LLM Mart Basic

Use when creating or maintaining OpenCLI site sitemaps: agent-facing navigation, page-state, action, workflow, API-reference, pitfall, and fallback knowledge for a website. Use after browser exploration discovers durable site context, when a sitemap is stale, or when promoting lo

Use at the start of any OpenCLI session — this is the top-level map of what `opencli` can do, how to discover adapters, what flags and output formats are universal, and which specialized skill to load next. Point here when an agent asks "what can opencli do?" or "how do I find th

基于 opencli 命令的智能搜索路由器。当用户想要使用 OpenCLI、CLI 或 API 搜索、查询、查找或研究信息时，尤其是涉及指定网站、社交媒体、技术资料、新闻、购物、旅游、求职、金融或中文内容时，务必使用此 skill

Run explicitly requested Autoprompt v2 work through the private controller. Ordinary coding and review requests do not activate this skill.

Run explicitly requested Autoprompt work with task routing, owned assignments, independent checks, and bounded recovery.

Run explicitly requested Autoprompt v2 work through the private controller. Ordinary coding and review requests do not activate this skill.

Run explicitly requested Autoprompt v2 work through the private controller. Ordinary coding and review requests do not activate this skill.

Run explicitly requested Autoprompt v2 work through the private controller. Ordinary coding and review requests do not activate this skill.

Run explicitly requested Autoprompt v2 work through the private controller. Ordinary coding and review requests do not activate this skill.

Run explicitly requested Autoprompt v2 work through the private controller. Ordinary coding and review requests do not activate this skill.

Run explicitly requested Autoprompt work with task routing, owned assignments, independent checks, and bounded recovery.

Run explicitly requested Autoprompt v2 work through the private controller. Ordinary coding and review requests do not activate this skill.

Run explicitly requested Autoprompt v2 work through the private controller. Ordinary coding and review requests do not activate this skill.

Imported from spielewoy/autoprompt-skill/agents/README.md.

Imported from spielewoy/autoprompt-skill/agents/claude/agents/README.md.

Report the compatibility redirect to `ap-independent-checker`; this retired role cannot perform new work.

Report the compatibility redirect to `ap-worker`; this retired role cannot perform new work.

Report the compatibility redirect to `ap-worker`; this retired role cannot perform new work.

Report the compatibility redirect to `ap-run-coordinator`; this retired role cannot perform new work.

Report the compatibility redirect to `ap-worker`; this retired role cannot perform new work.

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.

Analyze Python async code for correctness, patterns, and potential issues.

Profile Python code for performance bottlenecks using cProfile, memory_profiler, or py-spy.

Evaluate public API surfaces against guidelines and exemplars.

Principal-level architecture assessment against ADRs and design patterns.

Systematic bug detection with language-specific expertise.

Run a detailed review that picks its dimensions from what the codebase and diff contain.

Active security hardening of the existing codebase, with a report and concrete proposals to apply.

Audit Makefiles for best practices and portability.

Intensive mathematical analysis for numerical stability and correctness.

Static-analysis hot-spot review for time and space complexity.

Analyze code quality across 6 dimensions (duplication, algorithms, clean code, architecture, errors, style) and apply fixes.

Expert-level Rust audits for safety and correctness.

Audit shell scripts for correctness, safety, and portability.

View recent skill executions with full context and error details.

Analyze skill execution metrics and identify unstable or underperforming skills.

Evaluate and upgrade test suites with TDD/BDD rigor.

Run a computer use task on the desktop via Claude's vision and action API

Stage changes, generate conventional commit message, commit, and push to current branch. One-shot git add-commit-push.

Draft a Conventional Commit message for staged changes. Analyzes diffs, classifies change type, and formats scope/body.

Create git release tags from merged PRs or version args. Pushes a v-prefixed tag to trigger the release pipeline, then confirms the run started.

Open-source, self-hosted AI media server. One server replaces your entire media stack, with a web app, native iPhone app, and an AI agent that gets things done.…

A curated list of tools built for Jev — TypeSafe AI's System One model for typed decisions.

🦦 Crayotter: A Multimodal AI-Agent for Video-Editing, Video-Composing, and Video Production. Powered by Multimodal LLMs for autonomous Text-to-Video agentic fr…

Webextension tool for Odoo