---
title: "Other Agent Plugins, page 70 of 75"
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/70
last-updated: 2026-10-21
---

# Other Agent Plugins, page 70 of 75

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

| Name | Description | Repo |
| --- | --- | --- |
| [veed](https://agentpluginsdirectory.com/plugins/veed) | Connect to VEED through its official MCP server. | manikanta-kops/kipster-plugins |
| [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 |
| [vimeo](https://agentpluginsdirectory.com/plugins/vimeo--manikanta-kops) | Connect to Vimeo through its official MCP server. | manikanta-kops/kipster-plugins |
| [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 |
| [vista-social](https://agentpluginsdirectory.com/plugins/vista-social) | Connect to Vista Social through its official MCP server. | manikanta-kops/kipster-plugins |
| [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 |
| [wallet-by-budgetbakers](https://agentpluginsdirectory.com/plugins/wallet-by-budgetbakers) | Connect to Wallet by BudgetBakers through its official MCP server. | manikanta-kops/kipster-plugins |
| [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 |
| [webflow](https://agentpluginsdirectory.com/plugins/webflow--manikanta-kops) | Connect to Webflow through its official MCP server. | manikanta-kops/kipster-plugins |
| [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 |
| [webull](https://agentpluginsdirectory.com/plugins/webull--manikanta-kops) | Connect to Webull through its official MCP server. | manikanta-kops/kipster-plugins |
| [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 |
| [whimsical](https://agentpluginsdirectory.com/plugins/whimsical--manikanta-kops) | Connect to Whimsical through its official MCP server. | manikanta-kops/kipster-plugins |
| [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 |
| [wispr-flow](https://agentpluginsdirectory.com/plugins/wispr-flow--manikanta-kops) | Connect to Wispr Flow through its official MCP server. | manikanta-kops/kipster-plugins |
| [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 |
| [wix](https://agentpluginsdirectory.com/plugins/wix--manikanta-kops) | Connect to Wix through its official MCP server. | manikanta-kops/kipster-plugins |
| [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 |
| [wordpress](https://agentpluginsdirectory.com/plugins/wordpress) | Connect to WordPress through its official MCP server. | manikanta-kops/kipster-plugins |
| [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 |
| [zoominfo](https://agentpluginsdirectory.com/plugins/zoominfo) | Connect to ZoomInfo through its official MCP server. | manikanta-kops/kipster-plugins |
| [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 |
| [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 |