LLM Mart Basic

Analyze a Figma file via the Figma REST API and generate an interactive design knowledge graph (pages, screens, components, component sets, instances, design tokens) with a kind:"design" dashboard.

Analyzes a codebase's file structure, summaries, and import relationships to identify logical architectural layers and assign every file to exactly one layer.

Analyzes markdown files using pre-parsed structural data and LLM inference to extract knowledge graph nodes and edges (entities, claims, implicit relationships, topic clustering).

Reviews the output of merge-batch-graphs.py for semantic issues the script cannot catch. Recovers dropped nodes/edges and fills cross-batch gaps.

Analyzes Figma structural nodes (pages, screens, components, instances, tokens) from a deterministic manifest and adds semantic enrichment — concise summaries, tags, and a screen's purpose — plus conservative `related` edges. Does NOT invent structural nodes or edges.

Analyzes codebases to extract business domain knowledge — domains, business flows, and process steps. Produces a domain-graph.json that maps how business logic flows through the code.

Analyzes batches of source files to produce knowledge graph nodes and edges. Extracts file structure, functions, classes, and relationships using a two-phase approach: structural extraction script followed by LLM semantic analysis.

Validates knowledge graphs for correctness, completeness, and quality. Runs systematic checks and renders approval or rejection decisions.

Use this agent when users need help understanding, querying, or working with an Understand-Anything knowledge graph. Guides users through graph structure, node/edge relationships, layer architecture, tours, and dashboard usage.

Scans a codebase directory to produce a structured inventory of all project files, detected languages, frameworks, import maps, and estimated complexity.

Designs guided learning tours through codebases, creating 5-15 pedagogical steps that teach project architecture and key concepts in logical order.

Redis Cluster and replication guidance covering hash tags for multi-key operations, avoiding CROSSSLOT errors, and reading from replicas to scale read-heavy workloads. Use when designing keys for a sharded Redis Cluster, debugging CROSSSLOT errors on MGET / SDIFF / pipelines, con

Redis client and connection guidance covering connection pooling, multiplexing, pipelining, client-side caching with RESP3, avoiding slow commands (KEYS, SMEMBERS, HGETALL), and tuning socket timeouts. Use when configuring a Redis client (redis-py, Jedis, Lettuce, NRedisStack), b

Core Redis modeling guidance — choose the right data structure (String, Hash, List, Set, Sorted Set, JSON, Stream, Vector Set) and use consistent colon-separated key names. Use when designing a Redis data model, caching objects, deciding between Hash and JSON, building counters,

Redis observability guidance — which metrics to monitor (memory, connections, hit ratio, ops/sec, rejected connections), which built-in commands to reach for during incident triage (SLOWLOG, INFO, MEMORY DOCTOR, CLIENT LIST, FT.PROFILE), and when to use the Redis Insight GUI. Use

Redis Search guidance covering FT.CREATE schema design, field type selection (TEXT, TAG, NUMERIC, GEO, GEOSHAPE, VECTOR, JSON path), DIALECT 2 query syntax, FT.SEARCH / FT.AGGREGATE / FT.HYBRID command selection, vector similarity with HNSW or FLAT, hybrid retrieval combining lex

Redis security guidance covering authentication (requirepass and ACL users), TLS, ACL-based least-privilege access control, restricting network exposure via bind and protected-mode, firewall rules, and disabling dangerous commands. Use when deploying Redis to production, defining

Redis Cluster and replication guidance covering hash tags for multi-key operations, avoiding CROSSSLOT errors, and reading from replicas to scale read-heavy workloads. Use when designing keys for a sharded Redis Cluster, debugging CROSSSLOT errors on MGET / SDIFF / pipelines, con

Redis client and connection guidance covering connection pooling, multiplexing, pipelining, client-side caching with RESP3, avoiding slow commands (KEYS, SMEMBERS, HGETALL), and tuning socket timeouts. Use when configuring a Redis client (redis-py, Jedis, Lettuce, NRedisStack), b

Core Redis modeling guidance — choose the right data structure (String, Hash, List, Set, Sorted Set, JSON, Stream, Vector Set) and use consistent colon-separated key names. Use when designing a Redis data model, caching objects, deciding between Hash and JSON, building counters,

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.

Consolidate MEMORY.md and Project Learnings: dedupe, resolve conflicts, prune

Explain Solana code with a diagram and a step-by-step walkthrough

Generate a typed client from an Anchor or Shank IDL (Codama or Anchor TS)

Migrate TypeScript from @solana/web3.js 1.x to @solana/kit

Plan a Solana feature before coding: accounts, PDAs, instructions, risks, tests

First-time-user product review: scorecard and fix roadmap; --harsh for a roast

Measure compute units per instruction and flag the expensive ones

Format, lint and commit with a conventional message on a kit-named branch

Resync external skill submodules to latest upstream versions

Scaffold a Solana project (Anchor, fullstack, frontend, Pinocchio) with the kit

Set up GitHub Actions CI for Solana programs: lint, build, test, audit

Configure MCP server API keys in .env and add the optional MCP servers

Run tests, auto-fix fmt and lint, and fix failures until green or stuck

Run C# tests: Unity Test Framework in batchmode, or dotnet test

Run Rust tests for programs (LiteSVM, Mollusk, Surfpool, Trident) and backends

Run TypeScript tests for programs (Anchor TS, Kit) and dApp frontends

Update solana-ai-kit to latest version from upstream

Write docs for a Solana program, SDK or component from its code and IDL

Seventy-two slash commands for Claude Code, grouped by what you are doing.

Find missing aliases

Make any song you can imagine

Leading AI-powered video generation platform that specializes in creating hyper-realistic talking avatars

Hermes Agent is an open-source, self-improving autonomous AI agent developed by Nous Research

Kilo Code is a popular, open-source AI coding agent and "agentic engineering" platform designed to help developers build, refactor, and debug software faster

General-purpose agent in one static Go binary. ReAct loop, ACP server for IDEs, OpenAI-compatible REST API with embedded web UI, Telegram gateway, cron schedule…

Autonomous agent framework with structured memory, safety hooks, and loop management. Built by the agent that runs on it.

TSP自托管、零运维的 A 股「选股 + 监控 + 回测」量化工作台 | 基于 TickFlow 数据源 | LLM能力驱使策略定制+个股分析+复盘 | 自由接入第三方数据源与个性化扩展数据 | 个人开源 ,非TickFlow官方项目

Curated, verified Agent Skills powered by ModelStudio.

Run Claude Code, Codex, Antigravity, Cursor Agent and OpenCode as one runtime — persistent sessions, multi-agent councils, an OpenAI-compatible endpoint, an MCP…

pi had nothing (nothing), so I made something (something) — sorry mariozechner-senpai, I went ahead and lovingly soiled your pure pi for you. opinionated fork o…

A persistent workspace for development work that self-improves and continues beyond one session.

Open-source memory and context for user-aware agents: scoped memory, provenance, retrieval quality, correction, boundaries, evals, and MCP/HTTP access.

📚 A zero-dependency, git-backed micro-lesson library for AI Agents to asynchronously share and search verified debugging experience. Python stdlib only. | http…

Deterministic, local-first memory and guardrails for AI coding agents with no LLM in the hot path.

Deterministic spec-orchestration for local LLMs in the pi coding agent — drives prompts through refine→research→grill→compose→critique, with bundled web/docs/fe…

Native Safari browser automation for AI agents. 97 tools via AppleScript — zero overhead, keeps logins, runs silently in background. Drop-in alternative to Chro…

Agent OS: keep specialist agents in a hub, spin up a temporary orchestrator per task. Local-first, works with any model.

Git for agent memory. Branches, diffs, PRs, and rollback for what your agents know.

Multi-Provider AI Gateway - No personal logs by design. Model autodiscovery, Failover groups, High availability, Android companion app, and more - "Because we h…

Production-grade MCP server for MikroTik RouterOS with secure AI-native network automation.