LLM Mart Basic

Inspect embedded dashboard configuration, trusted domains, origins, guest-token routing, and embedded RLS routing through direct Superset API calls. Use only for direct API workflows; Do not use for MCP-only work.

Prepare and create Preset embedded dashboard guest tokens, external-user claims, resource claims, RLS claims, and token-handling plans through direct Superset API calls. Use only for direct API workflows; Do not use for MCP-only work.

Inspect and route direct Superset import/export workflows for dashboards, charts, datasets, databases, saved queries, themes, and asset bundles. Use only for direct API workflows; Do not use for MCP-only work.

Review Preset role, workspace membership, permission, access-control, DAR/RLS-adjacent, and effective-access changes through direct API calls. Use only for direct API workflows; Do not use for MCP-only work.

Prepare Snowflake Cortex direct API access: account URL, auth method, role, warehouse, database/schema context, privileges, and Cortex Agent routing. Use only for direct API workflows; Do not use for MCP-only work.

Run or route SQL Lab execution, result retrieval, exports, query stop, saved-query mutation, and permalink workflows through direct Superset API calls. Use only for direct API workflows; Do not use for MCP-only work.

Inspect Preset SQL Lab bootstrap, query history, saved queries, result/export routing, query control, and SQL execution routing through direct Superset API calls. Use only for direct API workflows; Do not use for MCP-only work.

Discover and validate direct Superset workspace API capabilities: version, OpenAPI, current user, permissions, menu, and API safety. Use only for direct API workflows; Do not use for MCP-only work.

Discover Preset teams and workspaces through direct Management API calls, including workspace lookup, hostnames, health/status, and read-only memberships. Use only for direct API workflows; Do not use for MCP-only work.

Route Superset MCP tool workflows, source-of-truth checks, and surface-boundary decisions. Use only for MCP tool workflows; do not use for direct API work.

Use Superset MCP tools for dashboard inspection, dashboard creation, and adding charts to dashboards. Use only for MCP tool workflows; do not use for direct API work.

Use Superset MCP data-returning tools for chart data, chart previews, rendered chart SQL, and dataset query results. Use only for MCP tool workflows; do not use for direct API work.

Use Superset MCP tools for dataset inspection, semantic-layer querying, and virtual dataset creation. Use only for MCP tool workflows; do not use for direct API work.

Use Superset MCP health, instance, list, detail, schema, and chart-type discovery tools. Use only for MCP tool workflows; do not use for direct API work.

Use Superset MCP tools for SQL execution, SQL Lab links, and saved SQL queries. Use only for MCP tool workflows; do not use for direct API work.

Troubleshoot Superset MCP health, validation, permission, response-size, and bug-report workflows. Use only for MCP tool workflows; do not use for direct API work.

Use Superset MCP tools for Explore links, chart configuration discovery, chart previews, saved charts, and chart updates. Use only for MCP tool workflows; do not use for direct API work.

Guided workflow for converting a Tableau workbook (.twb or .twbx) to a Preset dashboard via Superset MCP tools. Parses TWB XML, scopes conversion to the target dashboard's worksheets, maps chart types, carries worksheet filters across, calls generate_chart per worksheet, and asse

Drive Preset's `sup` CLI (PyPI package `superset-sup`) for shell, scripting, CI/CD, and agent-driven Preset workflows. Use only for CLI workflows; Do not use for MCP-only work or for direct HTTP/SDK code paths.

State-changing Preset `sup` CLI workflows: chart/dashboard/dataset push, user push/invite, destructive flags, and cross-workspace sync. Use only for CLI mutation workflows. Do not use for MCP-only work. Do not use for direct HTTP/SDK mutations.

Use MCP Inspector to connect to local or remote servers, inspect capabilities, call tools, read resources, test prompts, and diagnose failures before release.

Build an MCP server in TypeScript with focused tools, validated schemas, local and remote transports, Inspector tests, and production security controls.

An MCP server exposes tools, resources, or prompts through a standard protocol so an AI application can discover and use external capabilities.

Treat an AI agent skill as both an instruction package and a software dependency: inspect what it says, what it runs, what it can access, and how it updates.

Add remote HTTP or local stdio MCP servers to Claude Code, choose the right scope, protect credentials, verify the connection, and test with least privilege.

Skills teach Claude a repeatable method, connectors provide governed access to apps and live data, and plugins package related capabilities for installation and sharing.

Use an agent skill to package reusable know-how and workflow instructions. Use an MCP server when an agent needs live, governed access to external data or actions.

Custom commands and skills can both create a slash-invoked workflow in Claude Code. The important choice is how the workflow is discovered, shared, and permissioned.

A useful Claude skill solves one recurring engineering job, is easy to inspect, and saves more time than it creates in setup and review.

Claude skills can live in your Claude account, your local Claude Code setup, or a repository. Install them where the sessions that need them can load them.

Build a portable AI agent skill from one repeatable job: a precise description, concise instructions, focused resources, and tests that prove it works.

AI agent skills package instructions, scripts, references, and templates into portable folders an agent loads only when the task calls for them.

AI made publishing cheap, which is exactly the problem. What separates a page worth ranking from a competent summary of the first ten results.

A prompt that works once isn't a quality system. Five cases, an observable rubric, and a regression set will tell you whether a change helped.

One character of YAML, four pods that never started, and two safety nets I didn't know were holding. Every restart is an audit. Schedule them before they schedule you.

"Verify your work" isn't an instruction. It's a mood. Here's the version that's an instruction. Verify with a different mechanism than the one that made the claim.

A prompt that works once may still fail in production. A lightweight eval set gives you repeatable cases, a clear rubric, and a way to see whether a prompt change actually improved the workflow.

The best AI tool is not the one with the longest feature list. It is the one that solves a defined job reliably, fits the workflow, handles data appropriately, and remains useful after the novelty wears off.

Use AI to speed research without losing trust. Learn to find primary sources, verify claims, preserve uncertainty, and keep an auditable source trail.

Better prompts aren't magic wording. They're short briefs that hand the model a task, the context it can't infer, the limits, and a quality bar.

Discover testnet↔mainnet subnet lineage from repo configs and open a PR for review (pass --dry-run to report only)

Triage raw inbox notes into reviewed repository destinations without deleting their sources.

Audit and rewrite content to remove AI writing patterns

Log a completed piece of content to content/log.md after the user confirms it was published.

Validate a dream artifact, review each proposal, and apply only individually accepted changes.

Run a curator pass against the validated memory directory and produce a proposal artifact.

End a session — log what happened, update state and the decision log, propose memory updates, and check for uncommitted or unpushed work

Find relevant context files by topic. Use when you need to load files for a topic without a slash command, or when a task spans multiple domains.

Inventory and migrate selected Gemini CLI workflows with dry-run review and parity checks.

Find repeated workflows in selected Gemini CLI sessions and draft portable skills after review.

Scan multi-session drift and offer individually reviewed fixes only after explicit approval.

Scan orphaned worktrees and stale branches, then offer explicit approval-gated cleanup.

Guided onboarding or import for durable workspace context

Start a session — load state files, flag staleness, and give a briefing on current priorities, deadlines, and blockers

Create a morning heartbeat from repository state and update the local heartbeat log.

Mid-session checkpoint — append progress to today's session log and update state files if a priority shifted, without ending the session

Maintainer-only. Find every file that would newly ship to adopters, classify each one against the written distribution-boundary categories, default to withhold on no clean match, and ask the maintainer only where the taxonomy does not settle it. Drives the release CLI, which refuses to produce a manifest until every shipping file has an answer.

Audit memory, wiki, and auto-loaded files for duplication, conflicting instructions, and stale content. The default path researches, then asks you a single Apply / Discuss / Decline question; on Apply it applies the report, files any out-of-scope problem as a tech-debt issue, then commits, opens a PR, and merges it on a main-branch run like /update-deps. Pass --apply to re-run the apply-and-publish stage against the most recent report.

Fix the tech-debt backlog, a single issue or a recommended related batch, highest severity then oldest first, on a fresh isolated branch through the audit gate, closing the issue(s) on merge. Pass `list` to see the ordered backlog, `why <issue-number>` to explain the recommendation, or a bare `<issue-number>` to fix that issue directly.

Health-check and auto-heal this project's Claude integration, triage, heal, verify, and report an F-to-A+ grade.

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一套 DSH runtime，Desktop、Web 与 TUI 三种开发体验。

Open-source operational advisor for ClickHouse — real-time monitoring plus AI-driven index/partition/materialized-view recommendations.

⚙️ TypeScript Style Guide and Agent Skill. A concise set of conventions and best practices for consistent, maintainable code.

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Neo.mjs is a self-evolving software organism: a professional end-to-end AI engineering team whose cross-model swarm inhabits live apps via Neural Link, Active H…

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