LLM Mart Basic
@llm-mart · Joined Jun 2026
Detects conflicts across multiple Design Docs and provides structured reports. Use when multiple Design Docs exist, or when "consistency/conflict/sync/between documents" is mentioned. Focuses on detection and reporting only, no modifications.
Reviews one document or one ADR batch against governing requirements, repository evidence, and the needs of its next consumer. Use before user approval or when document consistency and completeness need verification.
Verifies changed integration and E2E tests against skeletons, proof obligations, or explicit prompt claims. Use PROACTIVELY after test implementation completes, or when "test review/skeleton verification" is mentioned. Returns quality reports with failing items and fix instructio
Comprehensively collects problem-related information and creates evidence matrix. Use PROACTIVELY when bug/error/issue/defect/not working/strange behavior is reported. Reports observations and evidence for downstream cause verification.
Creates PRD and structures business requirements. Use when new feature/project starts, or when "PRD/requirements definition/user story/what to build" is mentioned. Defines user value and success metrics.
Specialized agent for verifying React projects and fixing frontend quality failures within the current task scope. Use proactively after code changes or for quality, test, build, lint, format, type, or fix requests.
Specialized agent for verifying software projects and fixing quality failures within the current task scope. Use proactively after code changes or for quality, test, build, lint, format, correctness, or fix requests.
Collects compact scope and cost evidence for requirement confirmation while the user and orchestrator retain requirements, Structural Scale, and document-routing decisions. Use when new requirements, scope, or implementation extent must be confirmed.
Discovers functional scope from existing codebase for reverse documentation. Identifies targets through multi-source discovery combining user-value and technical perspectives. Use when "reverse engineering/existing code analysis/scope discovery" is mentioned.
Reviews implementation for security compliance against an authoritative Design Doc or Work Plan. Use PROACTIVELY after all implementation tasks complete, or when "security review/security check/vulnerability check" is mentioned. Returns structured findings with risk classificatio
Derives multiple solutions for verified causes and analyzes tradeoffs. Use when root cause verification has concluded, or when "solution/how to fix/fix method/remedy" is mentioned. Focuses on solutions from given conclusions without investigation.
Converts an approved Work Plan into the fewest executable implementation task files. Use when work plans are approved and task materialization is needed.
Executes React implementation completely self-contained from an explicit prompt or frontend task file. Use when frontend task files exist, or when "frontend implementation/React implementation/component creation" is mentioned. Asks no questions, executes consistently from investi
Executes implementation completely self-contained from an explicit prompt or task file. Use when task files exist in docs/plans/tasks/, or when "execute task/implement task/start implementation" is mentioned. Asks no questions, executes consistently from investigation to implemen
Creates a scoped frontend ADR batch or one Design Doc from confirmed UI requirements and decision-relevant repository evidence. Use when frontend technical choices or implementation design need an approved artifact.
Creates a scoped ADR batch or one backend/general Design Doc from confirmed requirements and decision-relevant repository evidence. Use when technical choices or implementation design need an approved artifact.
Gathers decision-relevant UI facts from recorded external resources and the existing codebase. Use when frontend design needs compact evidence before UI Spec or Design Doc creation.
Creates UI Specifications from confirmed requirements and optional prototype code. Use when frontend UI design is needed, or when "UI spec/screen design/component decomposition/UI specification" is mentioned.
Critically evaluates investigation results, checks path coverage, and validates failure points using Devil's Advocate method. Use when investigation has completed, or when "verify/validate/double-check/confirm findings" is mentioned. Focuses on verification and conclusion derivat
Creates implementation-focused work plans from approved Design Docs. Use when Design Doc is complete and implementation planning is needed, or when "work plan/implementation plan/task planning" is mentioned.
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.
/rollback-deploy
Rollback deploy
Rollback deployment to previous version
/rsi
Rsi
Read project commands and documentation to optimize AI-assisted development process
/run-ci
Run ci
Run CI checks and fix any errors until all tests pass
/security-audit
Security audit
Perform comprehensive security assessment
/security-hardening
Security hardening
Harden application security configuration
/session-learning-capture
Session learning capture
Capture and document session learnings
/setup-automated-releases
Setup automated releases
Setup automated release workflows
/setup-cdn-optimization
Setup cdn optimization
Configure CDN for optimal delivery
/setup-comprehensive-testing
Setup comprehensive testing
Setup complete testing infrastructure
/setup-development-environment
Setup development environment
Setup complete development environment
/setup-formatting
Setup formatting
Configure code formatting tools
/setup-kubernetes-deployment
Setup kubernetes deployment
Configure Kubernetes deployment manifests
/setup-linting
Setup linting
Setup code linting and quality tools
/setup-load-testing
Setup load testing
Configure load and performance testing
/setup-monitoring-observability
Setup monitoring observability
Setup monitoring and observability tools
/setup-monorepo
Setup monorepo
Configure monorepo project structure
/setup-rate-limiting
Setup rate limiting
Implement API rate limiting
/setup-visual-testing
Setup visual testing
Setup visual regression testing
/share-your-story
Share your story
Open the Build with Claude contribution guide for writing a community story
/simulation-calibrator
Simulation calibrator
Test and refine simulation accuracy with validation loops, bias detection, and continuous improvement frameworks.
Ultra-lightweight, open-source, self-hosted personal AI agent framework in Python with WebUI, tools, memory, MCP, multi-agent workflows, automation, and chat ap…
29 views 0 likesGovernance framework for AI coding agents. It runs them through a five-step workflow (plan, build, review, test, ship) where no step counts as done without evid…
17 views 0 likesUltimate Multi-Agent OS for Autonomous AI NPCs 2026
15 views 0 likesPersonal AI Agent Hub 2026 — Build Your 24/7 Autonomous Assistant
26 views 0 likesProven 2026 Multi-Agent AI Review System – Verdict-Driven Quality Control
29 views 0 likesSlash API Batch: Cut AI Costs by 50% in 2026
16 views 0 likesWeb dashboard for Hermes Agent — multi-platform AI chat, session management, scheduled jobs, usage analytics
19 views 0 likesAgent Skills for Solopreneurs
31 views 0 likesAirLLM dramatically reduces inference memory usage, letting 70B large language models run on a single 4GB GPU card
136 views 0 likesZero, your trustworthy AI teammate for real work.
16 views 0 likes一套 DSH runtime,Desktop、Web 与 TUI 三种开发体验。
12 views 0 likesOpen-source operational advisor for ClickHouse — real-time monitoring plus AI-driven index/partition/materialized-view recommendations.
17 views 0 likes⚙️ TypeScript Style Guide and Agent Skill. A concise set of conventions and best practices for consistent, maintainable code.
28 views 0 likesFramework for AI agents to build and maintain a digital brain through Obsidian wiki
17 views 0 likesApache Maka (Incubating) is a local-first AI agent workspace. Model messages, tool calls, tool results, permission decisions, and termination events are recorde…
25 views 0 likesNeo.mjs is a self-evolving software organism: a professional end-to-end AI engineering team whose cross-model swarm inhabits live apps via Neural Link, Active H…
25 views 0 likesAgentic development harness for Claude Code — SPEC-driven plan/run/sync, TRUST 5 quality gates, model+effort routing, and Claude×GLM multi-LLM cost control. Sin…
19 views 0 likesNocoBase is an open-source AI + no-code platform for building business systems fast. Instead of generating everything from scratch, AI works on top of productio…
27 views 0 likesAn open-source AI coding agent that lives in your terminal.
29 views 0 likesPawWork — free, open-source desktop AI agent for macOS and Windows. Alternative to Codex App and Claude Cowork. BYOK with 75+ providers, ChatGPT OAuth, local mo…
16 views 0 likes