LLM Mart Basic

Полный цикл от записи встречи до планов разработки: локальная транскрибация с разбором экрана и скриншотами, извлечение списка задач, отдельный план разработки по SDD на каждую задачу, которой нужен код. Используй ВСЕГДА, когда пользователь дает путь к записи встречи или созвона

Practical guide for creating human-readable and agent-parseable diagrams using Mermaid. Includes conservative, renderer-compatible templates and when-to-use guidance.

Рендерит mermaid-диаграммы в PNG/SVG/PDF через локальный mermaid-cli (mmdc). Используй когда пользователь просит отрендерить mermaid, сделать PNG из диаграммы, превратить mermaid в картинку, конвертировать .mmd в png/svg/pdf, извлечь mermaid из markdown в картинки, сохранить блок

PowerShell scripting rules for Windows environment. Use when running shell commands, Docker operations, or HTTP requests on Windows PowerShell.

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, update or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's des

Транскрибирование видео и аудио файлов. Используй когда пользователь просит транскрибировать, расшифровать запись, сделать конспект встречи, извлечь речь из видео или аудио, преобразовать речь в текст. Для аудио (m4a/mp3/wav/ogg/flac/aac/wma) по умолчанию локальный faster-whisper

Локальная транскрибация аудиофайлов без отправки в облако. Используй когда пользователь просит транскрибировать запись, расшифровать аудио, сделать конспект встречи, преобразовать речь в текст. Только для аудио (m4a/mp3/wav/ogg/flac/aac/wma/opus). Движок: faster-whisper CUDA + оп

Распаковка и сборка бинарных файлов 1С (CF/CFE/EPF) через Python-утилиту v8unpack. Используй когда нужно распаковать конфигурацию, расширение или обработку в исходники, или собрать обратно.

Справочник API конфигурации 1С:ЗУП 3.1 (Зарплата и управление персоналом) по кадровому учету. Используй при разработке/доработке на ЗУП 3.1, когда нужны методы КадровыйУчет, КадровыйУчетРасширенный, ЗарплатаКадры, ФизическиеЛицаЗарплатаКадры - получение кадровых данных сотруднико

Imported from desko77/claude-code-skills-1c/skills/skill-creator/agents/analyzer.md.

Imported from desko77/claude-code-skills-1c/skills/skill-creator/agents/comparator.md.

Imported from desko77/claude-code-skills-1c/skills/skill-creator/agents/grader.md.

TrailSnap CLI 命令行工具，用于查询照片、相册、标签、位置和人物等信息。当用户需要查看照片、相册数据时调用此技能。

Evaluate mathematical expressions and unit conversions. Handles arithmetic, percentages, exponents, and common unit conversions (temperature, distance, weight). No external dependencies.

Analyze text content and produce statistics including word count, line count, character count, most frequent words, and readability metrics. Works on any plain text input provided inline or from a file path.

Router for the Cargo CLI skill bundle — load first for anything Cargo, and whenever a task spans two Cargo domains. Explains what each skill owns, declarative workspace-as-code (cargo-cdk) vs the imperative CLI, the UUID and slug flow between skills, async polling of runs and bat

Build and configure AI agents inside Cargo — create an agent, choose its model and temperature, write its prompt, attach knowledge for retrieval (RAG), connect MCP tool servers, manage memories, and deploy releases. Triggers: "create an agent", "make an agent that", "give the age

Get data out of Cargo and measure what ran — download a run output, export a segment or model to CSV or JSON, and pull run and batch success and error counts. Triggers: "download the results", "export this to CSV", "give me the file", "how many succeeded", "what is my error rate"

Understand what Cargo is costing — remaining credits, usage broken down by workflow, connector, or agent, subscription state, and invoice history. Triggers: "how many credits do I have left", "what did that cost", "why is my bill so high", "am I about to run out", "will this fit

Manage a whole Cargo workspace as code — declare connectors, models, plays, tools, agents, MCP servers, segments, context, folders, files, workers, and apps in TypeScript, then reconcile them with `cargo-ai cdk` (init → types → plan → deploy), the way you would run Pulumi or the

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.

Check frontmatter against the schema

Pull knowledge into a project

Scan for credentials

Show what a claim rests on

Split an overloaded page

Find concept pages nobody has touched

Audit the tag vocabulary

How my sources developed over time

Show which pages an answer used

Add relation types where they matter

The weekly review

Implement an approved plan or issue in its own worktree, run the gate, open the pull request.

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.

Transform a vague prompt into precise, structured AI instructions

Implement an approved plan or issue in its own worktree, run the gate, open the pull request.

The fastest way to put Volcengine Ark in your terminal and your AI agent — go from prompt to generated media, multimodal answer, or deployed endpoint in a sin…

本地私有、开源的自进化跨平台 AI 内容发现 Agent：先理解你，再主动从 B站、小红书、抖音、YouTube、X、知乎、Reddit、微博等平台与开放 Web 寻找内容。（支持 deepseek harness 插件） | Local-first open-source cross-platform AI cont…

Persistent memory for AI coding agents — one verified kb_search replaces the grep/find/ls orientation loop. Cross-repo, CPU-only, zero token spend.

AI 时代的伯克希尔：基于 Claude Code / Codex 的价值投资研究框架。巴菲特·芒格·段永平·李录四大师方法论 + 多Agent并行研究。| AI-era Berkshire: a value investing research framework built for Claude Code / Co…

The batteries-included, No-Code FinOps automation platform, with the AI you trust.

Open-source 3D AI agent framework — GLB/glTF avatars with LLM brains, memory, emotions, and autonomous payments. MCP server · x402 · Solana/EVM · Three.js. Embe…

XLSX parser for LLMs, RAG, LangChain, LangGraph, CrewAI, Claude, MCP — turns Excel (.xlsx) into citation-ready JSON with formulas, charts, dependency graphs, an…

Hermes-Relay — Your Hermes AI agent, in your pocket — chat, voice, and control.

A minimalist, terminal-native coding agent written in C.

AI-powered OSINT agent with interactive REPL, MCP server, and CLI. 19 tools. Works with Claude, GPT-4, or local models. For authorized security research only.

AI pair programming in your terminal — one static binary, sub-ms startup, any model

Where data access meets operational intelligence

Build your own security agents. Open-source framework for agents with live, read-only access to your infrastructure, with no path to widen it. Reasons across AW…

Multi-workspace terminal aggregator with Claude Code AI integration

Go implementation of AI coding agent

Harness engineering beginner tutorial, from 0 to 1

Generate images directly in DeepSeek Harness chats

A smarter, self-hosted AI assistant — multi-user, multi-agent.

Turn any research paper into a commercialization report — 6 AI agents, TRL/MRL scoring, patent landscape, market intelligence, verified citations. DeepSeek / Op…

Power BI CLI - semantic models (.NET TOM) and PBIR reports for token-efficient AI agent usage, built for Claude Code