LLM Mart Basic

Use when assessing the architectural health of a codebase — before a major refactor, when onboarding to an unfamiliar repo, after rapid growth, when planning a redesign, or to surface structural strengths and risks before they become expensive. Not for fixing what it finds, and n

EXPERIMENTAL. Use when looking for meaningfully duplicated logic in a codebase, especially duplicate behavior hidden behind different names, different syntax, different control flow, or independently evolved implementations. Not for style issues, not for syntactic clone detection

EXPERIMENTAL. Use when code needs a security review against the OWASP Top 10:2025 — access control, misconfiguration, supply chain, cryptography, injection, insecure design, authentication, integrity, logging and alerting, and mishandled exceptional conditions. Not for penetratio

Use when reviewing current branch for bugs before pushing or merging, when wanting a thorough multi-agent review of local changes, or when preparing work for human review. Not for codebase structure, not for code style, and not for fixing what it finds.

Use when verifying that requirements/specs/PRDs and their implementation plans match — before starting work, after a spec or plan update, or when suspecting coverage gaps, scope creep, or design drift between intent and action documents. Needs both documents; not for checking cod

Use when working through architectural flaws documented in a .reviews/architecture/ report — selecting which flaws to fix, resuming a partial fix session across multiple sittings, or applying structural changes that need to be tracked back to a report. Not for producing that repo

EXPERIMENTAL. Use when a session is running out of context and the work needs to continue in a fresh one, or when starting a session meant to pick up where an earlier one stopped. Not for compacting in place — that is /compact — and not for specifying work that has not started, w

Use when reviewing a spec, PRD, requirements doc, or design plan before implementation begins — especially when the doc feels too big, bundles unrelated features, may contradict the current codebase, or seems vague, infeasible, or thin on security and error handling. Not for cros

EXPERIMENTAL. Use when options, a recommendation, or an already-chosen approach are on the table and the reasoning under them has not been independently checked — especially when the case rests on cited documentation, remembered behavior, or premises nobody verified. Not for gene

EXPERIMENTAL. Analyzes a repository and any existing test suite, grades existing tests for weakness, classifies mocks, emits a phased roadmap for building a test suite that catches real regressions, then executes those phases one at a time. Use when planning or building a test su

Use when making a small, quick change — a bug fix, typo, minor feature, tweak, or anything the user calls "vibe coding" — that looks like 1-3 files in the same module

Use when auditing a user-facing app — web, mobile (iOS/Android/React Native/Flutter), desktop, CLI, or games — for accessibility barriers or WCAG 2.2 conformance, before shipping UI changes, or in response to concerns about screen-reader, keyboard, low-vision, motor, cognitive, o

Use when assessing the architectural health of a codebase — before a major refactor, when onboarding to an unfamiliar repo, after rapid growth, when planning a redesign, or to surface structural strengths and risks before they become expensive. Not for fixing what it finds, and n

Use when reviewing current branch for bugs before pushing or merging, when wanting a thorough multi-agent review of local changes, or when preparing work for human review. Not for codebase structure, not for code style, and not for fixing what it finds.

Use when verifying that requirements/specs/PRDs and their implementation plans match — before starting work, after a spec or plan update, or when suspecting coverage gaps, scope creep, or design drift between intent and action documents. Needs both documents; not for checking cod

Use when working through architectural flaws documented in a .reviews/architecture/ report — selecting which flaws to fix, resuming a partial fix session across multiple sittings, or applying structural changes that need to be tracked back to a report. Not for producing that repo

EXPERIMENTAL. Use when a session is running out of context and the work needs to continue in a fresh one, or when starting a session meant to pick up where an earlier one stopped. Not for compacting in place — that is /compact — and not for specifying work that has not started, w

Use when reviewing a spec, PRD, requirements doc, or design plan before implementation begins — especially when the doc feels too big, bundles unrelated features, may contradict the current codebase, or seems vague, infeasible, or thin on security and error handling. Not for cros

EXPERIMENTAL. Use when options, a recommendation, or an already-chosen approach are on the table and the reasoning under them has not been independently checked — especially when the case rests on cited documentation, remembered behavior, or premises nobody verified. Not for gene

EXPERIMENTAL. Analyzes a repository and any existing test suite, grades existing tests for weakness, classifies mocks, emits a phased roadmap for building a test suite that catches real regressions, then executes those phases one at a time. Use when planning or building a test su

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.

Full health check for one Proofpoint Essentials customer org

Resolve a Proofpoint Essentials customer org by name or domain and show its details

View recent TAP threat events including blocked messages, delivered threats, and click activity

Decode a Proofpoint URL Defense rewritten URL back to the original URL

Deep-dive threat investigation with forensics, campaign context, and remediation options

Release one or more quarantined messages to their intended recipients

Search quarantined messages in Proofpoint by sender, recipient, subject, or reason

Get the Very Attacked People (VAP) report showing the most targeted users

Run the full billing-drift sweep for a billing period, formatted as a month-end reconciliation report

List upcoming contract and subscription renewals within a window, sorted by date

Run the license true-up reconciliation for one client or the whole portfolio

Search Freshdesk tickets with the Freshdesk query language — filter by status, priority, agent, group, type, tag, and date — and return a ranked, readable result list

Summarize a single Freshdesk ticket and its full conversation thread — the request, what has happened, current SLA state, and the recommended next action

Add an action (note, update, or response) to an existing HaloPSA ticket

Check contract status, service entitlements, and billing information for a client

Create a new service ticket in HaloPSA

Search the HaloPSA knowledge base for articles and solutions

Search for configuration items/assets by name, serial number, type, or client

Search for HaloPSA clients by name, domain, or other attributes

Search for tickets in HaloPSA by various criteria

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.