LLM Mart Basic

Author and validate Terraform / OpenTofu IaC with plan-before-apply discipline. Use for infrastructure changes and reviews.

Generate and run unit, API, and E2E tests (including Playwright) that match project conventions. Use when adding coverage or reproducing a bug with a failing test first.

Map the attack surface and build a STRIDE threat model - trust boundaries, data flows, entry points, assets, and prioritized threats with mitigations. Use for /threatmap, architecture security reviews, or "what could go wrong?" planning.

Remote-control tmux sessions for interactive CLIs by sending keystrokes and scraping pane output.

Verify and install missing CLI tools on demand - scanners, linters, runtimes - without root, using uvx, npx, or static binaries in ~/.local/bin. Use at the start of any audit, scan, or build when a needed tool may be absent.

Run the Navin Trading Agent OS. Use when the user wants a programmable investment agent, paper portfolio, market scan, debate, risk limits, backtest, or /trading.

Translate and localize between French, English, and Arabic - adapting idioms, register, formats, and cultural context, not just words. Use for any cross-language content.

Default web UI/UX design intelligence for Navin. Design-system generator (84 styles, 192 palettes, typography, landing patterns, UX rules) plus mandatory framer-motion for all web sites. Use when building, designing, scaffolding, or reviewing any website, landing page, dashboard,

One-time setup wizard for the navin upgrade skill. Triggers: setup update, configure update, 切设置更新, 初始化更新.

Value / quality screen for the Trading Agent OS. Prefer cash-generative names with reasonable debt and a margin of safety.

Generate short videos with the generate_video tool (Veo, Sora, Hailuo) - scripts, storyboards, scene-by-scene AI generation, and ffmpeg assembly. Use for social clips, product demos, and ads.

Hunt for concrete, exploitable vulnerabilities - OWASP Top 10 patterns, hardcoded secrets, vulnerable dependencies and CVEs, SSRF, path traversal. Use for /probe, dependency audits, or "find the flaws" requests.

Get current weather and forecasts (no API key required).

Turn websites into clean Markdown or structured JSON for RAG, migrations, or analysis. Use when the user needs scraped content, tables, or documentation corpora - not full browser testing.

Detect changes in price, content, tenders, or availability and alert only on meaningful deltas. Use with cron or .navin/HEARTBEAT.md for recurring checks.

Run a project's engineering decisions as a living log — MADR-lite ADRs (supersede, never delete) for decisions expensive to reverse, issues for the work (and its cheap-to-reverse choices), research artifacts, an optional PRD, a project constitution, and living Mermaid architectur

Author and validate knowledge as OKF (Open Knowledge Format) bundles — a directory of markdown files with YAML frontmatter, where every concept is one .md file with a REQUIRED `type` field, reserved index.md/log.md files, bundle-relative cross-links, and a permissive conformance

Organize, format, and index research as OKF-conformant knowledge bundles — dated, sourced, append-only snapshots of the external evidence behind a decision (technology evaluations, library comparisons, industry-practice surveys). Use when recording research output, structuring a

Add a new declarative attribute to the better-data library (e.g. #[ArrayOf], #[Default], domain hint). Attributes live in src/Attribute/ as final readonly classes with constructor-promoted public properties — pure data carriers, never business logic. The failure mode that catches

Compose better-data DTOs with the better-route library — use BetterRouteBridge::{get, post, put, patch, delete} to register a REST route that hydrates the request into a DTO, validates, calls the handler with (DataObject, mixed $request), and presents returned DataObject values t

Use MCP Inspector to connect to local or remote servers, inspect capabilities, call tools, read resources, test prompts, and diagnose failures before release.

Build an MCP server in TypeScript with focused tools, validated schemas, local and remote transports, Inspector tests, and production security controls.

An MCP server exposes tools, resources, or prompts through a standard protocol so an AI application can discover and use external capabilities.

Treat an AI agent skill as both an instruction package and a software dependency: inspect what it says, what it runs, what it can access, and how it updates.

Add remote HTTP or local stdio MCP servers to Claude Code, choose the right scope, protect credentials, verify the connection, and test with least privilege.

Skills teach Claude a repeatable method, connectors provide governed access to apps and live data, and plugins package related capabilities for installation and sharing.

Use an agent skill to package reusable know-how and workflow instructions. Use an MCP server when an agent needs live, governed access to external data or actions.

Custom commands and skills can both create a slash-invoked workflow in Claude Code. The important choice is how the workflow is discovered, shared, and permissioned.

A useful Claude skill solves one recurring engineering job, is easy to inspect, and saves more time than it creates in setup and review.

Claude skills can live in your Claude account, your local Claude Code setup, or a repository. Install them where the sessions that need them can load them.

Build a portable AI agent skill from one repeatable job: a precise description, concise instructions, focused resources, and tests that prove it works.

AI agent skills package instructions, scripts, references, and templates into portable folders an agent loads only when the task calls for them.

AI made publishing cheap, which is exactly the problem. What separates a page worth ranking from a competent summary of the first ten results.

A prompt that works once isn't a quality system. Five cases, an observable rubric, and a regression set will tell you whether a change helped.

One character of YAML, four pods that never started, and two safety nets I didn't know were holding. Every restart is an audit. Schedule them before they schedule you.

"Verify your work" isn't an instruction. It's a mood. Here's the version that's an instruction. Verify with a different mechanism than the one that made the claim.

A prompt that works once may still fail in production. A lightweight eval set gives you repeatable cases, a clear rubric, and a way to see whether a prompt change actually improved the workflow.

The best AI tool is not the one with the longest feature list. It is the one that solves a defined job reliably, fits the workflow, handles data appropriately, and remains useful after the novelty wears off.

Use AI to speed research without losing trust. Learn to find primary sources, verify claims, preserve uncertainty, and keep an auditable source trail.

Better prompts aren't magic wording. They're short briefs that hand the model a task, the context it can't infer, the limits, and a quality bar.

Close a finished session: sweep for unfinished work, ask once, land, file the follow-ups, hand off, tell the sessions that depend on this one, then archive.

Write the repository handoff file for the next session, and record any durable learning.

Merge an approved pull request, clean up its worktree and branch, then check whether a release is due.

Turn a topic or issue into a plan the reviewer approves in the native plan pane.

Answer a research question with parallel read-only gatherers and one synthesized digest.

Review the branch's diff in two fresh contexts — scope against the spec, then quality — and report findings only.

Audit infra security: secrets, deps, CI/CD, webhooks, AI/skill files

Audit Solana program code for exploitable bugs and write a findings report

Compare per-instruction CU with the stored baseline to catch regressions

Build the web client (Next.js, Vite, React) and check env, types and bundle

Build Solana programs (Anchor, Pinocchio, native), incl. verifiable builds

Build the Unity project in batchmode for WebGL, desktop, Android or PSG1

Turn a solana-ai-kit fork into a project: set up CLAUDE.md, remove kit files

Un-ignore and commit the kit config dir, instruction file, .mcp.json and .gitmodules

Replay a user's failing transaction on forked state and map the error to source

Deploy a program to devnet, or to mainnet after the user's explicit go-ahead

Review the branch diff for Solana security issues, CU waste and AI slop

Read-only check of toolchain and kit config, with one fix-it command per failure

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

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

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.