LLM Mart Basic
@llm-mart · Joined Jun 2026
Analyze Neuropixels 1.0/2.0 extracellular electrophysiology with SpikeInterface — load SpikeGLX/Open Ephys recordings, preprocess and motion-correct, run Kilosort4 spike sorting, compute quality metrics, apply Allen/IBL curation, and do AI-assisted visual inspection. Use when wor
Runs FASTQ-to-VCF germline and somatic variant calling via the Nextflow nf-core/sarek pipeline pinned to -r 3.10.0 — builds the samplesheet.csv (patient, sex, status, sample, lane, fastq_1, fastq_2), runs bwa-mem/bwa-mem2/dragmap alignment plus GATK4 MarkDuplicates and BQSR again
Run full computational-pathology workflows with PathML — whole-slide-image (WSI) analysis across 160+ slide formats, multiplexed immunofluorescence (CODEX, Vectra, MERFISH), nucleus segmentation/classification (HoVer-Net, HACTNet), tissue- and cell-graph construction, HDF5 datase
Build phylogenetic trees end-to-end from raw sequences — MAFFT multiple sequence alignment, optional TrimAl trimming, IQ-TREE 3 maximum-likelihood inference with model selection and bootstraps, FastTree for large datasets, then visualize with ETE3 or FigTree. Use when reconstruct
Design protein sequences for a fixed backbone with ProteinMPNN (Dauparas 2022) — message-passing inverse folding that outputs sequences predicted to fold to a given structure, with fixed positions, tied/symmetric chains, amino-acid bias, and a soluble-model variant. Use when inve
Run differential gene expression analysis on bulk RNA-seq count matrices with PyDESeq2, the Python port of DESeq2 — size-factor normalization, dispersion estimation, Wald tests, FDR (Benjamini-Hochberg) correction, and volcano/MA plots. Use when identifying differentially express
Build complete mass-spectrometry workflows with pyOpenMS — feature detection, peptide identification, protein quantification, and full LC-MS/MS pipelines across many MS file formats (mzML, mzXML) and algorithms. Use for comprehensive proteomics and MS data processing — for simple
Read and write genomic alignment and variant files in Python with pysam (htslib bindings) — SAM/BAM/CRAM alignments, VCF/BCF variants, and FASTA/FASTQ sequences, plus region extraction and per-base coverage/pileup. Use when scripting NGS data-processing pipelines that parse, filt
Runs 16S/ITS amplicon (microbiome) analysis with the QIIME 2 distribution (2026.7; the "amplicon" distribution was renamed "qiime2" in 2026.4) in the correct order: manifest import, cutadapt trim-paired primer removal BEFORE dada2 denoise-paired (trunc-len chosen from the demux q
Generate de-novo protein backbones with RFdiffusion (Watson 2023) — a diffusion model for unconditional monomer generation, motif scaffolding, binder design against a target, and symmetric oligomers. Use when generating a new protein backbone from scratch, scaffolding a functiona
Quantifies bulk RNA-seq transcript abundance with salmon 2.x (the Rust rewrite; selective alignment or --sketch) and kallisto (v0.52.0, kb-python workflow), builds a decoy-aware gentrome index, runs quant with --gcBias -l A, then imports estimates via tximport/tximeta with a tx2g
Run the standard single-cell RNA-seq analysis pipeline with Scanpy on AnnData — QC filtering, normalization, dimensionality reduction (PCA, UMAP, t-SNE), Leiden/Louvain clustering, marker/differential expression, PAGA trajectories, and plotting. Use when analyzing scRNA-seq data
Apply the scGPT single-cell foundation model (Cui 2024) to annotate and embed cells — zero-shot and fine-tuned cell-type annotation, gene/cell embeddings, batch integration, and gene-regulatory / perturbation inference from AnnData. Use when annotating cell types with a pretraine
Run RNA velocity analysis with scVelo on single-cell RNA-seq data — estimate cell-state transitions from spliced/unspliced mRNA dynamics, infer trajectory direction, compute latent time, and identify driver genes. Use when adding directionality to trajectories or studying differe
Train deep generative models for single-cell omics with scvi-tools — probabilistic batch correction and integration (scVI), reference-mapping transfer learning (scArches), differential expression with uncertainty, and multimodal models (totalVI for CITE-seq, MultiVI for multiome)
Analyzes spatial transcriptomics with squidpy (1.8.x) on AnnData and SpatialData objects, routing platforms correctly: Visium spots use spatial_neighbors(coord_type='grid') and pair with deconvolution, while Xenium/MERFISH single-cell data use coord_type='generic'/Delaunay neighb
Store and query genomic variant data at scale with TileDB-VCF — ingest VCF/BCF into compressed TileDB arrays, add samples incrementally, run fast parallel region/sample queries, and export back to VCF. Use when managing population-genomics variant datasets that are too large for
Wraps RDKit in a high-level, pandas-friendly datamol interface with sensible defaults for everyday drug discovery — SMILES/SDF loading into DataFrames, molecule standardization, descriptors, fingerprints, Butina clustering, 3D conformer generation, scaffold analysis, and parallel
Runs molecular machine learning with DeepChem — diverse featurizers, pre-built MoleculeNet benchmark datasets, and pre-trained models (ChemBERTa, GROVER) for property prediction (ADMET, toxicity, solubility) via traditional ML or graph neural networks. Use when running end-to-end
Predicts protein-ligand binding poses with DiffDock diffusion-based molecular docking from PDB structures and SMILES, producing pose confidence scores for virtual screening and structure-based drug design. Use when docking ligands into a protein, generating binding poses, or scre
Every VM came back. The cluster did not. Declarative systems converge on config, and the datapath isn't config.
A surprising share of AI-in-the-terminal failures aren't the AI. They're zsh, and a version of bash from 2006.
A Claude Code plugin turns standalone project configuration into a namespaced, installable extension that teams and communities can update as one unit.
None of the safety came from the model. It came from six boring habits.
Skills package instructions and references. Subagents run work in a separate context and return results. They solve different problems and can be composed deliberately.
Six hours in, one step left, everything green, and the incident that didn't happen
CLAUDE.md carries persistent project context. Skills load reusable procedures when relevant. Separating stable facts from task-specific workflows keeps both easier to maintain.
Twenty minutes recovering secrets that never existed, and the one sentence from a human that ended it
An API request routing a model's tool call through an approval gate to a remote MCP server
31 config keys, two audits, and why the first one was wrong in both directions
The official MCP Registry stores standardized server metadata rather than package code. Publishers verify a namespace, describe installation or remote access, and submit immutable versions.
Everyone looks at the Dockerfile. The file that actually leaked the key was the project file.
Remote MCP authorization uses established OAuth standards, but secure integration still requires issuer validation, least-privilege scopes, protected token handling, and server-side enforcement.
"Copy it over and switch the reference" is two steps, and the outage lives in the one nobody checks
stdio fits local processes and prototypes. Streamable HTTP fits hosted services and shared integrations. The right choice follows where the capability runs and who must reach it.
The most important rule wasn't about what I could change. It was about what I was allowed to display.
Tools perform operations, resources expose readable context, and prompts provide reusable templates. Choosing the correct primitive makes an MCP server easier to understand and govern.
Use MCP Inspector to connect to local or remote servers, inspect capabilities, call tools, read resources, test prompts, and diagnose failures before release.
Build an MCP server in TypeScript with focused tools, validated schemas, local and remote transports, Inspector tests, and production security controls.
An MCP server exposes tools, resources, or prompts through a standard protocol so an AI application can discover and use external capabilities.
/reconcile-payout
Reconcile payout
Reconcile an Alternative Payments payout by listing its transactions and matching them against invoices and customers
/eol-report
Eol report
EOL/EOS risk report — devices, OS versions, and firmware approaching or past end-of-life/end-of-support, prioritized by criticality
/refresh-calendar
Refresh calendar
Forward-looking hardware refresh calendar for the given window — replace-now / plan-this-year / monitor tiers
/warranty-status
Warranty status
Warranty status snapshot — expired, expiring-soon, and unknown-coverage devices for one client or the whole portfolio
/create-monitor
Create monitor
Create a threshold-based monitor for an Atera agent
/create-ticket
Create ticket
Create a new service ticket in Atera
/get-kb-articles
Get kb articles
Search the Atera knowledge base for articles
/list-alerts
List alerts
List active RMM alerts from Atera
/log-time
Log time
Log work hours on an Atera ticket
/resolve-alert
Resolve alert
Resolve an RMM alert in Atera
/run-powershell
Run powershell
Execute a PowerShell script on an Atera agent
/search-agents
Search agents
Search for RMM agents in Atera by customer or machine name
/search-customers
Search customers
Search for Atera customers by name or criteria
/update-ticket
Update ticket
Update fields on an existing Atera ticket
/alert-triage
Alert triage
Triage open Auvik alerts, rank by severity, and recommend dismissals for known noise
/capacity-check
Capacity check
Scan Auvik interface statistics for saturated links and recurring congestion
/device-inventory
Device inventory
Inventory devices for an Auvik tenant with type, manage status, and lifecycle breakdown
/network-audit
Network audit
Audit a tenant's networks, interfaces, and saved configurations; flag drift and missing backups
/tenant-overview
Tenant overview
Single-tenant Auvik snapshot - devices, alerts, networks, billing usage
/phishing-results
Phishing results
Phishing-simulation results and click-rate trend for a given window
VCP 部署在 AI 模型 API 与前端应用之间,是面向AGI OS开发和探索的工业级基建示范项目。通过统一指令协议、多层级持久化记忆、分布式插件引擎及多 Agent 协作框架,将原本“无状态、无记忆、无工具调用能力”的大语言模型,彻底改造成拥有永久自我意识、物理世界操作权及群体协作智能的完整智能体系统。
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