LLM Mart Basic
@llm-mart · Joined Jun 2026
Anti-slop skill for PRODUCT UI - dashboards, data tables, forms, multi-step flows, settings, list/detail, app shells. The agent reads the surface, budgets the frame first, and ships dense interfaces that are correct at every edge case (overflow, long labels, empty/error/loading s
Publish any markdown file from the vault to Confluence with format conversion and approval gate
Turn a product release (the list of shipped items plus real screen recordings) into a motion recap video and one explained demo per feature, with sound effects tied to on-screen motion and a composed music bed. Deterministic HTML scenes rendered frame by frame, ElevenLabs for sou
CP-7 retrospective: audit checkpoints, evidence quality, action items, and harvest candidates. Closes the V-model cycle and feeds the next run. Use via /retro after ship, escalate, or significant session.
Produce and continuously maintain ONE living review document for a multi-item session — a cockpit header (Progress checklist, Working folder, Context) plus per-item review cards that you approve or request changes on directly in the doc or side panel. Use whenever a session has m
Evaluate URLs and tools — check vault coverage, assess relevance, recommend save or skip
Deterministic pre-publish scan that refuses AI-slop tells in anything about to be written, published, or sent: files, artifacts, slide titles, table headers, diagram labels, chat messages, commit messages. Use before publishing or sending any deliverable, in CI, or wired as a Cla
Anti-slop frontend skill for landing pages, portfolios, and redesigns. The agent reads the brief, infers the right design direction, and ships interfaces that do not look templated. Real design systems when applicable, audit-first on redesigns, strict pre-flight check.
Generate daily team intelligence brief by cross-referencing GitHub, Linear, Slack, PostHog, meetings, and braindumps with two-way Linear sync-back
Run a large, multi-session goal (e.g. shipping a whole side product) through the full V-model closed loop, one phase at a time, with cross-session state and a final north-star acceptance gate. Ultragoals never downgrade the lane: every phase runs CP-1→CP-6 with adversarial verifi
Check for and apply upstream COG framework updates (skills, docs, scripts) without touching personal content
Maintain and update product knowledge base from releases, features, and project changes with optional wiki sync
Quick capture URLs with automatic content extraction, insights, and categorization into knowledge booklets
Measure your own writing corpus for the words and sentence shapes you over-use, so an agent writing in your voice stops amplifying your tics into a style. Produces a counted baseline file and checks any new draft against it. Use when an agent's drafts start sounding like a carica
Cross-domain pattern analysis and strategic reflection for weekly review
Deep strategic research engine — decomposes questions into parallel research threads, spawns multiple agents, and synthesizes into actionable strategic analysis
Quick capture of raw thoughts with intelligent domain classification and competitive intelligence extraction
Run one task through the V-model verification loop: CP-2 plan → CP-3 build → CP-3v component verify → CP-4 integration verify (full lane) → CP-5 acceptance. The worker never grades its own homework; evidence rows trace back to AC-n. Opt-in: invoke with /closed-loop or by asking f
Deep-dive 7-day analysis across all data sources for weekly reviews, board prep, and strategic planning
Autonomous content pipeline - scout announcements in your field, triage by trend momentum and personal angle, produce posts/blogs/videos in your voice with ledger-based dedup, hard volume caps, and screenshot-verified publishing
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.
/cite
cite
Generate a formatted bibliography from the most recent research session's findings.
/dig
dig
Interactively refine research results by searching deeper into a specific subtopic or channel. Requires an active research session from /tome:research.
/export
export
Export research findings to a format compatible with memory-palace's knowledge-intake skill.
/research
research
Run a multi-source research session searching GitHub, HN, Lobsters, Reddit, arXiv, and Semantic Scholar. Use for multi-channel topic surveys.
/setup
Setup
Configure Azure MCP server with Azure CLI authentication
/load-claude-md
Load claude md
Refresh context with CLAUDE.md instructions
/sync-allowlist
sync-allowlist
Sync allowlist from GitHub repository to user settings
/sync-claude-md
Sync claude md
Sync CLAUDE.md from GitHub repository
/update-readme
Update readme
Update README.md plugin sections and download links
/setup
Setup
Configure GCloud CLI authentication
/setup
Setup
Configure Paper Search MCP (requires Docker)
/functions
Functions
Manage Supabase Edge Functions.
/gen
Gen
Automatically generates type definitions based on your Postgres database schema.
/init
Init
Initialize configurations for Supabase local development.
/link
Link
Link your local development project to a hosted Supabase project.
/login
Login
Connect the Supabase CLI to your Supabase account by logging in with your [personal access token](https://supabase.com/dashboard/account/tokens).
/network-bans
Network bans
Network bans are IPs that get temporarily blocked if their traffic pattern looks abusive (e.g. multiple failed auth attempts).
/projects
Projects
Provides tools for creating and managing your Supabase projects.
/secrets
Secrets
Provides tools for managing environment variables and secrets for your Supabase project.
/start
Start
Starts the Supabase local development stack.
An open-source, privacy-first, self-hosted knowledge workspace where humans and AI agents work together 开源、隐私优先、自托管的知识工作空间,让人与智能体在此协作
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