---
title: "Other Agent Plugins, page 37 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/37
last-updated: 2026-10-03
---

# Other Agent Plugins, page 37 of 45

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

| Name | Description | Repo |
| --- | --- | --- |
| [clinical-epidemiologist](https://agentpluginsdirectory.com/plugins/clinical-epidemiologist) | Clinical epidemiology expert for causal study design, observational bias control, GRADE/EBM synthesis, and principled reporting (CONSORT/STROBE/PRISMA). | K-Dense-AI/scientific-agents |
| [the-workshop](https://agentpluginsdirectory.com/plugins/the-workshop) | Stop being the switchboard between your AI agents, direct a team. The Workshop puts long-running AI agents (desks) in the same room, on the same work, each with its own memory and history, sharing one workspace so you direct the work instead of relaying it. | rakthainet-DKH/FormTH-copilot |
| [clinical-laboratory-scientist](https://agentpluginsdirectory.com/plugins/clinical-laboratory-scientist) | Reasons from pre-analytical, analytical, post-analytical total testing process; EP15/EP09/EP28 validation, Westgard/Sigma IQC, HIL indices (C56), EP23/IQCP, critical-value read-back, type-and-screen/crossmatch, CLSI M100 direct AST, LC-MS/MS C62, and AUTO10 autoverification. | K-Dense-AI/scientific-agents |
| [tiny-tool-town-submitter](https://agentpluginsdirectory.com/plugins/tiny-tool-town-submitter) | Inspect a repository, improve Tiny Tool Town readiness, submit its listing issue, and launch remediation work. | rakthainet-DKH/FormTH-copilot |
| [clinical-microbiologist](https://agentpluginsdirectory.com/plugins/clinical-microbiologist) | Reasons from blood-culture volume and contamination criteria, staged Gram, ID, AST reporting, MALDI-TOF/VITEK/Phoenix and EUCAST RAST, CLSI M100 vs EUCAST breakpoint discipline, WHONET antibiograms, and NHSN MDRO alerts, treating contaminant vs pathogen and VME/ME as first-class failure modes. | K-Dense-AI/scientific-agents |
| [token-pacman](https://agentpluginsdirectory.com/plugins/token-pacman) | Visualizes live session AI-credit usage as a Pac-Man board with pellets, ghosts, fruit milestones, and game-over limits. | rakthainet-DKH/FormTH-copilot |
| [clinical-pharmacologist](https://agentpluginsdirectory.com/plugins/clinical-pharmacologist) | Reasons from exposure: response, popPK (NONMEM), DDI (ICH M12), TDM/NTI windows, and renal/hepatic/allometric adjustment; aligns dose finding with ICH E4 and FDA clinical pharmacology labeling. | K-Dense-AI/scientific-agents |
| [typescript-mcp-development](https://agentpluginsdirectory.com/plugins/typescript-mcp-development) | Complete toolkit for building Model Context Protocol (MCP) servers in TypeScript/Node.js using the official SDK. Includes instructions for best practices, a prompt for generating servers, and an expert chat mode for guidance. | rakthainet-DKH/FormTH-copilot |
| [clinical-trial-scientist](https://agentpluginsdirectory.com/plugins/clinical-trial-scientist) | Reasons from protocol SAPs, ICH-GCP, randomization/blinding, and CDISC SDTM while treating protocol deviations and immortal time as first-class failure modes. | K-Dense-AI/scientific-agents |
| [typespec-m365-copilot](https://agentpluginsdirectory.com/plugins/typespec-m365-copilot) | Comprehensive collection of prompts, instructions, and resources for building declarative agents and API plugins using TypeSpec for Microsoft 365 Copilot extensibility. | rakthainet-DKH/FormTH-copilot |
| [coastal-engineer](https://agentpluginsdirectory.com/plugins/coastal-engineer) | Reasons from joint-probability surge and waves through CEM/EurOtop runup-overtopping, Van der Meer/Rock Manual armor, CERC, Van Rijn sediment budgets, and CMS/XBeach/ADCIRC, SWAN model selection while treating toe scour, armor breakage, datum mismatch (BFE vs MHHW), and downdrift impacts as first-class failure modes. | K-Dense-AI/scientific-agents |
| [uizze](https://agentpluginsdirectory.com/plugins/uizze--rakthainet-dkh) | Build product-specific interfaces with a focused UIZZE workflow, optional real-screen evidence, and a practical finish check. | rakthainet-DKH/FormTH-copilot |
| [cognitive-neuroscientist](https://agentpluginsdirectory.com/plugins/cognitive-neuroscientist) | Reasons from latent constructs through converging behavior, fMRI/M/EEG, TMS, and lesion evidence; designs factorial and dissociation contrasts, fMRIPrep/GLMsingle/MNE pipelines, MVPA/RSA, and COBIDAS reporting while treating pure insertion, reverse inference, motion confounds, and in-sample decoding as first-class failure modes. | K-Dense-AI/scientific-agents |
| [visual-pr](https://agentpluginsdirectory.com/plugins/visual-pr) | Capture, annotate, and embed screenshots and animated GIF demos in pull request descriptions. Includes Playwright-based UI capture, PIL image annotations, PR embedding workflows for GitHub and Azure DevOps, and screen recording with variable timing. | rakthainet-DKH/FormTH-copilot |
| [cognitive-scientist](https://agentpluginsdirectory.com/plugins/cognitive-scientist) | Reasons from Marr's levels of analysis, latent processes behind RT and accuracy, and strong inference through PsychoPy paradigms, signal-detection d-prime/criterion, sequential-sampling and ACT-R models, and crossed mixed-effects designs while treating speed-accuracy tradeoffs, criterion shifts, item confounds, and underpowered WEIRD samples as first-class failure modes. | K-Dense-AI/scientific-agents |
| [where-was-i](https://agentpluginsdirectory.com/plugins/where-was-i) | Reconstruct your dev context (branch, commits, uncommitted work, PR clues) and trigger a resume prompt to continue quickly. | rakthainet-DKH/FormTH-copilot |
| [colloid-chemist](https://agentpluginsdirectory.com/plugins/colloid-chemist) | Reasons from interfacial thermodynamics, DLVO and non-DLVO forces, zeta-potential, and rheology through orthogonal characterization (DLS, NTA, cryo-TEM, SAXS/SANS S(Q), pendant-drop tensiometry) and accelerated-aging stability tests while treating coalescence, Ostwald ripening, creaming, and flocculation crossing the isoelectric point as first-class failure modes. | K-Dense-AI/scientific-agents |
| [windows-app-storage-inspector-cleanup](https://agentpluginsdirectory.com/plugins/windows-app-storage-inspector-cleanup--rakthainet-dkh) | Inspect Windows application storage, understand local disk usage, and safely move approved cleanup items to the Recycle Bin. | rakthainet-DKH/FormTH-copilot |
| [combinatorialist](https://agentpluginsdirectory.com/plugins/combinatorialist) | Reasons from labelled vs unlabelled enumeration, EGF/OGF and species, bijective and probabilistic proofs, Turán/Ramsey/extremal bounds, and BIBD/OA design parameters through SageMath/GAP/nauty, OEIS, House of Graphs, and Colbourn, Dinitz tables while treating isomorphism double-counting, parity barriers, and OEIS false matches as first-class failure modes. | K-Dense-AI/scientific-agents |
| [work-hub](https://agentpluginsdirectory.com/plugins/work-hub) | Generic cross-repo command center canvas for GitHub Copilot with onboarding, focus planning, repo health, work signals, and session cleanup. | rakthainet-DKH/FormTH-copilot |
| [combustion-engineer](https://agentpluginsdirectory.com/plugins/combustion-engineer) | Reasons from stoichiometry, flame stability, emissions, and CFD-reacted flows while treating blow-off, flashback, and soot formation as first-class failure modes. | K-Dense-AI/scientific-agents |
| [communications-engineer](https://agentpluginsdirectory.com/plugins/communications-engineer) | Reasons from Shannon capacity and matched-filter detection through OFDM/MIMO, 3GPP NR LDPC/polar (TS 38.212), TR 38.901 link budgets, Keysight 89600 VSA EVM, ns-3 SLS, and berconfint Monte Carlo while treating CFO/IQ/phase-noise coupling, pre- vs post-FEC BER, and AWGN-only optimism as first-class failure modes. | K-Dense-AI/scientific-agents |
| [community-ecologist](https://agentpluginsdirectory.com/plugins/community-ecologist) | Reasons from Vellend's four processes and Chesson stabilizing/equalizing coexistence through PERMANOVA/betadisper, betapart turnover, nestedness, Gotelli SIM9/C-score null models, and vegan/entropart/picante pipelines while treating compositional closure, dispersion heterogeneity, and pseudoreplicated quadrats as first-class failure modes. | K-Dense-AI/scientific-agents |
| [comparative-medicine-researcher](https://agentpluginsdirectory.com/plugins/comparative-medicine-researcher) | Reasons from species biology, translational validity, and the 3Rs through model-validity frameworks, IACUC protocols, ARRIVE 2.0 reporting, and FELASA/AALAS health surveillance while treating substrain drift, subclinical colony infection (murine norovirus, pinworm, Mycoplasma), analgesia-pathway confounds, and unstated husbandry variables as first-class failure modes. | K-Dense-AI/scientific-agents |
| [comparative-physiologist](https://agentpluginsdirectory.com/plugins/comparative-physiologist) | Reason from the Krogh principle and oxygen cascade through intermittent-flow respirometry, SMR/BMR/MMR scope, Q10 and heterothermy, hemoglobin P50, allometry and PGLS, while treating chamber drift, activity artifacts, phylogenetic pseudoreplication, and SMR definition mismatch as first-class failure modes. | K-Dense-AI/scientific-agents |
| [composites-engineer](https://agentpluginsdirectory.com/plugins/composites-engineer) | Reasons from CLT/ABD laminate mechanics, Halpin: Tsai micromechanics, and CMH-17/NCAMP allowables through autoclave/OOA/RTM process control, ASTM D30 mechanical qualification, ultrasonic C-scan and CAI damage tolerance while treating fiber waviness, void content, under-cure, and quasi-isotropic strength traps as first-class failure modes. | K-Dense-AI/scientific-agents |
| [computational-chemist](https://agentpluginsdirectory.com/plugins/computational-chemist) | Reasons from Kohn: Sham DFT, def2/D4 functional selection, and conformer ensembles through VASP/Gaussian/ORCA, AMBER/GROMACS MD, CREST/CENSO sampling, ONIOM/QM/MM electrostatic embedding, GMTKN55 validation, and SCF convergence escalation while treating B3LYP/6-31G*, BSSE, link-atom artifacts, and force-field mismatch as first-class failure modes. | K-Dense-AI/scientific-agents |
| [computational-linguist](https://agentpluginsdirectory.com/plugins/computational-linguist) | Reasons from UD/PTB formalisms, validate.py/eval.py (LAS/MLAS/ELAS), and evalb.prm settings through Stanza/UDPipe pipelines, PropBank/FrameNet/AMR/UMR layers, IAA (κ, Krippendorff α), CONDA contamination checks, and ARR reproducibility while treating tokenizer mismatch, oracle inflation, train, test leakage, and guideline drift as first-class failure modes. | K-Dense-AI/scientific-agents |
| [computational-neuroscientist](https://agentpluginsdirectory.com/plugins/computational-neuroscientist) | Reasons from encoding/decoding, GLM/LNP spike-train likelihood, mean-field E-I balance, and neural manifolds through NEST/Brian/NEURON/BMTK, GPFA/LFADS, Brain-Score alignment, and trained-RNN reverse engineering while treating spike-sorting contamination, model non-identifiability, nested-CV leakage, and task-optimization≠mechanism as first-class failure modes. | K-Dense-AI/scientific-agents |
| [computational-physicist](https://agentpluginsdirectory.com/plugins/computational-physicist) | Reasons from governing equations, discretization, and HPC scaling through code/solution verification, DFT, MD, Monte Carlo, and FEM/FVM workflows (VASP, LAMMPS, COMSOL, OpenFOAM, QE). | K-Dense-AI/scientific-agents |
| [computational-scientist](https://agentpluginsdirectory.com/plugins/computational-scientist) | Reasons from Roache code/solution verification and ASME V&V 10/20/40 credibility through MMS/GCI, UQ ensembles, and Snakemake/Nextflow/CWL pipelines with conda-lock/Apptainer provenance while treating environment drift, workflow cache staleness, and validation-vs-calibration conflation as first-class failure modes. | K-Dense-AI/scientific-agents |
| [computational-social-scientist](https://agentpluginsdirectory.com/plugins/computational-social-scientist) | Reasons from social mechanisms, measurement validity, and sampling frames through DAGs, fixed-effects and IV/DiD/RDD designs, ERGM/SAOM network models, and human-audited text classifiers while treating unobserved homophily, network interference and SUTVA violations, platform-driven selection, bot contamination, and digital-skew unrepresentativeness as first-class failure modes. | K-Dense-AI/scientific-agents |
| [computer-architecture-researcher](https://agentpluginsdirectory.com/plugins/computer-architecture-researcher) | Reasons from ISA semantics, AMAT/CPI, MESI coherence, and branch prediction through gem5/SPEC/MLPerf evaluation, Amdahl and roofline discipline, TPU/GPU dataflow accelerators, DVFS/EDP, and Spectre/Meltdown mitigation overhead at ISCA/MICRO/HPCA rigor. | K-Dense-AI/scientific-agents |
| [computer-graphics-researcher](https://agentpluginsdirectory.com/plugins/computer-graphics-researcher) | Reasons from the rendering equation, Monte Carlo bias/variance, sampling theory, and BSDF energy conservation through pbrt-v4/Mitsuba 3 references, MIS/ReSTIR/path guiding, DXR 1.2 real-time stacks, 3DGS and NerfBaselines protocols, FLIP/ColorVideoVDP evaluation, OpenUSD/MaterialX/OpenPBR interchange, and ACES 2/OCIO color while treating unconverged references, unequal-time comparisons, denoiser and temporal-reuse bias, scene- vs display-referred color errors, and white-furnace energy failures as first-class failure modes. | K-Dense-AI/scientific-agents |
| [computer-hardware-engineer](https://agentpluginsdirectory.com/plugins/computer-hardware-engineer) | Reasons from CPI/AMAT and MESI/MOESI coherence through SystemVerilog RTL, PrimeTime/Design Compiler/Innovus signoff, PCIe LTSSM/TLP, SVA formal, and clock-gating power while treating CDC metastability, false-path abuse, X-optimism, and coherency traffic as first-class failure modes. | K-Dense-AI/scientific-agents |
| [computer-scientist](https://agentpluginsdirectory.com/plugins/computer-scientist) | Reasons from computational models, abstraction contracts, invariants, and measurable complexity through CLRS-grade algorithm analysis, impossibility results (FLP, CAP, NP-hardness), property-based and chaos testing, and formal tools (TLA+, Coq, Z3) while treating partial failure, race conditions, label leakage, and abstraction leaks like GC pauses and clock skew as first-class failure modes. | K-Dense-AI/scientific-agents |
| [computer-security-researcher](https://agentpluginsdirectory.com/plugins/computer-security-researcher) | Reasons from explicit threat models and CIA/STRIDE through AFL++/libFuzzer triage, ASan/KASAN oracles, ProVerif/Tamarin proofs, CVE/CWE/CAPEC taxonomies, CyberGym dual-execution benchmarks, Menlo/CVD ethics, and USENIX open-science artifact norms. | K-Dense-AI/scientific-agents |
| [computer-vision-scientist](https://agentpluginsdirectory.com/plugins/computer-vision-scientist) | Reasons from image formation, projective geometry (pinhole intrinsics and extrinsics, epipolar/PnP/bundle adjustment, similarity-scale ambiguity), and COCO/LVIS AP mechanics through DINOv3/SigLIP 2 foundation baselines, RF-DETR/YOLO26/SAM 3 models, COLMAP 4/GLOMAP and VGGT geometry, pycocotools/TrackEval/BOP evaluation, and CVPR reporting and EU AI Act limits while treating train, test and pretraining leakage, AP evaluation-setting gaming, preprocessing mismatches (EXIF, BGR, aliased resizing), label noise, and camera-convention and scale errors as first-class failure modes. | K-Dense-AI/scientific-agents |
| [condensed-matter-physicist](https://agentpluginsdirectory.com/plugins/condensed-matter-physicist) | Reasons from Bloch bands, quasiparticles, and symmetry through ARPES/STM/neutron/transport workflows, VASP/QE/Wannier90/DMFT, and Materials Project/ICSD/MPDS while treating matrix-element artifacts, Mott vs. DFT gaps, pseudogap vs. SC gap, and Planckian bad-metal transport as first-class failure modes. | K-Dense-AI/scientific-agents |
| [connectomics-scientist](https://agentpluginsdirectory.com/plugins/connectomics-scientist) | Reasons from vEM acquisition and petascale alignment through FFN/RoboEM segmentation, FlyWire/neuPrint/MICrONS/H01 graphs, and synapse-level QC while treating split/merge errors, alignment tears, false synapses, and release-version drift as first-class failure modes. | K-Dense-AI/scientific-agents |
| [conservation-biologist](https://agentpluginsdirectory.com/plugins/conservation-biologist) | Reasons from IUCN Red List A: E and Green Status recovery metrics, PVA/Ne, occupancy and distance sampling (unmarked, msocc, RMark), prioritizr/Marxan SCP, Conservation Evidence and ROSES synthesis, counterfactual impact evaluation, METT/SMART PAME, and eDNA false-positive models while treating pseudoreplication, GBIF effort bias, offset baselines, and Red List≠priority conflation as first-class failure modes. | K-Dense-AI/scientific-agents |
| [conservation-scientist](https://agentpluginsdirectory.com/plugins/conservation-scientist) | Reasons from measurable biodiversity change, counterfactual impact, and effective population size through IUCN Red List/Green Status criteria, occupancy and PVA models (unmarked, Vortex), Marxan/prioritizr planning, and BACI/matching designs while treating detection heterogeneity, spatial pseudoreplication, REDD+ leakage, and Ne sample bias as first-class failure modes. | K-Dense-AI/scientific-agents |
| [construction-engineer](https://agentpluginsdirectory.com/plugins/construction-engineer) | Reasons from design intent versus means-and-methods through CPM/P6 and Last Planner scheduling, Revit/Navisworks BIM coordination, IBC Chapter 17 special inspections, ASTM C31/C39 cylinder acceptance, and ACI 318 low-break/core protocols while treating formwork collapse, honeycombing, tolerance stack-up, and schedule logic errors as first-class failure modes. | K-Dense-AI/scientific-agents |
| [control-systems-engineer](https://agentpluginsdirectory.com/plugins/control-systems-engineer) | Reasons from plant dynamics, stability margins, and disturbance-rejection specs through Bode/Nyquist and Routh-Hurwitz analysis, LQR/H-infinity and pole placement, Kalman/EKF observers, RGA pairing, and HIL validation while treating integrator windup, actuator saturation and backlash limit cycles, sensor delay masking phase margin, and estimator divergence as first-class failure modes. | K-Dense-AI/scientific-agents |
| [corrosion-engineer](https://agentpluginsdirectory.com/plugins/corrosion-engineer) | Reasons from electrochemical couples, Pourbaix/galvanic selection, and ISO 15156 sour-service limits through AMPP SP0169/SP0502 CP and ECDA, CO₂ models (NORSOK M-506, OLI), coupon/ER/LPR monitoring, and ASTM G5/G48/G61 qualification while treating IR-masked CIPS, salt-spray overclaim, MIC vs. biocide residual, and MR0175≠fit-for-service as first-class failure modes. | K-Dense-AI/scientific-agents |
| [cosmochemist](https://agentpluginsdirectory.com/plugins/cosmochemist) | Reasons from oxygen three-isotope taxonomy (Δ17O), chondrite, achondrite classification, and presolar grain NanoSIMS through Meteoritical Bulletin curation, Al, Mg and Pb, Pb isochrons, CRE vs formation-age separation, and clean-lab sample prep while treating terrestrial weathering, mount contamination, and breccia mixing as first-class failure modes. | K-Dense-AI/scientific-agents |
| [cosmologist](https://agentpluginsdirectory.com/plugins/cosmologist) | Reasons from Friedmann/ΛCDM, r_s and transfer functions, and multi-probe inference (Planck CMB, DESI BAO, lensing, Pantheon+ SNe) through CAMB/CLASS, Cobaya, and GetDist while treating photo-z, IA coupling, CMB foreground pipelines, H0/S8 tensions, and emulator extrapolation as first-class failure modes. | K-Dense-AI/scientific-agents |
| [critical-care-researcher](https://agentpluginsdirectory.com/plugins/critical-care-researcher) | Reasons from acute physiology trajectories, modular organ dysfunction, and cluster-aware trial design through APACHE/SAPS/SOFA scoring, Berlin/Sepsis-3/KDIGO definitions, MIMIC-IV phenotyping, and target-trial emulation with clone-censor-weighting while treating immortal-time bias, cluster contamination, competing risks from early death, and sepsis-phenotype cohort inflation as first-class failure modes. | K-Dense-AI/scientific-agents |