---
title: "Other Agent Plugins, page 40 of 45"
description: "Plugins whose purpose does not fit any category above, and plugins whose manifest describes too little to classify honestly."
canonical: https://agentpluginsdirectory.com/categories/other/page/40
last-updated: 2026-10-03
---

# Other Agent Plugins, page 40 of 45

Plugins whose purpose does not fit any category above, and plugins whose manifest describes too little to classify honestly.

| Name | Description | Repo |
| --- | --- | --- |
| [gravitational-wave-astronomer](https://agentpluginsdirectory.com/plugins/gravitational-wave-astronomer) | Reasons like a senior GW astronomer across LIGO, Virgo, KAGRA matched-filter CBC searches, calibration-aware PE, GraceDB/GWTC alert, catalog discipline, BAYESTAR/Bilby skymaps, and EM follow-up campaigns. | K-Dense-AI/scientific-agents |
| [green-chemist](https://agentpluginsdirectory.com/plugins/green-chemist) | Reasons from Anastas: Warner 12 principles, Trost atom economy, and PMI/MMI/E-factor mass metrics; selects solvents via CHEM21/GSK/ACS GCIPR guides, integrates catalysis and LCA (ISO 14040), and aligns REACH/CSS with ACS GC&E benchmarking. | K-Dense-AI/scientific-agents |
| [health-economist](https://agentpluginsdirectory.com/plugins/health-economist) | Reasons from QALY/ICER and NMB opportunity-cost framing, NICE reference case and WTP bands, cohort Markov/PSM models with PSA (CEAC/CEAF), ISPOR transferability and DCE conjoint checklists, CHEERS 2022 and trial-based RCT-CEA reporting. | K-Dense-AI/scientific-agents |
| [health-informatician](https://agentpluginsdirectory.com/plugins/health-informatician) | Reasons from semantic interoperability, provenance, and patient safety through FHIR/US Core, SNOMED-LOINC-RxNorm terminology mapping, OMOP/OHDSI ETL with DQD/Achilles, and chart-review PPV validation while treating immortal-time and confounding-by-indication bias, patient-matching/MPI failures, billing-code phenotypes, and vocabulary version drift as first-class failure modes. | K-Dense-AI/scientific-agents |
| [heat-transfer-engineer](https://agentpluginsdirectory.com/plugins/heat-transfer-engineer) | Reasons from conduction, convection, radiation, and coupled fluid-solid physics through thermal resistance networks, Biot/NTU/film-temperature scaling, LMTD and epsilon-NTU exchanger methods, fin efficiency, and conjugate-heat-transfer CFD while treating contact resistance and TIM pump-out, fouling, boiling CHF, non-condensables, and non-conservative interface flux mapping as first-class failure modes. | K-Dense-AI/scientific-agents |
| [heliophysicist](https://agentpluginsdirectory.com/plugins/heliophysicist) | Reasons from MHD, magnetic topology, reconnection, and IMF Bz coupling through SDO/HMI magnetograms, DEM and NLFFF analysis, coronagraph GCS fitting, and WSA-ENLIL/EUHFORIA ensembles while treating LOS foreshortening, AIA stray light, force-free NLFFF breakdown, and Dst/SYM-H saturation as first-class failure modes. | K-Dense-AI/scientific-agents |
| [hematologist](https://agentpluginsdirectory.com/plugins/hematologist) | Reasons from hematopoietic hierarchy, clonal evolution, and hemostatic balance through peripheral smear review, reticulocyte production index, flow cytometry, mixing studies, and WHO/ICC-anchored NGS and cytogenetics, while treating pseudothrombocytopenia, preanalytic line-draw and EDTA artifacts, and missed consumptive emergencies like TTP and DIC as first-class failure modes. | K-Dense-AI/scientific-agents |
| [hepatologist](https://agentpluginsdirectory.com/plugins/hepatologist) | Reasons from hepatic injury pattern (R-value), synthetic function, and portal hypertension through Child-Pugh/MELD 3.0, FIB-4 and elastography, LI-RADS/BCLC staging, SAAG paracentesis, and Baveno VII criteria while treating DILI misattribution, elastography false positives in cholestasis, hypersplenic thrombocytopenia, and missed acute-on-chronic decompensation as first-class failure modes. | K-Dense-AI/scientific-agents |
| [herpetologist](https://agentpluginsdirectory.com/plugins/herpetologist) | Reasons from detectability-limited sampling and ectotherm phenology through VES, pitfall/drift-fence and cover-board arrays, NAAMP call surveys, Program MARK CJS, unmarked/occuTTD occupancy, and Bd/Bsal MW113 qPCR biosecurity; uses AmphibiaWeb, Reptile Database, SSAR 9th ed./CNAH names, Amphibian Disease Portal, GBIF/CoordinateCleaner, and ARRIVE 2.0 while treating weather bias, pitfall selectivity, and pathogen cross-contamination as first-class failure modes. | K-Dense-AI/scientific-agents |
| [high-energy-astrophysicist](https://agentpluginsdirectory.com/plugins/high-energy-astrophysicist) | Reasons from Compton/synchrotron radiative processes and compact-object energetics through HEASARC/Fermi/Swift/XMM/Chandra/NuSTAR/XRISM pipelines, XSPEC/Sherpa spectral fitting, pile-up and background systematics, blazar/GRB/TDE campaigns, and GCN multi-messenger coordination while treating RMF versioning, soft-proton flares, and look-elsewhere significance as first-class failure modes. | K-Dense-AI/scientific-agents |
| [high-performance-computing-specialist](https://agentpluginsdirectory.com/plugins/high-performance-computing-specialist) | Reasons from NUMA topology and hybrid MPI+OpenMP+CUDA decomposition through Slurm fairshare/backfill job design, strong/weak scaling (Amdahl/Gustafson), Darshan/mpiP/Nsight profiling, and parallel HDF5/MPI-IO on Lustre while treating I/O storms, collectives bottlenecks, and rank-binding mistakes as first-class failure modes. | K-Dense-AI/scientific-agents |
| [histologist](https://agentpluginsdirectory.com/plugins/histologist) | Reasons from fixation-through-stain pre-analytical chain (NBF, grossing, processing, embedding orientation, microtomy); validates H&E pH/QC, CAP IHC (90% concordance, predictive scoring systems), RNAscope controls, WSI (60-case validation), and treats floaters, autolysis, crush, ice-crystal, and decalcification artifacts as first-class failure modes. | K-Dense-AI/scientific-agents |
| [horticulturist](https://agentpluginsdirectory.com/plugins/horticulturist) | Reasons from plant-environment-cultural coupling, source-sink carbon partitioning, and chilling/photoperiod thresholds through rootstock-scion matching, DLI/VPD and substrate EC/pH targets, Dynamic/Utah chill models, and CA/MA postharvest setpoints while treating blossom-end rot, bitter pit, tipburn, storage scald, and MRL/PHI breaches as first-class failure modes. | K-Dense-AI/scientific-agents |
| [human-computer-interaction-researcher](https://agentpluginsdirectory.com/plugins/human-computer-interaction-researcher) | Reasons from situated context, Fitts/GOMS/KLM, and CHI contribution types; runs contextual inquiry through LMM/CLMM analysis with SUS/NASA-TLX triangulation; uses Prolific/OSF and treats demand characteristics, novelty effects, ordinal misuse, and WEIRD samples as first-class failure modes. | K-Dense-AI/scientific-agents |
| [hvac-engineer](https://agentpluginsdirectory.com/plugins/hvac-engineer) | Reasons from psychrometric state, parallel heating and cooling load paths, and vapor-compression thermodynamics through TRACE/HAP and EnergyPlus load models, ASHRAE 62.1/55/90.1 and Guideline 36 sequences, and TAB/commissioning per ASHRAE 15/34, while treating low ΔT syndrome, simultaneous reheat fight, coil-leaving condensation, and A2L refrigerant safety as first-class failure modes. | K-Dense-AI/scientific-agents |
| [hydraulic-engineer](https://agentpluginsdirectory.com/plugins/hydraulic-engineer) | Reasons from Bernoulli, Darcy, Weisbach, Moody diagrams, HEC-RAS/EPANET modeling, and pump/system curves while treating transient water hammer, air entrainment, and roughness aging as first-class failure modes. | K-Dense-AI/scientific-agents |
| [hydrogeologist](https://agentpluginsdirectory.com/plugins/hydrogeologist) | Reasons from Darcy's law, mass conservation, aquifer heterogeneity, and coupled biogeochemical transport through Theis and Cooper-Jacob aquifer-test analysis, MODFLOW/MT3DMS modeling with PEST uncertainty, and low-flow geochemical sampling while treating wellbore-storage and skin artifacts, equivalent-porous-medium failure in fractured/karst flow, and non-unique K-S calibration as first-class failure modes. | K-Dense-AI/scientific-agents |
| [hydrologist](https://agentpluginsdirectory.com/plugins/hydrologist) | Reasons from hydrologic-cycle closure (P = ET + Q + ΔS + I), runoff-generation mechanisms, and channel routing through HEC-HMS, HEC-RAS, the National Water Model, and split-sample KGE/NSE/PBIAS calibration, while treating post-flood rating-curve shifts, radar QPE bias, snow/rain misclassification, and non-stationarity in extremes as first-class failure modes. | K-Dense-AI/scientific-agents |
| [ichthyologist](https://agentpluginsdirectory.com/plugins/ichthyologist) | Reasons from meristic fin formulae, sagittal otolith annuli/daily increments, larval flexion staging, and ICZN type discipline through Eschmeyer's Catalog, FishBase/WoRMS, MiFish/12S eDNA with blank controls, FSA/TropFishR/SS3 stock assessment, and Darwin Core museum metadata while treating CPUE catchability, unvalidated otolith ages, larval pigmentation loss, and eDNA false positives as first-class failure modes. | K-Dense-AI/scientific-agents |
| [immunogeneticist](https://agentpluginsdirectory.com/plugins/immunogeneticist) | Reasons from HLA/KIR/FcγR diversity, haplotype LD, and epitope immunogenicity; adjudicates NGS typing, imputation fine-mapping, eplet/TCE matching, and DSA/crossmatch artifacts. | K-Dense-AI/scientific-agents |
| [immunologist](https://agentpluginsdirectory.com/plugins/immunologist) | Reasons from innate/adaptive immunity and MHC presentation; designs flow cytometry (FMO, gating, exhaustion panels), ICS, ELISpot (DFR), tetramer, and multiplex cytokine assays; validates epitopes via IEDB/NetMHCpan; reports per MIATA and MIFlowCyt. | K-Dense-AI/scientific-agents |
| [immunotherapy-scientist](https://agentpluginsdirectory.com/plugins/immunotherapy-scientist) | Reasons from antigen recognition and checkpoint circuits through CAR-T/bispecific design, ASTCT CRS/ICANS grading, flow cytometry release CQAs, iRECIST response assessment, COMPASS/TIDE biomarker modeling, and JACIE/FACT IEC accreditation while treating antigen escape, pseudoprogression, tonic signaling, and step-up CRS as first-class failure modes. | K-Dense-AI/scientific-agents |
| [industrial-ecologist](https://agentpluginsdirectory.com/plugins/industrial-ecologist) | Reasons from mass balance closure, in-use stocks, and system boundaries through STAN (ÖNorm S 2096), dynamic MFA with Weibull lifetime distributions, EEIO tables (EXIOBASE, USEEIO) and pedigree-scored Monte Carlo while treating non-closing residuals, re-export trade hubs, downcounted informal-sector leakage, and Kalundborg-copied symbiosis without quality-spec match as first-class failure modes. | K-Dense-AI/scientific-agents |
| [industrial-engineer](https://agentpluginsdirectory.com/plugins/industrial-engineer) | Reasons from takt, Little's Law, and ρ-stable queueing (M/M/c, Erlang C) through DMAIC/DMADV, VSM, line balancing, SLP/ALDEP/CRAFT layout, MTM/MOST, work sampling, RNLE/RULA/REBA, ISO 22400 OEE, and Arena/AnyLogic DES V&V; treats simulation warm-up/replication gaps, CRAFT non-contiguity, and OEE-without-takt red herrings as first-class failure modes. | K-Dense-AI/scientific-agents |
| [industrial-microbiologist](https://agentpluginsdirectory.com/plugins/industrial-microbiologist) | Reasons from SmF/SSF physiology, fed-batch μ/OTR, OUR/RQ, DoE media optimization, PAT (Raman soft sensors), ICH Q8/Q7 characterization, bioleaching, SVI/F/M filament ID, and phage (10⁴, 10⁶ PFU/mL) plant hygiene; treats antifoam kLa penalty, F₀ CIP/SIP cold spots, DSP mass balance, and golden-batch vs biofilm red herrings as first-class failure modes. | K-Dense-AI/scientific-agents |
| [infectious-disease-specialist](https://agentpluginsdirectory.com/plugins/infectious-disease-specialist) | Reasons from the host-pathogen-antimicrobial triangle, source control, and local resistance through IDSA/CLSI M100 breakpoints, PK/PD targets (vancomycin AUC24 400-600, beta-lactam time-above-MIC), and diagnostics like MALDI-TOF, BioFire panels, and galactomannan while treating colonization-versus-infection, blood-culture contaminants, and noninfectious fever mimics (drug fever, IRIS) as first-class failure modes. | K-Dense-AI/scientific-agents |
| [information-retrieval-scientist](https://agentpluginsdirectory.com/plugins/information-retrieval-scientist) | Reasons from the Probability Ranking Principle, Saracevic's relevance layers, and Cranfield pooling through tuned BM25, SPLADE/ColBERT/dense and LLM reranking, trec_eval/ir_measures with paired topic-level tests, interleaving, and nugget-based RAG evaluation while treating unjudged-as-nonrelevant pools, position-biased clicks, LLM-judge circularity, single-vector embedding limits, and benchmark contamination as first-class failure modes. | K-Dense-AI/scientific-agents |
| [inorganic-chemist](https://agentpluginsdirectory.com/plugins/inorganic-chemist) | Reasons from electron counting, ligand field theory, and HSAB matching through Schlenk/glovebox air-sensitive synthesis, SCXRD/checkCIF validation, Evans/EPR/XANES oxidation-state assignment, and hot-filtration leaching tests while treating paramagnetic NMR overinterpretation, mystery-oil misidentification, and DFT-without-multiplicity claims as first-class failure modes. | K-Dense-AI/scientific-agents |
| [instrumentation-engineer](https://agentpluginsdirectory.com/plugins/instrumentation-engineer) | Reasons from the process variable, its transduction chain, 4-20 mA loop integrity, and traceable calibration through ISA-5.1 P&IDs, NAMUR NE43 fault bands, ISO 5167 orifice sizing, and GUM uncertainty budgets while treating plugged impulse lines, double-applied square-root, ground-loop noise, and unrevalidated SIS bypasses as first-class failure modes. | K-Dense-AI/scientific-agents |
| [integrated-circuit-designer](https://agentpluginsdirectory.com/plugins/integrated-circuit-designer) | Reasons from spec through Virtuoso schematic/layout, Calibre DRC/LVS/RCX, and foundry PDK corners across analog, mixed-signal, RF, and structured digital blocks; treats TT-only signoff, LVS-without-PEX, and common-centroid violations as first-class tapeout failure modes. | K-Dense-AI/scientific-agents |
| [isotope-geochemist](https://agentpluginsdirectory.com/plugins/isotope-geochemist) | Reasons from fractionation theory, decay schemes, reservoir mixing, and closure assumptions through standard-sample bracketing, double-spike deconvolution, isochron/Tera-Wasserburg fitting with MSWD, and ISO Guide uncertainty propagation while treating Pb-blank and lab-air contamination, mass bias, Pb loss and inheritance, and open-system resetting as first-class failure modes. | K-Dense-AI/scientific-agents |
| [knowledge-representation-researcher](https://agentpluginsdirectory.com/plugins/knowledge-representation-researcher) | Reasons from model-theoretic semantics, the expressivity-vs-decidability-vs-scalability tradeoff, and competency questions through OWL 2 profiles, reasoners (HermiT, Pellet, ELK), ROBOT/Protégé pipelines, and SHACL validation while treating unsatisfiable classes, silent OWA-vs-CWA semantic mixing, hallucinated LLM-suggested axioms, and IRI-reuse on bad merges as first-class failure modes. | K-Dense-AI/scientific-agents |
| [landscape-ecologist](https://agentpluginsdirectory.com/plugins/landscape-ecologist) | Reasons from scale-explicit pattern-process feedbacks, configuration over composition, and functional rather than graphical connectivity through FRAGSTATS, landscapemetrics, Circuitscape resistance surfaces, NLMR neutral models, and block cross-validation, while treating MAUP grain artifacts, classification error propagation, isolation-by-distance confounding isolation-by-resistance, and resistance surfaces unvalidated by movement as first-class failure modes. | K-Dense-AI/scientific-agents |
| [laser-physicist](https://agentpluginsdirectory.com/plugins/laser-physicist) | Reasons from population inversion and threshold gain (g ≥ l/L + T), ABCD resonator stability (g₁g₂ in 0 to 1) and c/(2L) mode spacing, GVD/TOD dispersion management and time-bandwidth ΔtΔν ≈ 0.44, and Kerr/SPM nonlinear phase through Kerr-lens/SESAM mode-locking, CPA stretcher, amplifier, compressor with B-integral budgeting, QPM in PPLN/PPLKTP for SHG/OPA, FROG/SPIDER and Dazzler pulse shaping, M² caustics per ISO 11146, and IEC 60825 classification while treating thermal-lens drift out of the stability zone, autocorrelation-width-mistaken-for-pulse-width, uncompensated TOD wings and regen double-pulsing, and LMA-fiber mode instability and SRS/SBS above kW as first-class failure modes. | K-Dense-AI/scientific-agents |
| [life-cycle-assessment-analyst](https://agentpluginsdirectory.com/plugins/life-cycle-assessment-analyst) | Reasons from functional unit, attributional-versus-consequential framing, and ISO 14044 allocation hierarchy through openLCA, SimaPro, Brightway2, ecoinvent, and LCIA methods like TRACI and EF 3.0 while treating allocation-driven ranking flips, biogenic-versus-fossil carbon mistagging, cut-off-masked hotspots, and PCR mismatch as first-class failure modes. | K-Dense-AI/scientific-agents |
| [limnologist](https://agentpluginsdirectory.com/plugins/limnologist) | Reasons from stratification, Schmidt stability, and nutrient, light coupling; profiles with CTD/EXO and Carlson TSI components; models with rLakeAnalyzer and GLM while treating internal P loading, sensor fouling, and spatial pseudoreplication as first-class failure modes. | K-Dense-AI/scientific-agents |
| [logician](https://agentpluginsdirectory.com/plugins/logician) | Reasons from syntax-versus-semantics and object-versus-metalanguage discipline, the ⊢/⊨ distinction, and matching each logic to its intended semantics through natural deduction and sequent calculus (cut-elimination, subformula property), compactness and Löwenheim, Skolem, forcing and diagonalization, and proof assistants (Lean 4/mathlib, Coq, Isabelle/HOL, Agda) with Z3/CVC5/Vampire/Mace4 automation while treating use, mention conflation, second-order misuse of compactness, mis-stated incompleteness hypotheses (ω-consistency vs consistency), and Gödel philosophical overreach as first-class failure modes. | K-Dense-AI/scientific-agents |
| [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 |