LLM Mart Basic
@llm-mart · Joined Jun 2026
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
/schema
Schema
Check frontmatter against the schema
/scope
Scope
Pull knowledge into a project
/secrets
Secrets
Scan for credentials
/sources
Sources
Show what a claim rests on
/split
Split
Split an overloaded page
/stale
Stale
Find concept pages nobody has touched
/tags
Tags
Audit the tag vocabulary
/timeline
Timeline
How my sources developed over time
/trace
Trace
Show which pages an answer used
/typed-links
Typed links
Add relation types where they matter
/weekly
Weekly
The weekly review
/build
Build
Implement an approved plan or issue in its own worktree, run the gate, open the pull request.
/close-out
Close out
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.
/handoff
Handoff
Write the repository handoff file for the next session, and record any durable learning.
/land
Land
Merge an approved pull request, clean up its worktree and branch, then check whether a release is due.
/plan
Plan
Turn a topic or issue into a plan the reviewer approves in the native plan pane.
/research
Research
Answer a research question with parallel read-only gatherers and one synthesized digest.
/review
Review
Review the branch's diff in two fresh contexts — scope against the spec, then quality — and report findings only.
/ia-refine-prompt
ia-refine-prompt
Transform a vague prompt into precise, structured AI instructions
/build
Build
Implement an approved plan or issue in its own worktree, run the gate, open the pull request.
Make any song you can imagine
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