LLM Mart Basic
@llm-mart · Joined Jun 2026
Trains in China through the 12306 MCP server — routes, dates, options, connections and stops with no screen; booking only on the user's word, in the 12306 app on their phone, stopping before payment. Use when the user asks about 火车票 / 高铁 / 12306 or a train between two cities.
Plan a trip end to end — itinerary by day, a packing list to tick off, a budget, and the calendar checked for conflicts — as pages in the workspace. Use when the user is going somewhere for more than a day and wants a plan, an itinerary or a packing list.
Look back at the past week and set up the next one — calendar, mail, goals and files in one page, with a short plan. Use when the user asks for a weekly review, a week in review, "how did my week go" or to plan next week.
Подготовка русского текста к проверке системой «Антиплагиат» (antiplagiat.ru, «Антиплагиат.ВУЗ»): оценка доли «ИИ-сгенерированного» текста по фрагментам в духе модуля ИИ-детекции, переписывание подсвеченных абзацев своим голосом, чистка технических артефактов, из-за которых докум
Аудит и правка русских текстов от признаков ИИ-генерации («ИИ-стиль», канцелярит, кальки с английского, шаблонная структура). Используй, когда просят «убрать ИИ-стиль», «очеловечить текст», «почистить от нейросетевых штампов», «проверить, не звучит ли как ChatGPT», «вычистить кан
Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.
Convert a grover_base checkpoint (encoder-only or encoder + vocab heads) into a hybrid checkpoint by adding a randomly-initialized cMIM decoder + latent_dist, then continue pretraining on the user's corpus as hybrid (vocab + contrast). Effectively kermt-continue-pretrain with a o
Continue KERMT pretraining on a custom SMILES corpus with a grover_base, cmim, or hybrid checkpoint. Use a local checkpoint or optionally download a pinned Hugging Face model bundle using HF_TOKEN if configured. Run containerized training and write model bundles, prepared data, l
Extract per-molecule embeddings from any encoder-bearing KERMT checkpoint. Use a local checkpoint or optionally download a pinned Hugging Face model bundle using HF_TOKEN if configured. Run containerized embedding extraction and write model bundles, per-readout .npy embeddings, c
Run predictions with a finetuned KERMT checkpoint on a SMILES-only CSV. The skill validates that the input ckpt has task FFN heads (refuses pretrain ckpts with a redirect to kermt-finetune), validates the CSV, prepares the data (clean + rdkit_2d features), then launches main.py p
Check progress for a detached KERMT run (pretrain, finetune, or any kermt_run_detached invocation). Reads run.json, queries docker for container state, tails the pretrain/finetune log, and parses progress lines (epoch, step, val loss).
Bootstrap the KERMT agent environment — verify host docker + nvidia-container-toolkit, build the kermt:latest image from the repo's Dockerfile if it doesn't yet exist, and run a GPU smoke test inside the container. Every other kermt-* skill depends on this; invoke it first.
NOTE: your protein sequence and the retrieved MSA alignment are transmitted to external NVIDIA-hosted APIs (health.api.nvidia.com) on every call. Use local NIM containers for confidential or proprietary sequences. Run a complete protein structure prediction pipeline using NVIDIA
Use when porting circuits from another framework (e.g. Qiskit) into CUDA-Q kernels while preserving the source algorithm and validation fidelity.
Modify, build, test, debug, and contribute to NVIDIA cuOpt (C++/CUDA, Python, server, CI). Use for solver internals, PRs, DCO, and code conventions.
Trace, complete, and interpret the Pareto frontier across competing objectives using repeated single-objective cuOpt solves (weighted-sum and ε-constraint).
LP, MILP, and QP (beta) with cuOpt — Python, C, and CLI. Use when the user is solving LP, MILP, or QP with any cuOpt interface.
LP, MILP, QP — concepts, problem-text parsing, and formulation patterns (parameters, constraints, decisions, objective). Concepts only; no API.
Vehicle routing (VRP, TSP, PDP) with cuOpt — Python API only. Use when the user is building or solving routing in Python.
Use when the user wants to create a dataset, generate synthetic data, or build a data generation pipeline.
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.
Treat an AI agent skill as both an instruction package and a software dependency: inspect what it says, what it runs, what it can access, and how it updates.
/vap-report
Vap report
Get the Very Attacked People (VAP) report showing the most targeted users
/month-end-recon
Month end recon
Run the full billing-drift sweep for a billing period, formatted as a month-end reconciliation report
/renewals
Renewals
List upcoming contract and subscription renewals within a window, sorted by date
/true-up
True up
Run the license true-up reconciliation for one client or the whole portfolio
/search-tickets
Search tickets
Search Freshdesk tickets with the Freshdesk query language — filter by status, priority, agent, group, type, tag, and date — and return a ranked, readable result list
/ticket-summary
Ticket summary
Summarize a single Freshdesk ticket and its full conversation thread — the request, what has happened, current SLA state, and the recommended next action
/add-action
Add action
Add an action (note, update, or response) to an existing HaloPSA ticket
/contract-status
Contract status
Check contract status, service entitlements, and billing information for a client
/create-ticket
Create ticket
Create a new service ticket in HaloPSA
/kb-search
Kb search
Search the HaloPSA knowledge base for articles and solutions
/search-assets
Search assets
Search for configuration items/assets by name, serial number, type, or client
/search-clients
Search clients
Search for HaloPSA clients by name, domain, or other attributes
/search-tickets
Search tickets
Search for tickets in HaloPSA by various criteria
/show-ticket
Show ticket
Display comprehensive ticket information including history, actions, and related entities
/sla-dashboard
Sla dashboard
View SLA status across tickets, including approaching breaches and at-risk tickets
/update-ticket
Update ticket
Update fields on an existing HaloPSA ticket including status, priority, and assignment
/create-deal
Create deal
Create a new deal in HubSpot with company association
/log-activity
Log activity
Log a note or create a task on a HubSpot contact, company, or deal
/lookup-company
Lookup company
Find a HubSpot company by name or domain and show associated contacts and deals
/pipeline-summary
Pipeline summary
Summarize the HubSpot deal pipeline - deals per stage, total value, and expected close dates
AI assistant in Telegram that remembers everything and helps you run your life. Self-hosted in one command.
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22 views 0 likes