LLM Mart Basic
@llm-mart · Joined Jun 2026
Debugging methodology, hypothesis testing, reading stack traces, isolating issues. Use when facing an unexpected bug, a flaky test, a production incident, or any situation where the cause isn't immediately obvious.
Tailwind CSS v4 patterns, component styling, dark mode, responsive design, and design system integration. Use when styling components or reviewing CSS.
TDD red-green-refactor cycle, test structure, mocking patterns for Vitest/Jest. Use when starting a new feature, fixing a bug, or refactoring — write the test first, then the implementation.
TypeScript type system patterns, generics, utility types, and strict mode best practices. Use when writing or reviewing TypeScript code.
Web design best practices, accessibility, responsive layout, color contrast. Use when auditing a UI for a11y compliance, designing responsive layouts, or establishing design standards across a web app.
Playwright E2E patterns, Testing Library component tests, test selectors. Use when writing browser tests, component tests, or setting up an E2E testing pipeline for a Next.js or React app.
Generating Excel files with xlsx/exceljs in Node.js. Use when generating .xlsx reports, data exports, dashboards, or spreadsheets from database data.
Reports on the health and state of architecture documentation (counts of ADRs, reviews, activity levels, documentation gaps). Use when the user asks "What's our architecture status?", "Show architecture documentation", "How many ADRs do we have?", "What decisions are documented?"
Creates a NEW Architectural Decision Record (ADR) documenting a specific architectural decision. Use when the user requests "Create ADR for [topic]", "Document decision about [topic]", "Write ADR for [choice]", or when documenting technology choices, patterns, or architectural ap
Displays the roster of architecture team members with their specialties and expertise areas. Use when the user asks "Who's on the architecture team?", "List architecture members", "Show me the architects", "What specialists are available?", "Who can I ask for reviews?", or wants
Enables and configures Pragmatic Guard Mode (YAGNI Enforcement) to prevent over-engineering. Use when the user requests "Enable pragmatic mode", "Turn on YAGNI enforcement", "Activate simplicity guard", "Challenge complexity", or similar phrases.
Statistical significance calculator for A/B test results with sample size requirements, segment breakdowns, and hypothesis generation. Use when feeding test results, checking statistical significance, calculating sample sizes, analyzing experiment outcomes, or generating next tes
Designs statistically valid split tests for ads, audiences, landing pages, or bid strategies. Calculates required sample sizes before you start, monitors results during the test, and calls winners when statistical significance is actually reached — not when you feel like one is w
Evaluates your campaign and ad set structure against your actual goals and budget. Flags over-segmentation that fragments your data, under-segmentation that hides performance differences, budget allocation issues, and consolidation opportunities that would improve algorithmic del
Analyzes your top performing ads, identifies what's working in the hooks, CTAs, messaging angles, and formats, then generates new variants that follow the same winning patterns while introducing enough variation to test meaningfully. Platform: Google and Meta.
Reviews all your Google Ads extensions — sitelinks, callouts, structured snippets, call extensions, image extensions, price extensions — across every campaign. Flags what's missing, what's underperforming, what's outdated, and writes replacements based on your best performing ads
Multi-channel budget optimization using MER, marginal ROAS, and diminishing returns analysis. Use when pasting multi-channel spend and results data, requesting reallocation recommendations, analyzing budget shift priorities, or optimizing marketing efficiency across Google, Meta,
Audit how visible your brand is inside AI answers (ChatGPT, Claude, Gemini, Perplexity, AI Overviews). Claude builds a prompt panel for your category, scores where you show up vs competitors, and turns the gaps into a prioritized fix list. Platform: AI visibility.
Catches unusual performance changes across your accounts — CPC spikes, CVR drops, spend surges, impression collapses, CTR shifts — and flags them with context about what likely changed. The goal is to catch problems in hours instead of discovering them days later during a routine
Runs your conversion data through different attribution models side by side — last click, first click, linear, time decay, position based, and data-driven. Shows you how credit shifts between campaigns depending on the model so you can make better budget decisions instead of over
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.
/python-scaffold
Python scaffold
Scaffold a Python project (FastAPI, Django, library, or CLI) with uv, type hints, testing, and dev tooling
/approve-review
Approve review
Open a review-action approval window by creating the ./.review-approved flag file. Takes an optional reason string that is recorded in the flag file and an unsigned approval log.
/list-pending
List pending
List the review actions that the review-governance policy blocked in this session. protect-mcp 0.7.4 writes no receipt for a denied call, so the list comes from the session, not from ./review-receipts/.
/compliance-check
Compliance check
Review software compliance controls, regulatory requirements, and audit readiness
/security-dependencies
Security dependencies
Scan dependencies for vulnerabilities and generate supply chain security evidence
/security-hardening
Security hardening
Orchestrate comprehensive security hardening with defense-in-depth strategy across all application layers
/security-sast
Security sast
Static Application Security Testing (SAST) for code vulnerability analysis across multiple languages and frameworks
/setup
Setup
Initialises the ShipMate pipeline in the current project. Creates the stories folder, sets up the pipeline state directory, and runs the initial codebase scan to generate project-doc.md and AGENTS.md.
/ship
Ship
Master pipeline entry point. Routes requirements from a story file through scan → orchestrate → architect → implement → review → QA → playwright stages. Use /ship stories/foo.md to start, /ship status to check progress, /ship resume to continue.
/business-case
Business case
Generate comprehensive investor-ready business case document with market, solution, financials, and strategy
/financial-projections
Financial projections
Create detailed 3-5 year financial model with revenue, costs, cash flow, and scenarios
/market-opportunity
Market opportunity
Generate comprehensive market opportunity analysis with TAM/SAM/SOM calculations
/rust-project
Rust project
Scaffold a Rust project (binary, library, workspace, or Axum web API) with Cargo, testing, and dev tooling
/tdd-cycle
Tdd cycle
Execute a comprehensive TDD workflow with strict red-green-refactor discipline
/tdd-green
Tdd green
Implement minimal code to make failing tests pass in TDD green phase
/tdd-red
Tdd red
Write comprehensive failing tests following TDD red phase principles
/tdd-refactor
Tdd refactor
Refactor code while keeping all tests green in TDD refactor phase
/issue
Issue
Resolve a GitHub issue from triage and root cause analysis through test-driven implementation and a pull request
/standup-notes
Standup notes
Generate async standup notes from git commits, Jira tickets, and Obsidian notes
/accessibility-audit
Accessibility audit
Audit UI code for WCAG compliance
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