, and ARRIVE 2.0/study-plan lab reporting while treating observer expectation, field, lab arena mismatch, habituation, and pseudoreplication as first-class failure modes. | K-Dense-AI/scientific-agents |
| [evolutionary-biologist](https://agentpluginsdirectory.com/plugins/evolutionary-biologist) | Reasons from coalescent demography, MSC gene-tree discordance, and selection, drift nulls; runs IQ-TREE/BEAST/ASTRAL/ANGSD workflows while treating LBA, rogue taxa, batch effects, and uncorrected genome scans as first-class failure modes. | K-Dense-AI/scientific-agents |
| [exercise-physiologist](https://agentpluginsdirectory.com/plugins/exercise-physiologist) | Reason from the Fick principle and verified gas exchange: separate VO2max, VT1/RCP, MLSS, and lactate kinetics before metabolic carts, biopsy, MRS, or periodization prescriptions. | K-Dense-AI/scientific-agents |
| [exoplanet-scientist](https://agentpluginsdirectory.com/plugins/exoplanet-scientist) | Reasons from Keplerian motion, transit and RV geometry, and degenerate retrieval spaces through TLS/BLS searches, centroid and odd-even vetting, RadVel and GP activity models, and petitRADTRANS retrievals while treating eclipsing-binary blends, stellar-rotation-mimicking RV signals, and look-elsewhere completeness cliffs as first-class failure modes. | K-Dense-AI/scientific-agents |
| [experimental-physicist](https://agentpluginsdirectory.com/plugins/experimental-physicist) | Reasons from GUM error budgets, traceable calibration chains, and multiplied signal-chain transfer functions, separating Type A and Type B uncertainty, null runs, and ELN-linked reproducibility before precision or discovery claims. | K-Dense-AI/scientific-agents |
| [extremophile-biologist](https://agentpluginsdirectory.com/plugins/extremophile-biologist) | Reason from physicochemical limits, T, pH, salinity, pressure, and redox, as filters on membrane chemistry, osmoadaptation, chaperones, and cultivation fidelity before astrobiology or extremozyme claims. | K-Dense-AI/scientific-agents |
| [fermentation-scientist](https://agentpluginsdirectory.com/plugins/fermentation-scientist) | Reasons from Monod, Luedeking, Piret kinetics, overflow μcrit, OTR/RQ/RAMOS analytics, DoE media optimization, and 13C-MFA/COBRApy flux bounds while treating stuck-ferment ethanol×T synergy, SSF heat/moisture gradients, and OD-as-biomass red herrings as first-class failure modes. | K-Dense-AI/scientific-agents |
| [finite-element-analyst](https://agentpluginsdirectory.com/plugins/finite-element-analyst) | Reasons from discretization error, element technology, and constraint physics; runs mesh convergence and Richardson studies, Nastran/Abaqus/ANSYS workflows, RBE2/RBE3 and contact discipline, and ASME V&V 10 verification-before-validation reporting on governing QoIs. | K-Dense-AI/scientific-agents |
| [fire-protection-engineer](https://agentpluginsdirectory.com/plugins/fire-protection-engineer) | Reasons from NFPA 13 Hazen-Williams hydraulics (K-factor, remote area, hose stream) and NFPA 101 egress (occupant load, travel distance, capacity factors) through NFPA 92 smoke containment/management, ASET/RSET PBD, and FDS/CFAST/CONTAM/PyroSim modeling while treating breached compartmentation, C-factor/fitting errors, and supply-curve shortfall as first-class failure modes. | K-Dense-AI/scientific-agents |
| [fisheries-scientist](https://agentpluginsdirectory.com/plugins/fisheries-scientist) | Reasons from recruitment, growth, and natural and fishing mortality through state-space assessment models (SS3, SAM, JABBA), CPUE/GLM standardization, and reference points like F_MSY and B_lim under ICES and Magnuson-Stevens frameworks, while treating hyperstability, retrospective bias (Mohn's rho), unaccounted discard mortality, and misspecified M or selectivity as first-class failure modes. | K-Dense-AI/scientific-agents |
| [flavor-fragrance-chemist](https://agentpluginsdirectory.com/plugins/flavor-fragrance-chemist) | Reasons from odor activity values, threshold perception, matrix release, and degradation kinetics through GC-MS with retention indices, GC-O/AEDA, chiral GC authentication, ISO 8586 trained sensory panels, and IFRA/FEMA regulatory limits while treating aldehyde oxidation, citral cyclization, top-note fade, and allergen exceedance as first-class failure modes. | K-Dense-AI/scientific-agents |
| [fluid-dynamicist](https://agentpluginsdirectory.com/plugins/fluid-dynamicist) | Reasons from Navier: Stokes and dimensionless scaling through RANS/LES/DNS selection, mesh/y+ strategy, OpenFOAM/Fluent workflows, and MMS + ASME V&V 20 / PIV validation. | K-Dense-AI/scientific-agents |
| [fluid-mechanics-engineer](https://agentpluginsdirectory.com/plugins/fluid-mechanics-engineer) | Reasons from Navier, Stokes reductions through Darcy, Weisbach/Crane TP-410 pipe networks, pump system curves, NPSH/affinity laws, HI turbomachinery selection, and ASME V&V 20 CFD validation when simulation supports design. | K-Dense-AI/scientific-agents |
| [food-chemist](https://agentpluginsdirectory.com/plugins/food-chemist) | Reasons from food matrix effects, aw and lipid oxidation, AOAC-validated HPLC/GC-MS/LC-MS/MS, FoodData Central/FNDDS, and trained sensory panels while treating matrix suppression, accelerated-shelf-life misuse, and untrained-taster data as first-class failure modes. | K-Dense-AI/scientific-agents |
| [food-engineer](https://agentpluginsdirectory.com/plugins/food-engineer) | Reasons from water activity, thermal microbiology, transport-coupled reaction, and rheology through heat-penetration studies, F0/D/z lethality integration, HACCP with prerequisite programs, and CFR Title 21 LACF/acidified-food rules while treating cold-point under-processing, aw and pH drift, post-process contamination, and unvalidated scale-up as first-class failure modes. | K-Dense-AI/scientific-agents |
| [food-microbiologist](https://agentpluginsdirectory.com/plugins/food-microbiologist) | Reasons from food as a hurdle-governed matrix of water activity, pH, and redox through BAM/ISO reference methods, c/n/m/M sampling plans, PMA-v-qPCR, and ComBase kinetics while treating VBNC and injured cells, post-process contamination, matrix inhibition, and unconfirmed PCR hits as first-class failure modes. | K-Dense-AI/scientific-agents |
| [food-scientist](https://agentpluginsdirectory.com/plugins/food-scientist) | Reasons from a_w and GAB isotherms, Maillard/acrylamide kinetics, HLB emulsions, TPA/rheology, ISO sensory methods, and HACCP/FSMA preventive controls while treating aw, moisture conflation, HLB-only emulsion fixes, and Arrhenius misuse as first-class failure modes. | K-Dense-AI/scientific-agents |
| [forensic-chemist](https://agentpluginsdirectory.com/plugins/forensic-chemist) | Reasons from chain of custody, validated methods, measurement uncertainty, and class-versus-individual characteristics through GC-MS, LC-MS/MS, FTIR, and SWGDRUG-aligned identification under ISO/IEC 17025, while treating carryover contamination, secondary transfer, isomer co-elution, and upgrading equivocal results into source attribution as first-class failure modes. | K-Dense-AI/scientific-agents |
| [forestry-scientist](https://agentpluginsdirectory.com/plugins/forestry-scientist) | Reasons from silvicultural systems, site index, DGH and DBH increment, and FIA cruise design through FVS/ORGANON calibration, LiDAR area-based inventory with support matching, and IPCC carbon pools while treating site-index misassignment, plot edge effects, and change-of-spatial-support bias as first-class failure modes. | K-Dense-AI/scientific-agents |
| [formal-methods-researcher](https://agentpluginsdirectory.com/plugins/formal-methods-researcher) | Reasons from operational semantics and temporal logics through SPIN/TLA+/PRISM, Coq/Lean/Isabelle, Z3/CVC5, refinement and separation logic, vacuity and false-positive diagnosis, and Dafny/F* versus property-based testing boundaries. | K-Dense-AI/scientific-agents |
| [foundation-engineer](https://agentpluginsdirectory.com/plugins/foundation-engineer) | Reasons from effective stress, ULS versus SLS limit states, and construction-altered soil behavior through CPT/SPT logging, triaxial and oedometer testing, LRFD φ-factor checks, and LPILE/PLAXIS analysis while treating liquefaction-driven lateral spread, negative skin friction downdrag, differential settlement, and scour as first-class failure modes. | K-Dense-AI/scientific-agents |
| [fpga-engineer](https://agentpluginsdirectory.com/plugins/fpga-engineer) | Reasons from metastability budgets, setup/hold margins, and tool-reported WNS/TNS through Vivado/Quartus timing and CDC reports, XDC/SDC constraints, Spyglass/Verilator lint, and ILA/IBERT lab bring-up while treating unsafe clock-domain crossings, reset domain crossings, sim-versus-silicon X mismatches, and unconstrained timing paths as first-class failure modes. | K-Dense-AI/scientific-agents |
| [functional-genomics-scientist](https://agentpluginsdirectory.com/plugins/functional-genomics-scientist) | Reasons from perturbation as causal probe, genotype-to-phenotype linkage, library representation, and effect-size-plus-FDR statistics through MAGeCK/BAGEL/CERES-Chronos, CRISPRcleanR, CRISPResso2, MPRAnalyze, and Perturb-seq pipelines while treating MOI/bottleneck artifacts, copy-number and p53/DSB toxicity, RNAi seed effects, and guide-assignment or gating errors as first-class failure modes. | K-Dense-AI/scientific-agents |
| [fusion-scientist](https://agentpluginsdirectory.com/plugins/fusion-scientist) | Reasons from Lawson triple product and Q through tokamak/stellarator confinement (H-mode, ELMs, RMP), NBI/ICRH/ECRH heating, EFIT/TRANSP/SOLPS-ITER workflows, ITER/JET/DIII-D/W7-X benchmarks, PMI (W/Be PFCs), and tritium breeding blankets. | K-Dense-AI/scientific-agents |
| [galactic-astronomer](https://agentpluginsdirectory.com/plugins/galactic-astronomer) | Reasons from distance ladders, dust extinction, and survey selection functions through Gaia DR3 cross-matches, isochrone and Bayesian SFH fitting (PARSEC/MIST, Starfish), and orbit integration in named potentials (McMillan17, MWPotential2015) via galpy, Agama, and Gala, while treating parallax-S/N and RUWE failures, unresolved binaries and fiber collisions, dust-or-crowding overdensities, and spiral-arm-mimicking-streams as first-class failure modes. | K-Dense-AI/scientific-agents |
| [gene-therapy-scientist](https://agentpluginsdirectory.com/plugins/gene-therapy-scientist) | Reasons from vector biology, biodistribution, linked potency, and integration risk through ddPCR titer, empty/full analytics, ISA and off-target NGS (GUIDE-seq, CIRCLE-seq), and ICH S12 nonclinical design while treating pre-existing capsid neutralizing antibodies, RCV/RCA/RCR positivity, gonadal vector genome, and oligoclonal VCN expansion as first-class failure modes. | K-Dense-AI/scientific-agents |
| [genetic-counselor](https://agentpluginsdirectory.com/plugins/genetic-counselor) | Reasons from probabilistic penetrance, Bayesian pretest probability, and patient autonomy through three-generation pedigrees, ACMG/AMP variant criteria, ClinVar/ClinGen/gnomAD, NCCN and CPIC guidelines, and cascade-testing protocols while treating VUS over-upgraded to pathogenic, screening-versus-diagnostic confusion (NIPT vs amnio/CVS), and unaddressed psychosocial and GINA discrimination risk as first-class failure modes. | K-Dense-AI/scientific-agents |
| [geneticist](https://agentpluginsdirectory.com/plugins/geneticist) | Reasons from particulate inheritance, segregation, recombination, allele frequency, and genotype-phenotype evidence through ACMG/AMP-ClinGen classification, gnomAD/ClinVar/OMIM, HPO phenotyping, and PLINK/GATK/VEP QC while treating sample swaps, cryptic relatedness, population stratification, LD tagging, phenocopies, winner's curse, and build/transcript mismatch as first-class failure modes. | K-Dense-AI/scientific-agents |
| [genome-engineering-crispr-scientist](https://agentpluginsdirectory.com/plugins/genome-engineering-crispr-scientist) | Reasons from NHEJ/HDR/MMEJ competition and editor modality choice through CRISPick/CRISPResso2 guide design, LOCK/lssDNA and RNP HDR, base and prime editing (PE4/PE5, epegRNA), CAST-Seq/UDiTaS on-target SV assessment, clonal genotyping, and FDA/IBC-bound off-target and genome-integrity analytics. | K-Dense-AI/scientific-agents |
| [genomicist](https://agentpluginsdirectory.com/plugins/genomicist) | Reasons from reference-relative coordinates, haplotypes, variant classes, and sequencing-as-measurement through GATK/DeepVariant, VEP/ClinVar/gnomAD, GIAB/hap.py benchmarking, and ACMG/AMP-ClinGen frameworks while treating build mismatches, paralog/pseudogene and GC dropout artifacts, contamination and index hopping, batch effects, and annotation drift as first-class failure modes. | K-Dense-AI/scientific-agents |
| [geobiologist](https://agentpluginsdirectory.com/plugins/geobiologist) | Reasons from metabolism, redox geochemistry, microbe-mineral interactions, and diagenetic filters through stromatolite microfabric petrography, CSIA and clumped-isotopologue analysis, nanoSIMS-FISH mapping, and NASA's Ladder of Life Detection while treating Fischer-Tropsch-type synthesis, serpentinization, Rayleigh distillation, drilling-fluid contamination, and epigenetic overprint as first-class failure modes. | K-Dense-AI/scientific-agents |
| [geochemist](https://agentpluginsdirectory.com/plugins/geochemist) | Reasons from Gibbs equilibria, mass and isotope balance, and fluid, rock interaction through stable (δ) and radiogenic (ε, isochron) systems, ICP-MS/LA-ICP-MS/TIMS/MC-ICP-MS/IRMS, PHREEQC/Perple_X phase modeling, and EarthChem/GeoReM workflows while treating alteration, matrix effects, Pb loss, mixing arrays, and Fretwell's Law violations as first-class failure modes. | K-Dense-AI/scientific-agents |
| [geodesist](https://agentpluginsdirectory.com/plugins/geodesist) | Reasons from coordinates as four-dimensional objects with epoch, velocity, and frame realization (ITRS vs. ITRF2020, WGS84) through GAMIT/GLOBK and Bernese PPP-AR, SBAS/PS-InSAR with GACOS atmospheric correction, IERS Conventions, and 14-parameter Helmert transforms while treating ITRF-realization switches, undocumented APC/ATX mismatches, monument motion, and unscreened seasonal loading as first-class failure modes. | K-Dense-AI/scientific-agents |
| [geodynamics-tectonics-scientist](https://agentpluginsdirectory.com/plugins/geodynamics-tectonics-scientist) | Reasons from plate kinematics, lithospheric rheology, and interseismic-versus-coseismic strain partitioning through GAMIT/GLOBK and MintPy geodesy, Okada/viscoelastic slip inversion, OxCal paleoseismic chronologies, ASPECT mantle modeling, and OpenQuake/USGS NSHM hazard while treating InSAR atmospheric delay, monument instability, unmodeled postseismic afterslip, and incompatible-timescale rate stacking as first-class failure modes. | K-Dense-AI/scientific-agents |
| [geographic-information-scientist-gis](https://agentpluginsdirectory.com/plugins/geographic-information-scientist-gis) | Reasons from location, topology, scale, and positional uncertainty through PostGIS/GDAL pipelines, explicit EPSG/datum choices, kriging with cross-validated variograms, and ISO 19115/FGDC metadata while treating MAUP and ecological fallacy, Web Mercator area statistics, floating-point slivers and broken topology, and spatial-autocorrelation-inflated significance as first-class failure modes. | K-Dense-AI/scientific-agents |
| [geological-oceanographer](https://agentpluginsdirectory.com/plugins/geological-oceanographer) | Reasons from sediment transport mechanics, stratigraphic context, and accommodation through multibeam (EM122/EM712) and sub-bottom/seismic imaging, gravity and piston cores with XRF/X-ray CT, CTD tow-yo ORP/Mn/CH4 plume surveys, and Bouma/Lowe facies analysis, while treating bathymetry-mimicking multipath artifacts, bioturbation homogenizing event beds, glacial isostasy mistaken for eustasy, and single-core turbidite extrapolation as first-class failure modes. | K-Dense-AI/scientific-agents |