LLM Mart Basic
@llm-mart · Joined Jun 2026
Uses 1M context window to ingest an entire codebase and output a file-by-file migration plan. Supports JS to TS, React class to hooks, framework migrations, and more. Generates migration-plan.md with file inventory, dependency graph, migration order, file-by-file changes, estimat
Uses MCP Connectors to read Gmail inbound leads, score them by ICP fit, draft personalized responses, and log qualified leads to your CRM. Turns your inbox into an automated pipeline.
Create Gentle AI pull requests with issue-first checks. Trigger: creating, opening, or preparing PRs for review.
Trigger: PRs over 400 lines, stacked PRs, review slices. Split oversized changes into chained PRs that protect review focus.
Design docs that reduce cognitive load. Trigger: writing guides, READMEs, RFCs, onboarding, architecture, or review-facing docs.
Write warm, direct collaboration comments. Trigger: PR feedback, issue replies, reviews, Slack messages, or GitHub comments.
Use Gentle AI harness discipline for Pi work: clarify first, preserve OpenSpec artifacts, use strict TDD where available, delegate through subagents when useful, and protect review workload.
Create and triage GitHub issues from repository evidence. Trigger: issue creation, bug reports, feature requests, or issue approval.
Trigger: judgment day, judgement day, dual review, adversarial review, juzgar. Run explicit blind dual review with at most two scoped fix/re-judgment rounds.
Trigger: RDD, receipt-driven development, review authority, receipt/lineage, correction/recovery, delivery gate/kill switch, bounded review defects. Guide work.
Trigger: /skill-creation, skill creation, skill creator, create skill, new skill. Create LLM-first skills with valid frontmatter.
Trigger: improve skills, audit skills, refactor skills, skill quality. Audit and upgrade existing LLM-first skills.
Trigger: update skills, skill registry, actualizar skills, after skill changes. Index available skills by trigger and path.
Plan commits as reviewable work units. Trigger: implementation, commit splitting, chained PRs, or keeping tests and docs with code.
AI-generated documentation for GitHub repositories
Creates and removes isolated Git worktrees under a consistent repository sibling directory. Use when starting feature or fix work in a dedicated worktree, or cleaning up a finished worktree.
A JupyterLab extension supporting Claude Code, Copilot, Ollama, and OpenAI-compatible LLMs, with MCP, skills, plugins, and notebook agents.
Use when adding A2UI rendering to any AG-UI-supported framework or custom AG-UI application, scaffolding an AG-UI app that should render A2UI, adapting an AG-UI integration to emit A2UI surfaces, or wiring the AG-UI A2UI middleware/toolkit with a compatible renderer.
Core agent-browser usage guide. Read this before running any agent-browser commands. Covers the snapshot-and-ref workflow, navigating pages, interacting with elements (click, fill, type, select), extracting text and data, taking screenshots, managing tabs, handling forms and auth
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a websit
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.
/group-chat
Group chat
Create a multi-agent group chat with AG2 using configurable speaker selection patterns
/new-a2a-agent
New a2a agent
Scaffold an A2A-compliant AG2 agent with server wiring, card settings, and skill definitions
/new-agent
New agent
Scaffold a new AG2 ConversableAgent with tool functions, system prompt, and LLM config
/new-tool
New tool
Create a tool function for an AG2 agent with type annotations, docstrings, and JSON return contracts
/sequential-workflow
Sequential workflow
Create a sequential multi-agent pipeline where each agent processes and passes results to the next
/workflow-from-spec
Workflow from spec
Design a complete multi-agent workflow from a natural language description, selecting the right orchestration pattern
/act
Act
Follow RED-GREEN-REFACTOR cycle approach for test-driven development
/add-authentication-system
Add authentication system
Implement secure user authentication system
/add-changelog
Add changelog
Generate and maintain project changelog
/add-mutation-testing
Add mutation testing
Setup mutation testing for code quality
/add-package
Add package
Add and configure new project dependencies
/add-performance-monitoring
Add performance monitoring
Setup application performance monitoring
/add-property-based-testing
Add property based testing
Implement property-based testing framework
/add-to-changelog
Add to changelog
Add a new entry to the project's CHANGELOG.md file following Keep a Changelog format
/agent-preflight
Agent preflight
Preflight a repo before AI agents change files
/all-tools
All tools
Display all available development tools
/architecture-review
Architecture review
Review and improve system architecture
/architecture-scenario-explorer
Architecture scenario explorer
Explore architectural decisions through systematic scenario analysis with trade-off evaluation and future-proofing assessment.
/bidirectional-sync
Bidirectional sync
Enable bidirectional GitHub-Linear synchronization
/big-features-interview
Big features interview
Interview to flesh out a plan/spec
Atom Agent, Open-Source Governed AI Agent Platform for Self-Hosted Automation
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