LLM Mart Basic
@llm-mart · Joined Jun 2026
Use apko to build small OCI images from declarative package manifests when supply-chain clarity and minimal contents matter more than a conventional Dockerfile flow.
Use DeepAgents to build long-running task agents with a batteries-included harness for planning, tools, and workflow structure.
Extract structured intelligence from audio using the AssemblyAI API with sentiment analysis, entity detection, topic modeling, and auto-chapter generation. Uses the assemblyai Python SDK for transcript processing pipelines.
Summarizes audio content using AssemblyAI's LeMUR (Large Language Model for Audio Understanding) API. Chains the /v2/transcript endpoint with /lemur/v3/generate/summary for contextual audio intelligence.
Streams audio from Twilio Media Streams over WebSocket to AssemblyAI real-time transcription, extracting speaker-diarized transcripts with word-level timestamps. Triggers entity detection and sentiment analysis via AssemblyAI LeMUR on completed call segments. Results are pushed t
Transcribes audio and generates auto-chapters with summaries using AssemblyAI's /v2/transcript endpoint with auto_chapters=true. Extracts key topics, sentiment analysis, and content safety labels via AssemblyAI SDK.
Use ast-grep (sg) to search, lint, and rewrite code across large codebases using AST pattern matching. A blazing-fast alternative to regex-based code transformations that understands syntax structure.
Astro is a modern web framework for building content-driven websites. It ships zero JavaScript by default, supports multiple UI frameworks (React, Vue, Svelte, Solid), and provides islands architecture for optimal performance.
Generates event-driven architecture documentation from AsyncAPI 3.0 specifications. Uses the AsyncAPI parser-js library to extract channels, message schemas, and server bindings for Kafka and RabbitMQ.
Parses AsyncAPI 2.x/3.x specifications to generate event-driven architecture catalogs using the AsyncAPI Generator. Produces channel documentation, message schema validators, and EventBridge rule templates.
mcp-atlassian is a Model Context Protocol server that connects AI assistants to Atlassian Jira and Confluence. It enables searching and managing Jira issues, reading and editing Confluence pages, and performing project management tasks through natural language via any MCP-compati
The Atlassian Rovo MCP Server bridges your Atlassian Cloud workspace with any MCP-compatible client. Search and summarize Jira issues, create tickets from natural language, update Confluence pages, and query Compass services.
Atuin replaces your existing shell history with a SQLite database that records additional context like exit codes, session IDs, working directories, and command durations. It provides encrypted cross-machine sync and a full-screen fuzzy search UI bound to Ctrl-R.
Identifies audio content using Chromaprint/AcoustID fingerprinting, Shazam API recognition, and ACRCloud monitoring. Matches music, speech, and ambient audio against fingerprint databases.
Separates audio tracks into individual stems (vocals, drums, bass, other) using Meta's Demucs neural network model via the demucs Python package. Supports batch processing of WAV and MP3 files, outputs isolated stems in FLAC or WAV format, and integrates with FFmpeg for format co
Integrate Audiobookshelf's self-hosted audiobook and podcast server into AI agent workflows. Agents can manage libraries, track listening progress, search metadata, and automate podcast episode downloads through the comprehensive REST API.
audioFlux is a deep learning tool library for audio and music analysis and feature extraction, supporting dozens of time-frequency transforms and hundreds of feature combinations for classification, separation, MIR, and ASR tasks.
audiowaveform is a BBC open-source C++ CLI tool that generates waveform data from MP3, WAV, FLAC, Ogg Vorbis, and Opus audio files. It outputs binary or JSON waveform data and renders PNG waveform images at configurable zoom levels.
Use GEO Optimizer from the CLI or MCP so agents can audit a site for AI search visibility, generate llms.txt/schema fixes, check bot access, and track citation readiness.
Use HarnessKit to inventory, audit, enable, disable, and deploy skills, MCP servers, plugins, hooks, CLIs, configs, memory, and rules across multiple coding agents.
Fourteen posts of being wrong in production, compressed to checkboxes
Healthy nodes, a quiet network, 300 restarts in three days, and a latency budget measured in milliseconds
Discovery worked. Ping worked. Every TCP connection timed out, and later the tunnel only worked when someone had a terminal open.
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.
/requirements
Requirements
Generate requirements from goal and research
/research
Research
Run or re-run research phase for current spec
/start
Start
Smart entry point that detects if you need a new spec or should resume existing
/status
Status
Show all specs and their current status
/switch
Switch
Switch active spec
/tasks
Tasks
Generate implementation tasks from design
/triage
Triage
Decompose a large feature into multiple dependency-aware specs (epic triage)
/tree-ring-update
Tree ring update
Check for or install a verified Tree Ring Memory CLI update without changing installation scope
/README
README
This directory contains the command implementations for the fast-agent CLI.
/close
Close
They operate it without you.
/outcome
Outcome
Promised, measured, accepted. A number nobody signed is claimed, not delivered.
/prep
Prep
Prepare the meeting. One page from the record.
/receipts
Receipts
Find the receipt. A dated line, or it did not happen.
/trust
Trust
Diagnose trust. Process gap, or they stopped trusting you.
/awesome-docs
awesome-docs
Generate, convert, and maintain animated GitHub-safe Markdown documents with animated SVG diagrams. Covers four SVG patterns (architecture flow, lifecycle loop, field carousel, timeline phases), guided interview for any doc type (README, architecture guide, runbook, API reference, tutorial, RFC, post-mortem, how-it-works, or custom), converting existing plain Markdown, diffing for stale diagrams, quality auditing, local preview, and multi-platform export. Use when asked to "create a README for X", "write an architecture doc", "animate this guide", "convert my doc to animated", "check if my diagrams are stale", or "export my doc for Confluence".
/aws-profile
aws-profile
AWS profile management for MCP servers — discover profiles across SSO, Granted, and assumed-role chains, check credential TTL, switch profiles across VS Code and Claude Code MCP configs, and scan AWS Organization accounts.
/aws
aws
Structured guidance for AWS CloudFront distributions, WAF web ACLs, Lambda@Edge, CloudFront Functions, Firewall Manager multi-account enforcement, and IAM/IRSA patterns. Covers OAC, cache policies, security headers, managed rule groups, rate limiting, FMS FIRST/MIDDLE/LAST ownership model, and production-ready Terraform generation.
/azure
azure
Azure identity (Workload Identity, OIDC, Entra ID), resource tagging, AKS platform patterns, RBAC scoping, and production-readiness review — with Terraform generation.
/chaos
chaos
Design, run, and debug Chaos Engineering experiments on Kubernetes using Litmus Chaos v3 and Chaos Mesh v2. Covers fault injection (pod-delete, network-loss, CPU stress, node-drain), steady-state hypothesis probes, GameDay runbooks, scheduled experiments, DORA feedback loop, and RBAC setup. Use when asked to "inject a pod fault", "run a GameDay", "schedule chaos experiments", or "debug why my ChaosEngine is stuck".
/checkov
checkov
Bootstrap Checkov on a developer laptop, run static or plan-level Terraform security scanning for AWS/Azure/GCP/EKS, resolve private GitHub modules via gh CLI, generate pre-commit hooks, produce multi-format output (cli/json/sarif/junit), and fix violations with AI-generated patches. Use when asked to "scan my Terraform", "run checkov", "check my IaC for security issues", "set up checkov pre-commit", or "fix checkov findings".
An open-source, privacy-first, self-hosted knowledge workspace where humans and AI agents work together 开源、隐私优先、自托管的知识工作空间,让人与智能体在此协作
14 views 0 likesScale computer-use 2.0 with open-source drivers, cross-OS fleets, and benchmarks for training, evaluation, and data generation.
17 views 0 likesThe go-to web for your AI coding agent — local-first search, fetch, crawl & research over MCP. No API keys, no cloud, $0/query. Public beta.
19 views 0 likesTransform and optimize your markdown documentation for Large Language Models (LLMs) and RAG systems. Generate llms.txt automatically.
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27 views 0 likesThis is a fork of the https://dockbox.dev project I made
25 views 0 likes🏆 Curated, ranked list of AI agent harnesses (100+) — plus an MCP server, llms.txt & JSON so agents can recommend them too. Rescored weekly.
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14 views 0 likes⌥ Coding agent with the IDE wired in
17 views 0 likesSupercharge AI Agents, Safely
33 views 0 likesX (Twitter) Scraper API and X API Alternative. You do not need an official X developer account. You do not need to connect or use an X account for supported scr…
16 views 0 likesMy AI Stand. Realtime by day, rewriting itself by night. Summon my AI superpower.
14 views 0 likesOpen-source coding agent for your terminal, built in Rust and on a journey of continuous community improvement. Issues and PRs welcome.
15 views 0 likes观澜 / Guanlan:AI Agent 的中文互联网研究、阅读与信源路由工具。
12 views 0 likesMac Agent for macOS 26: the agentic AI harness for your Mac Desktop. Computer use, automation, scripting, coding, and more. Powered by 18+ providers across loca…
15 views 0 likesSemantic version control => entity-level diffs, blame, and impact analysis on top of git. 28 languages via tree-sitter. Built for coding agents.
28 views 0 likes