LLM Mart Basic

Clean a raw transcript before ingestion: punctuate, paragraph, label speakers, fix mistranscribed technical terms, and split long recordings by topic. Use this skill when the user drops an auto-generated transcript, subtitle file, podcast or lecture transcript, meeting recording

Draft from the vault: outline from concept pages, keep citations to the user's own sources, and surface where their material disagrees. Use this skill when the user wants to write an article, post, essay or newsletter based on what they have collected, or asks what they could wri

Use this skill when selecting and reporting verification for Skill changes; triggers include Skill change verification, quality gates, and evidence levels.

Use this skill when designing, running, interpreting, or reporting Agent Skill evaluations, selecting cases or judges, and analyzing trigger, benchmark, or regression evidence; triggers include Skill evaluation and evaluation design.

Use this skill when reviewing the contract completeness of Skills, Prompts, metadata, or QA documentation; triggers include Skill prose review, Prompt review, and contract audit.

Use this skill when auditing or trimming process residue from Skills, Prompts, comments, or docs; triggers include process prose cleanup, review residue, and current-state rewriting.

Use this skill when reviewing a complete Skill package for architecture, scope, triggers, independent installation, bilingual consistency, Eval readiness, and evidence boundaries; triggers include Skill quality review and package review.

Use this skill when you need to review acceptance criteria for ambiguity, missing rules, and verifiability; triggers include acceptance criteria review.

Use this skill when you need to design accessibility testing against WCAG, keyboard navigation, and assistive technology scenarios; triggers include accessibility testing and a11y testing.

Use this skill when you need evidence-bounded failure classification, retry/fallback/escalation, state consistency, user notice, and recovery evidence; triggers include Agent 故障恢复 and Agent failure recovery.

Use this skill when you need evidence-bounded checkpoints, heartbeats, resume, cancellation, duplicate submission, timeouts, and resource lifecycle; triggers include 长运行 Agent and long-running Agent.

Use this skill when you need evidence-bounded loop state, plan/action/observation cycles, stop conditions, budgets, repetition, and trace evidence; triggers include Agent 循环 and Agent loop.

Use this skill when you need evidence-bounded memory write/read/update/delete, retention, contamination, isolation, provenance, and forgetting behavior; triggers include Agent 记忆 and Agent memory.

Use this skill when you need evidence-bounded Agent identity, tool/resource scope, approval, denial, escalation, and side-effect boundaries; triggers include Agent 权限 and Agent permission.

Use this skill when you need to test AI agent tool-call contracts, authorization, failures, and side-effect boundaries; triggers include agent tool testing.

Use this skill when you need to test AI agent goals, state, planning, recovery, and safety boundaries; triggers include ai agent testing.

Use this skill when you need AI-assisted testing workflows such as test data generation, root-cause analysis, and prioritization; triggers include AI-assisted testing and AI for QA.

Use this skill when you need to test an AI-enabled product feature for behavior, safety, and user-impact boundaries; triggers include AI feature testing.

Use this skill when reviewing AI-generated unit, functional, API, or end-to-end tests for false confidence, weak assertions, missing risks, or unsafe test behavior; triggers include AI-generated test review and functional test review.

Use this skill when you need evidence-bounded safety policy, abuse categories, refusal/redirect behavior, privacy, escalation, and Human risk decisions; triggers include AI 安全 and AI safety.

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.

Stage and reconcile an ImmyBot desired-state software deployment to a tenant or computer

List and filter ImmyBot-managed computers, optionally scoped to a tenant

Show ImmyBot maintenance session status — active sessions, or detail and logs for a specific session

Find and execute an ImmyBot PowerShell script on a target computer (destructive, SYSTEM context)

Search the ImmyBot software catalog (per-tenant + global)

Portfolio-wide Inforcer baseline drift report — every managed tenant's alignment vs its assigned baseline, classified aligned / semi-aligned / drifted and sorted drifted-first, with secure score

Single-tenant Microsoft 365 posture snapshot from Inforcer — secure score plus alignment score, band, and the per-policy drift detail against the tenant's assigned baseline

Get an Ironscales AI verdict on a raw email, then act on it with a remediation action

Triage open Ironscales phishing incidents — list by status and severity, investigate, and remediate

Get a Kaseya Quote Manager quote with its sections and line items

Get a Kaseya Quote Manager sales order with its lines and payments

List Kaseya Quote Manager quotes, optionally scoped to a recent window

Add a note or comment to an existing Autotask ticket

View contract status, entitlements, and remaining hours for a company or specific contract

Check pricing details for an Autotask product or service from price lists

Create a new Autotask quote with line items for products, services, and service bundles

Create a new service ticket in Autotask PSA

Use this skill when working with Autotask expense reports - creating reports, adding expense items, searching by status or submitter, and tracking reimbursable and billable expenses

Search for Autotask configuration items/assets by name, serial number, or company

Search for Autotask companies by name, ID, or other attributes

Your efficient agentic AI coding CLI assistant

AI coding with Aegis governance built into execution: baseline-aware changes, evidence-backed delivery. Free desktop client, your choice of model. 将哲科思维融入 AI 开发…

Android in docker solution with noVNC supported, video recording, mcp server and AI-agent

Your AI assistant, built for the agentic era. Open source and local: talk to it, and it runs your agents, your coding CLIs, your browser and your apps. Windows,…

Verifiable program-level self-evolution for AI-for-Science agents: Skills and typed Operators grow from replay-verified executions.

Let Claude Code and other coding agents drive and debug Chrome from the shell. DOM, network, console and raw CDP as short commands, no screenshots and no MCP ne…

Unofficial, agent-friendly Mercadona shopping CLI (Go) — search products, read prices, build a cart and prepare checkout from the terminal. BYO credentials.

Local-first AI canvas for visual workflows, AI short drama, image/video generation, storyboarding and asset management. ComfyUI, AI agents, MCP & Blender. 本地优先…

Let any AI coding tool — Claude Code, Cursor, Codex — drive your real Chrome. One-prompt setup, muscle memory, local-first.

runs anywhere. uses anything

Open-source dots for the web: an AI agent with its own browser, one that does not get blocked.

Free MIT AI coding agent — no API key needed. Built-in free model, or bring Claude, GPT, Gemini, DeepSeek, Ollama +50 more. Swarm mode, 40+ tools, browser & des…

Open-source AI customer support system. AI-first support, human-ready operations.

A durable, multiplayer, mobile-first web app for Pi agents, built on Pi Durable

Embedded Cypher knowledge graph for Python and Rust. Bundled MCP server, describe() schema, and code-graph parser for LLM agents.

Turn PDFs, books and papers into interactive learning webpages｜将复杂材料转化为可追溯、可测验、可做笔记的学习网页

PhysicsOS 是一个面向初高中物理学习的公益可视化智能体，通过 AI 理解题目并结合物理引擎，将抽象物理过程转化为可交互、可观察、可计算、可验证的真实物理场景。