LLM Mart Basic

Imported from arbiterforge/codearbiter/site/src/curated/agents/tribunal-lens-reviewer.md.

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

Release moi-computer to npm — verify locally, hand off to the gated GitHub Actions workflow, then verify the published package. Defaults to a `next` preview; pass `stable` for a `latest` release. Use when the user asks to publish, release, ship a version, or cut a dev preview.

Manages shadcn components and projects — adding, searching, fixing, debugging, styling, and composing UI. Provides project context, component docs, and usage examples. Applies when working with shadcn/ui, component registries, presets, --preset codes, or any project with a compon

The moi workspace — the web UI the user chats from, extended with agent-authored applets (widgets, views) plus theme & config. Read this FIRST when a message carries a hidden moi-context envelope or the user uses moi vocab such as workspace, applet, widget, view, scratchpad, dash

Embed screenshots, images, diagrams, GIFs, and screen recordings in GitHub PRs and issues — or stage them ahead of a PR, collect them into one attachments comment, or get a durable public link to share a visual with a person. Use this whenever a visual needs to end up in a PR des

Bake hand-drawn boxes, arrows, labels, freeform strokes, and redactions onto a screenshot so a PR reviewer or teammate sees exactly what changed and where to look, instead of reading a caption and hunting for it. Use this whenever a screenshot needs a callout — "point at the new

Reference for the uploads CLI and its stdio/hosted MCP tools — exact flags, keys, and contracts for put and attach, screenshot capture, stable PR/issue keys, the managed attachments comment, metadata and search, galleries, config defaults, login/doctor, and output formats. Use wh

Write Persian web copy (blog articles, product descriptions, landing pages, category text, microcopy, meta titles and descriptions) using Cursor workers, so the writing runs on the Cursor subscription's quota instead of Claude's. Use when the user asks for Persian content for a s

Delegate a coding or research task to the Cursor CLI (cursor-agent) so it runs on the Cursor subscription's quota instead of Claude's, with Claude still orchestrating. Use when the user says "delegate to cursor", "run this with cursor-agent", "offload to cursor", "spend the curso

Orchestrate a fleet of Cursor CLI (cursor-agent) workers to execute a large or multi-part job, while Claude does the architecture, UX/UI decisions, decomposition, review, and integration. Use when the user says "orchestrate with cursor", "build this with a cursor fleet", "fan out

Deep-clean and simplify a whole repo or codebase, then leave guardrails so it stays clean. Delete dead surfaces, gitignore build artifacts, relocate dev docs, notes, clones and dumps to the project's workspace repo (the assistant-dev home base), split oversized files and function

Give a repo its own rules at both layers, and put each rule at the cheapest layer that holds it. The told layer is what a project's workspace tells a model before it writes, so it establishes where a session starts, which repo each kind of file belongs in, how the gate is run, th

Generate a GitLab CI/CD pipeline and Helm chart that builds the current project with kaniko, pushes to the GitLab Container Registry, and deploys to a Kubernetes namespace via a kubeconfig CI variable. Use when the user asks to set up CI/CD, create a GitLab pipeline, write a Helm

Right-size Kubernetes pod CPU/memory requests from real Prometheus usage data (per-pod p95 CPU, p98 memory over 7 days), compute how many nodes the cluster needs, and tune KEDA queue-based autoscaling. Use when the user asks to rightsize workloads, fix over/under-provisioned requ

Deep OWASP reference for security reviews and secure implementation. Covers OWASP Top 10:2025, ASVS 5.0 levels, secure code patterns (injection, auth, error handling, fail-closed), language-specific security quirks for 20 languages, OWASP LLM Top 10 (2025), and Agentic AI securit

Apply a baseline of secure-coding standards to code being written or reviewed. Covers auth, secrets management, input validation, injection prevention, password hashing, PII handling, dependency hygiene, rate limiting, CORS, security headers, HTTPS, error/logging discipline, and

Export a reply or chat card as a standalone styled HTML report file, in the exact readable card template (Persian RTL with Vazirmatn, or English LTR with Inter). Use ONLY when the user explicitly asks to save, export, file, or extend THE CURRENT widget/card ("همین کارت رو ذخیره ک

Turn an answer into a light visual (flowchart, timeline, comparison, architecture sketch), in any language. Trigger ONLY when the user explicitly asks to see something visual, e.g. "show me", "visualize this", "show it as a diagram", "draw it", "با شکل نشون بده", "شماتیکش رو بکش"

The way into learnable and the way back. If a course already exists here it continues from exactly where the learner stopped, next lesson, next chapter, no re-planning. If nothing exists yet it asks whether to measure their level first or go straight to building a course. Use whe

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.

Record the present state by mode — decision (ADR, RFC, rule), code (spec, doc, guide, scenario), or research (a ready report or one external material); a gate picks the document type.

First-time Archcore setup — wire host configs, measure the authored context, compose the full first-day seed in one preview, and create it on one confirm; import converts CLAUDE.md, AGENTS.md, rule files, ADRs, and docs into native documents; refresh adds new facts or drills into one domain.

Plan a feature or initiative through a computed route — a small fix exits with no documents, one capability gets a spec and a plan, a large initiative gets an umbrella PRD with one spec per capability; start with sdd, sources (market research), iso (regulated work), or research (technical investigation) to run that path directly.

Review branch changes against Archcore docs, or report project health; drift runs staleness detection, deep a full documentation audit, closeout closes a finished feature, experience captures a repeated pattern.

Verify that citations actually exist and that the claims they support are faithful to the cited source. Runs deterministic existence checks (Crossref / OpenAlex / Semantic Scholar / arXiv) plus a claim-faithfulness pass via the alterlab-citation-verifier skill.

Run a systematic, reproducible literature review on a topic and return an APA 7.0 annotated bibliography with a documented search strategy. Invokes the alterlab-deep-research pipeline in lit-review mode.

Run a full multi-perspective peer review of a manuscript, simulating an Editor-in-Chief plus three peer reviewers and a Devil's Advocate, and produce a structured editorial decision and revision roadmap. Invokes the alterlab-paper-reviewer skill.

Orchestrate the end-to-end academic research-to-publication workflow (research, write, integrity check, review, revise, re-review, finalize) with mandatory integrity gates and two-stage peer review. Invokes the alterlab-research-pipeline orchestrator.

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

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.