---
title: "Agent Plugins Directory, page 76 of 81"
description: "| Name | Description | Repo |"
canonical: https://agentpluginsdirectory.com/directory/page/76
last-updated: 2026-10-03
---

# Agent Plugins Directory, page 76 of 81

| Name | Description | Repo |
| --- | --- | --- |
| [low-temperature-physicist](https://agentpluginsdirectory.com/plugins/low-temperature-physicist) | Reasons from kT budgets, He-3/He-4 dilution refrigeration, and BCS/GL superconductivity; measures Tc, QHE, and Landauer conductance with lock-in/SQUID workflows while treating wiring heat loads, Kapitza resistance, flux trapping, TLS dielectric loss, and sample-vs-MXC thermometer mismatch as first-class failure modes. | K-Dense-AI/scientific-agents |
| [machine-learning-engineer](https://agentpluginsdirectory.com/plugins/machine-learning-engineer) | Reasons from decision-policy framing, Google's Rules of ML, point-in-time data, and prefill/decode inference physics through GBDT/PyTorch baselines, vLLM/SGLang/KServe serving with FP8/AWQ quantization, hybrid-retrieval RAG, RAGAS and human-validated LLM judges, OpenTelemetry GenAI tracing, and post-Omnibus EU AI Act obligations while treating train-serve skew, temporal leakage, prompt injection, LLM nondeterminism, and degenerate feedback loops as first-class failure modes. | K-Dense-AI/scientific-agents |
| [machine-learning-researcher](https://agentpluginsdirectory.com/plugins/machine-learning-researcher) | Reasons from population risk, double descent, and inductive bias; enforces sacred test sets, hierarchical ablations, nested CV, and HELM/Dynabench-aware benchmarking; reports with NeurIPS and Pineau reproducibility checklists while treating leakage, meta-overfitting, benchmark contamination, Goodhart gaming, and seed variance as first-class failure modes. | K-Dense-AI/scientific-agents |
| [mammalogist](https://agentpluginsdirectory.com/plugins/mammalogist) | Reasons from mammalian life history and detectability-limited sampling through ASM MDD taxonomy, Sherman/camera-trap/SCR survey design, occupancy and SECR models, bat acoustic validation, and museum voucher discipline while treating trap heterogeneity, camera autocorrelation, closure violation, and WNS decontamination gaps as first-class failure modes. | K-Dense-AI/scientific-agents |
| [manufacturing-engineer](https://agentpluginsdirectory.com/plugins/manufacturing-engineer) | Reasons from process physics, capability, and cost through Shercliff-Lovatt process selection, ASME Y14.5 GD&T, CAM simulation, and AIAG APQP/PPAP with MSA-gated SPC capability, treating high %GRR masquerading as variation, false Cpk on unstable or short runs, datum-scheme mismatch, and uncontrolled ECN tweaks as first-class failure modes. | K-Dense-AI/scientific-agents |
| [marine-biologist](https://agentpluginsdirectory.com/plugins/marine-biologist) | Reasons from water-mass stratification, CTD: Niskin and CalCOFI-style net tows, BRUV, and MiFish/COI eDNA through OBIS/WoRMS/GBIF and ARGO/BGC-Argo; treats mesopelagic DVM, hypoxia/Ω_aragonite constraints, fluorometer quenching, BRUV MaxN bias, and transect pseudoreplication as first-class failure modes. | K-Dense-AI/scientific-agents |
| [marine-engineer](https://agentpluginsdirectory.com/plugins/marine-engineer) | Reasons from propulsion thermodynamics, shaft BPF/torsional barred speeds, central LT/HT cooling, class machinery surveys, and ISO 15016:2025 sea trials while treating cat fines liner wear, scavenge fire, purifier mis-set, blackout PMS logic, and tropical SW fouling as first-class failure modes. | K-Dense-AI/scientific-agents |
| [marine-geologist](https://agentpluginsdirectory.com/plugins/marine-geologist) | Reasons from stratigraphy, sedimentary processes, geophysical facies, and age control through multibeam bathymetry, 2D/3D seismic, piston/IODP cores tied via synthetic seismograms, and CSF-A age models while treating bad-SVP false scarps, BSRs mimicking free gas, gas-charged push-down faking structural offset, and reworked-carbon radiocarbon dates as first-class failure modes. | K-Dense-AI/scientific-agents |
| [mass-spectrometrist](https://agentpluginsdirectory.com/plugins/mass-spectrometrist) | Reasons from ion formation, m/z resolution and mass accuracy, fragmentation, and calibrated ion statistics through ESI/APCI/MALDI tuning, CID/HCD/ETD MS/MS, isotope-pattern formula assignment, and spectral libraries (NIST, mzCloud, GNPS) under FDA/ICH M10/MSI tiers, while treating matrix suppression, PEG/siloxane/keratin contamination, decoy-driven FDR inflation, and unassigned adducts as first-class failure modes. | K-Dense-AI/scientific-agents |
| [materials-chemist](https://agentpluginsdirectory.com/plugins/materials-chemist) | Reasons from Kröger, Vink defect equilibria, soft-chemistry routes (sol-gel, hydrothermal, ALD), and structure, property links; validates with GSAS-II/TOPAS Rietveld QPA, GIPAW ssNMR, ICSD/COD/Materials Project, and XPS/BET protocols while treating preferred orientation, AdC mis-referencing, degas artifacts, and metastable phase traps as first-class failure modes. | K-Dense-AI/scientific-agents |
| [materials-physicist](https://agentpluginsdirectory.com/plugins/materials-physicist) | Reasons from band structure, defects, strain, and Landau order parameters; integrates HRXRD/RSM, ARPES, TEM/4D-STEM, van der Pauw transport, and SQUID/MOKE with Materials Project/VASP while treating matrix-element ARPES artifacts, substrate-dominated GIXRD, contact-resistance Hall errors, and DFT gap overclaim as first-class failure modes. | K-Dense-AI/scientific-agents |
| [materials-scientist](https://agentpluginsdirectory.com/plugins/materials-scientist) | Reasons from CALPHAD phase diagrams, Scheil solidification, and Hall, Petch microstructure, property links; validates with XRD Rietveld QPA, EBSD, TEM/STEM, and ASTM mechanical testing while treating preferred orientation, FIB Ga artifacts, EBSD overlap, and Rietveld overfitting as first-class failure modes. | K-Dense-AI/scientific-agents |
| [mathematical-analyst](https://agentpluginsdirectory.com/plugins/mathematical-analyst) | Reasons from function-space topology, convergence modes, and constant-dependent inequalities (Hölder, Sobolev, Gronwall) through compactness theorems (Rellich-Kondrachov, Banach-Alaoglu), Calderon-Zygmund and Schauder estimates, and Lax-Milgram while treating limit-integral swaps without dominated convergence, boundary-degenerating constants, weak-versus-classical regularity gaps, and concentration-compactness loss as first-class failure modes. | K-Dense-AI/scientific-agents |
| [mathematical-modeler](https://agentpluginsdirectory.com/plugins/mathematical-modeler) | Reasons from nondimensionalization, conservation/positivity laws, and minimal-viable model structure through mechanistic ODE/PDE, stochastic, and agent-based formulations, profile-likelihood and Fisher-information identifiability, and Sobol/Morris sensitivity analysis, while treating sloppy unidentifiable parameters, structural model uncertainty, and out-of-regime extrapolation as first-class failure modes. | K-Dense-AI/scientific-agents |
| [mathematical-physicist](https://agentpluginsdirectory.com/plugins/mathematical-physicist) | Reasons from Hilbert-space domains, Wightman/OS and Haag, Kastler axioms, constructive QFT, Gibbs measures, and spectral/scattering theory; uses Reed, Simon, Glimm, Jaffe, MathSciNet/math-ph, while treating wrong self-adjoint extensions, invalid Wick rotation, limit-order swaps, and lattice-as-continuum claims as first-class failure modes. | K-Dense-AI/scientific-agents |
| [mathematical-statistician](https://agentpluginsdirectory.com/plugins/mathematical-statistician) | Reasons from LAN, empirical processes, and influence functions; proves M/Z-estimator limits, minimax rates (Fano/Le Cam/Assouad), and semiparametric efficiency while validating with ADEMP simulations and treating naive bootstrap, non-Donsker classes, and debiasing sparsity violations as first-class failure modes. | K-Dense-AI/scientific-agents |
| [mechanical-design-engineer](https://agentpluginsdirectory.com/plugins/mechanical-design-engineer) | Reasons from function, datum reference frames, and tolerance budgets; releases inspectable drawings and MBD through ASME Y14.5 GD&T, WC/RSS/Monte Carlo stack-ups, SAE J1739 DFMEA (Action Priority), and Boothroyd, Dewhurst DFM/DFA, not stress plots alone. | K-Dense-AI/scientific-agents |
| [mechanical-engineer](https://agentpluginsdirectory.com/plugins/mechanical-engineer) | Reasons from equilibrium, failure physics, and code-backed allowables; designs through requirements, GD&T, DFMEA, and hand/FEA/test validation with explicit governing failure modes. | K-Dense-AI/scientific-agents |
| [mechatronics-engineer](https://agentpluginsdirectory.com/plugins/mechatronics-engineer) | Reasons from reflected inertia, control bandwidth, sensor physics, and thermal duty cycle through Bode loop-shaping with phase/gain margins, FOC current-velocity-position loops, plant identification, HIL, and IEC 61800-5-2 STO architecture while treating backlash and structural-mode resonance, transport-delay phase loss, encoder aliasing, and EMC ground loops as first-class failure modes. | K-Dense-AI/scientific-agents |
| [medical-geneticist](https://agentpluginsdirectory.com/plugins/medical-geneticist) | Reasons from pedigree priors, HPO phenotype match, and ACMG/ClinGen variant classification; integrates exome/genome, CMA, RNA splicing, NBS ACT pathways, Tier 3 carrier screening, SF v3.3, and CPIC pharmacogenomics while treating VUS overcall, CPM/NIPT discordance, mtDNA heteroplasmy sampling, and SpliceAI-only splicing claims as first-class failure modes. | K-Dense-AI/scientific-agents |
| [medical-mycologist](https://agentpluginsdirectory.com/plugins/medical-mycologist) | Reasons from EUCAST/CLSI antifungal susceptibility, culture and MALDI-TOF ID, galactomannan/β-D-glucan assays, and CLSI breakpoints while treating contamination, cryptic species mis-ID, and azole MIC trailing as first-class failure modes. | K-Dense-AI/scientific-agents |
| [medical-parasitologist](https://agentpluginsdirectory.com/plugins/medical-parasitologist) | Reasons from specimen-stage fit, exposure-based pretest probability, and antigen-versus-antibody kinetics through thick/thin Giemsa films, formalin-ethyl-acetate concentration with trichrome, multiplex PCR, and EITB serology against CDC DPDx and WHO algorithms, while treating pfhrp2/3-deleted RDT false-negatives, single-O&P misses of Strongyloides, and colonization-mistaken-for-infection as first-class failure modes. | K-Dense-AI/scientific-agents |
| [medical-physicist](https://agentpluginsdirectory.com/plugins/medical-physicist) | Reasons from CTDIvol/SSDE, HU accuracy, ACR/MQSA QA, TG-126 PET/CT, TG-18/TG-270 displays, and NCRP 147 shielding across CT, MRI, mammography, NM/PET, and therapy QA while treating phantom-mismatch dose claims, SUV normalization drift, display washout, and post-upgrade MEE gaps as first-class failure modes. | K-Dense-AI/scientific-agents |
| [medicinal-chemist](https://agentpluginsdirectory.com/plugins/medicinal-chemist) | Reasons from structure-activity relationships, lipophilicity and unbound-fraction physicochemistry, synthetic accessibility, and target-product-profile multiparameter optimization through LLE/Fsp3/QED scoring, FEP+/Glide docking validated against co-crystal and SPR data, ELN-tracked LC-MS/NMR synthesis, and DMPK panels (microsomal CL, Caco-2 efflux, hERG, CYP) while treating PAINS and aggregation assay artifacts, biochemical-versus-cellular potency gaps, reactive-metabolite soft spots, and freedom-to-operate cliffs as first-class failure modes. | K-Dense-AI/scientific-agents |
| [membrane-biophysicist](https://agentpluginsdirectory.com/plugins/membrane-biophysicist) | Reasons from Helfrich elasticity, Lo/Ld phase behavior, and intrinsic curvature; builds GUVs, SLBs, nanodiscs, and BLMs; reads Laurdan GP, FRAP/FCS, aspiration, and electrophysiology while treating multilamellarity, detergent carryover, and probe misinterpretation as first-class failure modes. | K-Dense-AI/scientific-agents |
| [mems-engineer](https://agentpluginsdirectory.com/plugins/mems-engineer) | Reasons from scale-dependent mechanics, squeeze-film damping, and electrostatic pull-in through DRIE Bosch/surface micromachining, CoventorMP/COMSOL, foundry PDKs, LDV/WLI metrology, and AEC-Q103 qual while treating release stiction, DRIE scallop bias, package-stress offset drift, and functional-WLT-vs-reliability gaps as first-class failure modes. | K-Dense-AI/scientific-agents |
| [metabolomics-scientist](https://agentpluginsdirectory.com/plugins/metabolomics-scientist) | Reasons from MSI annotation levels, pooled-QC RSD and D-ratio gates, MZmine/MS-DIAL/XCMS pipelines, HMDB/GNPS identification, and MetaboAnalyst batch correction (ComBat, QC-RLSC); treats injection-order drift, ComBat over-correction, and Level-5 pathway stories as first-class failure modes. | K-Dense-AI/scientific-agents |
| [metallurgist](https://agentpluginsdirectory.com/plugins/metallurgist) | Reasons from phase diagrams, TTT/CCT paths, and Scheil solidification through Jominy hardenability (ASTM A255), ASM heat-treat cycles, metallography (ASTM E3/E112/E407), and staged failure analysis while treating decarburization, quench cracking, HAZ liquation, hot tearing, and microsegregation as first-class failure modes. | K-Dense-AI/scientific-agents |
| [meteorologist](https://agentpluginsdirectory.com/plugins/meteorologist) | Reasons from hydrostatic and geostrophic balance, scale-dependent dynamics, and the obs-to-NWP pipeline; works the Snellman funnel, matches HRRR/GFS/ECMWF to scale, and treats spin-up, convective scheme bias, radar AP, and PoP misinterpretation as first-class failure modes. | K-Dense-AI/scientific-agents |
| [metrology-scientist](https://agentpluginsdirectory.com/plugins/metrology-scientist) | Reason from SI traceability, GUM uncertainty budgets, and VIM distinctions between calibration and verification; propagates Type A/B components, CIPM MRA equivalence, and ILAC decision rules before any pass/fail claim. | K-Dense-AI/scientific-agents |
| [microbial-ecologist](https://agentpluginsdirectory.com/plugins/microbial-ecologist) | Reasons from Vellend assembly (selection, dispersal, drift, diversification), compositional stats (ANCOM-BC2, MaAsLin2, Aitchison), and SILVA/GTDB/EMP workflows; treats kitome contamination, GCN bias, pseudoreplication, SparCC-as-interaction, and PICRUSt2-as-measured-function as first-class failure modes. | K-Dense-AI/scientific-agents |
| [microbial-physiologist](https://agentpluginsdirectory.com/plugins/microbial-physiologist) | Reasons from Monod/chemostat (μ = D), YX/S and Pirt maintenance, Crabtree/overflow, 13C-MFA and FBA, and BMSAB taxonomy; treats OD-as-biomass yield error, FBA-as-measured-flux, washout misread, and portable ms across media as first-class failure modes. | K-Dense-AI/scientific-agents |
| [microbiologist](https://agentpluginsdirectory.com/plugins/microbiologist) | Reasons from culturability limits, CFU/MPN enumeration, selective media, DADA2/QIIME2 16S ASVs (SILVA/GTDB), and shotgun metagenomics (Kraken2, MetaPhlAn, HUMAnN); treats plate-count anomaly, compositional stats pitfalls, kit contamination, and index hopping as first-class failure modes. | K-Dense-AI/scientific-agents |
| [microbiome-scientist](https://agentpluginsdirectory.com/plugins/microbiome-scientist) | Reasons from compositional and longitudinal stats (MaAsLin2, ANCOM-BC2), STORMS pre-analytics, FMT/LBP and diet trials, and multi-omics integration; treats PPI/antibiotic confounders, kitome contamination, host-DNA swamping, and HMA causality overclaim as first-class failure modes. | K-Dense-AI/scientific-agents |
| [microelectronics-engineer](https://agentpluginsdirectory.com/plugins/microelectronics-engineer) | Reasons from CTE mismatch, θja networks (JESD51), and package RLC through wire bond (Au, Al IMC, loop height), flip-chip (UBM, underfill, HIP/NWO), J-STD-020 MSL, and JESD22 TCT/uHAST; treats datasheet θja without board definition, wire sweep, die-attach voids, and soak-mode mismatch as first-class failure modes. | K-Dense-AI/scientific-agents |
| [mineralogist](https://agentpluginsdirectory.com/plugins/mineralogist) | Reasons from crystal chemistry, Pauling coordination, and converging optics, XRD/Raman, EPMA evidence; uses RRUFF/Mindat/AMCSD and CNMNC Checklist 2025 while treating preferred orientation, clay EG/heat triads, metamict amorphization, and QEMSCAN library bias as first-class failure modes. | K-Dense-AI/scientific-agents |
| [mining-geologist](https://agentpluginsdirectory.com/plugins/mining-geologist) | Reasons from deposit-type models (porphyry, VMS, SEDEX, orogenic Au, IOCG, skarn) through oriented core logging, domaining, variography, OK/MIK/LUC estimation, and Chain-of-Mining reconciliation to JORC Table 1, NI 43-101 Item 14, and CIM MRMR reporting; uses Leapfrog, GIM Suite/MX Deposit, and Parker F-series factors while treating support/compositing errors, batch QA/QC failures, OK smoothing bias, and Inferred-overclaim as first-class failure modes. | K-Dense-AI/scientific-agents |
| [mlops-engineer](https://agentpluginsdirectory.com/plugins/mlops-engineer) | Reasons from data contracts, feature parity, evaluation gates, and rollback-readiness through MLflow/W&B registries, Feast feature stores, KServe/Triton serving, Great Expectations/TFDV validation, and Evidently PSI/KS drift monitors while treating train-serve skew, data leakage, silent degradation, and schema/concept drift as first-class failure modes. | K-Dense-AI/scientific-agents |
| [molecular-biologist](https://agentpluginsdirectory.com/plugins/molecular-biologist) | Reasons from central-dogma sequence flow, binding affinity (Kd/Km/kcat), gene regulation, and biological-versus-technical replicate structure through MIQE-compliant RT-qPCR, ddPCR, Western/flow/microscopy, CRISPR editing with rescue, and IWGAV antibody validation while treating off-target reagent effects, batch effects, mycoplasma and cell-line misidentification, and toxicity-driven artifacts as first-class failure modes. | K-Dense-AI/scientific-agents |
| [molecular-ecologist](https://agentpluginsdirectory.com/plugins/molecular-ecologist) | Reasons from population genetics, eDNA/metabarcoding, and marker choice (microsatellites, SNPs, mtDNA); analyzes with STRUCTURE/ADMIXTURE, hierfstat FST, DADA2 pipelines, and ddPCR; treats null alleles, Wahlund effect, batch effects, and eDNA allelic dropout as first-class failure modes. | K-Dense-AI/scientific-agents |
| [molecular-geneticist](https://agentpluginsdirectory.com/plugins/molecular-geneticist) | Reasons from sequence-as-hypothesis, reference context (genome build, MANE/RefSeq transcript, HGVS), and allele-level molecular consequence through ACMG/AMP-ClinGen criteria, IGV/VEP/SpliceAI/gnomAD/ClinVar review, MIQE-compliant qPCR/ddPCR, and Sanger/NGS validation while treating allele dropout, pseudogene/paralog misalignment, FFPE deamination, contamination and barcode bleed, and transcript/build mismatch as first-class failure modes. | K-Dense-AI/scientific-agents |
| [molecular-neuroscientist](https://agentpluginsdirectory.com/plugins/molecular-neuroscientist) | Reasons from NPQ transmission, AMPAR/NMDAR trafficking, monoamine receptor/transporter systems, optogenetics (ChR2/Chrimson/ACR) with retinal-artifact controls, AAV/rabies circuit tracing, and region RNA-seq with DESeq2/SynGO, integrating synaptic biochemistry, perturbation, and omics while treating mini-detection bias, TVA leak, and batch/composition confounds as first-class failure modes. | K-Dense-AI/scientific-agents |
| [molecular-pathologist](https://agentpluginsdirectory.com/plugins/molecular-pathologist) | Reasons from tumor cellularity, assay-specific LOD, and AMP/ASCO/CAP Tier I, IV classification; validates IHC (CAP ≥90% concordance), FISH (HER2/ALK break-apart), and NGS oncology panels under CAP/CLIA MM09 while treating FFPE deamination, HER2-low/ultralow scoring, PD-L1 TPS vs CPS, and ctDNA CHIP as first-class failure modes. | K-Dense-AI/scientific-agents |
| [molecular-virologist](https://agentpluginsdirectory.com/plugins/molecular-virologist) | Reasons from Baltimore mRNA pathways, RNP/polymerase biochemistry, cap-snatching and expression strategy, CPER/BAC/trVLP rescue, protease cis/trans mapping, viral-factory LLPS, and CRISPR host-factor screens (Brunello/MAGeCK/replicon/TRPPC) with iCLIP/ChIP-seq, while treating CPER PCR errors, DIP packaging competition, minigenome structural-protein signal, and uninfected CRISPR dropout as first-class failure modes. | K-Dense-AI/scientific-agents |
| [mycobacteriologist](https://agentpluginsdirectory.com/plugins/mycobacteriologist) | Reasons from slow-growing acid-fast bacilli, knife-edge NALC-NaOH decontamination, and BSL-3 aerosol risk through MGIT culture, Xpert MTB/RIF and line-probe assays, MALDI-TOF and WGS resistance calls against the WHO mutation catalog while treating over-decontamination false negatives, laboratory cross-contamination pseudo-outbreaks, and NTM colonizer-versus-disease misclassification as first-class failure modes. | K-Dense-AI/scientific-agents |
| [mycologist](https://agentpluginsdirectory.com/plugins/mycologist) | Reasons from fungal life cycles, voucher-first taxonomy, and integrated sporocarp, culture, ITS/multilocus workflows; uses MycoBank/UNITE/MaarjAM, FUSARIUM-ID, EPPO Q-bank, and MycoCosm while treating rich-media non-sporulation, ITS saturation in Fusarium/Penicillium, AMF SSU vs ITS misuse, environmental-DNA-only names, and BSL-3 dimorphic mould handling as first-class failure modes. | K-Dense-AI/scientific-agents |
| [nanomaterials-scientist](https://agentpluginsdirectory.com/plugins/nanomaterials-scientist) | Reasons from size-dependent thermodynamics, surface-to-volume ratio, and DLVO colloidal stability through TEM/STEM statistics, DLS/NTA, XRD Scherrer, XPS, ICP-MS, and PL quantum-yield methods while treating aggregation, beam damage, intensity-weighted DLS sizing bias, and Ostwald ripening as first-class failure modes. | K-Dense-AI/scientific-agents |
| [nanophysicist](https://agentpluginsdirectory.com/plugins/nanophysicist) | Reasons from quantum confinement dimensionality, Coulomb diamonds and SET conditions, Kondo vs Luttinger-liquid power laws, and lock-in cryostat transport through STM/STS, AFM/KPFM, and STEM/EELS while treating charging artifacts, tip convolution, contact resistance, and beam-damage plasmon shifts as first-class failure modes. | K-Dense-AI/scientific-agents |