LLM Mart Basic
@llm-mart · Joined Jun 2026
Agent management expert. ALWAYS invoke this skill when you need to list available agents, download or reference agent source code, deploy agent code to the host, or query the LLM connector matrix. Do not guess agent structures — use this skill first.
Audio expert. ALWAYS invoke this skill when the user asks to transcribe, recognize, or convert speech/audio to text.
Kanban board and task query expert. ALWAYS invoke this skill when the user asks about boards, tasks, task status, or project progress. Do not guess task state — use this skill first.
Yao process documentation expert. ALWAYS invoke this skill when the user needs to discover available processes, read process signatures, or validate process names. Do not guess process APIs — use this skill first.
Image expert. ALWAYS invoke this skill when you need to read, analyze, describe, or generate images. Use for screenshots, photos, charts, diagrams, AI-generated images, or any visual content.
OCR text recognition expert. ALWAYS invoke this skill when you need to extract text from images or PDFs — including invoices, receipts, ID cards, bank cards, business licenses, tables, handwritten documents, or any visual text content.
Yao process execution expert. ALWAYS invoke this skill when the user needs to call a Yao process, query data models, run scripts, or check process permissions. Do not call processes without checking this skill first.
Secret management expert. ALWAYS invoke this skill when you need to read API keys, tokens, or other secrets configured by the user. Never hardcode credentials — use this skill to retrieve them securely.
Web information retrieval expert. ALWAYS invoke this skill when the user needs to search the web, fetch a URL, or access real-time information beyond training data. Do not guess or use stale knowledge — use this skill first.
Workspace file I/O expert. ALWAYS invoke this skill when you need to list workspaces, read or write files in a workspace on a remote node, or browse workspace directories. Use this for cross-node file operations — for local sandbox files, use standard filesystem tools instead.
Workspace Git identity and credential management. ALWAYS invoke this skill when the user asks about configuring Git user info, adding HTTPS tokens, importing SSH keys, or managing workspace-level Git authentication.
Draft a well-formed new skill (a SKILL.md scaffold, optionally with scripts/references) from a described recurring need, for human review and approval. Use whenever a repeated workflow gap has no existing skill covering it, when someone wants to propose or create a new skill or c
Synthesize a plausible SWMM drainage network from public data (OSM streets + DEM) when NO real pipe-network data exists — input is just a bbox. Use ONLY when the user has no pipe shapefile/CAD/GIS data, or to establish a baseline before real data arrives; if real pipe data exists
Assemble a runnable SWMM INP deterministically from subcatchment geometry/attributes, merged parameter JSON, network JSON, and climate references. Use when creating auditable INP + manifest artifacts for downstream swmm-runner/calibration.
Calibration and validation scaffold for EPA SWMM. Use when an agent needs to (1) compare simulated vs observed flow, (2) evaluate candidate parameter sets, (3) rank explicit candidates by an objective, (4) run a bounded random / LHS / adaptive search for the best-fitting paramete
Fetch a ready-to-run SWMM model for any Canadian area from the SWMMCanada upstream service — real published municipal storm pipes where a supported city covers the AOI, synthesized elsewhere in Canada. Input is a bbox or GeoJSON polygon plus a rainfall date window. Use for Canadi
Deterministic rainfall/climate formatting for SWMM. Use when converting timestamped rainfall CSV files into SWMM-ready [TIMESERIES] lines and [RAINGAGES] helper snippets for swmm-builder.
Top-level orchestration skill for agentic SWMM modelling. Use when an agent needs one entrypoint that decides which module tools to run, in what order, and when to stop, for example to build, run, QA, and optionally calibrate a SWMM case from prepared or partially prepared inputs
Consolidate Agentic SWMM run artifacts into auditable provenance, comparison records, and local Obsidian audit notes. Use after any SWMM build/run/QA attempt, successful or failed, when an agent or CLI workflow needs a traceable record of inputs, commands, artifacts, metrics, QA
GIS/DEM preprocessing for SWMM experiments using the user's own QGIS/GRASS layers. Use when the user asks to (1) delineate subcatchments through QGIS/GRASS (standard or entropy-guided), (2) preprocess QGIS-derived subcatchment polygons into builder-ready CSV, (3) identify high-en
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.
/setup
Setup
credo - Set up Claude Code with recommended workflows and plugins
/cleanup
Cleanup
dogma - Find and fix AI-typical patterns in code (reactive cleanup)
/docs-update
Docs update
dogma - Sync documentation across README files and wiki articles
/force
Force
dogma - Interactively collect and apply CLAUDE rules to the project
/ignore
Ignore
dogma - Add ignore patterns to multiple locations at once
/lint
Lint
dogma - Run project-specific linting and formatting on staged files (non-interactive)
/permissions
Permissions
dogma - Create or update DOGMA-PERMISSIONS.md interactively (Git, File, and Workflow permissions)
/sanitize-git
Sanitize git
dogma - Sanitize git history from Claude/AI traces and fix tracking issues
/sync
Sync
dogma - Intelligently sync Claude instructions from a source to the current project with interactive review
/versioning
Versioning
dogma - Check and fix version mismatches across all version files
/setup
gsd:setup
Install GSD resources into the active Claude config dir (${CLAUDE_CONFIG_DIR:-$HOME/.claude}/get-shit-done/) (required before using other GSD commands)
/uninstall
gsd:uninstall
Remove GSD resources from the active Claude config dir (${CLAUDE_CONFIG_DIR:-$HOME/.claude})
/cleanup
Cleanup
hydra - Remove already merged worktrees and their branches
/create
Create
hydra - Create a new Git worktree for isolated work
/delete
Delete
hydra - Safely remove a Git worktree
/help
Help
hydra - Show available commands and explain the concept
/list
List
hydra - List all Git worktrees of the repository
/merge
Merge
hydra - Merge a worktree branch back into current branch
/parallel
Parallel
hydra - Start multiple agents in parallel across worktrees
/spawn
Spawn
hydra - Start an agent in an existing worktree
VCP 部署在 AI 模型 API 与前端应用之间,是面向AGI OS开发和探索的工业级基建示范项目。通过统一指令协议、多层级持久化记忆、分布式插件引擎及多 Agent 协作框架,将原本“无状态、无记忆、无工具调用能力”的大语言模型,彻底改造成拥有永久自我意识、物理世界操作权及群体协作智能的完整智能体系统。
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