LLM Mart Basic
@llm-mart · Joined Jun 2026
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
Create user stories with duplicate checking across any project tracker (Linear, GitHub Issues, Jira)
Generate personalized news intelligence with verified sources (7-day freshness requirement)
A passive daily work journal that Claude keeps FOR you so you never have to write it yourself. Append short entries after meaningful work (what was done, what you focused on, artifacts touched) to 01-daily/journal/YYYY-MM-DD.md. Run a guided reflection at night or in the morning.
Pick the right way to represent a dataset so a reader gets the finding in three seconds — a catalog of 20+ chart and diagram forms with when-to-use and failure modes, plus the encoding decisions that make any of them readable (takeaway headline, direct labels, kill the axis, high
Generate meaning-carrying editorial data-illustrations in the monotykamary / Linear aesthetic (near-black grayscale, Inter display + mono labels, hairline framed figures) with a single coral accent. This is a GENERATIVE GUIDE, not a template gallery: it teaches the "claim -> geom
Audit and export open issues from any project tracker with summary analysis and vault archival
Generate product requirements documents with optional publishing to Confluence or other wiki platforms
Generate categorized release notes from any source (GitHub, Linear, Jira, or manual input) with optional publishing
Capture durable session learnings, stage for human promotion to 05-knowledge/lizard, and propose skill/CLAUDE.md patches. Triggered by /harvest, SessionEnd hook staging, or nightly enhance. Never writes durable knowledge without your approval.
Build frameworks from scattered insights across all braindumps and notes
Shared loop-engineering reference for COG skills - the agent loop, deterministic verifiers, termination conditions, in-loop context management, and named patterns. Invoke when designing or debugging a skill that iterates (search-verify-retry, scan-until-dry, fetch-retry-gate).
Process meeting recordings and notes into structured decisions, action items, and team dynamics with intelligent noise filtering
Periodic trust sweep of persistent memory and durable knowledge notes - re-verifies environment-dependent claims against the live environment, stamps last_verified + confidence, and proposes archiving drifted entries
Source authentic, high-res PUBLIC-DOMAIN artwork from museum open-access APIs (Met, Cleveland, SMK, Rijksmuseum, NGA, Art Institute of Chicago, Getty, Smithsonian) instead of AI-generated or generic-stock imagery. The default move whenever a visual needs an aesthetic, credible im
Edit drafts into sharper, more human writing while preserving the writer's personal voice, or detect AI-slop patterns without rewriting. Use when the user wants a draft clearer, more direct, more opinionated, or less AI-sounding, or asks whether writing reads as AI.
Personalize COG for your workflow - creates profile, interests, and watchlist files with guided setup (run this first!)
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
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.
/smart-fix
Smart fix
Intelligent issue resolution with multi-agent debugging, root cause analysis, and verified fix implementation
/typescript-scaffold
Typescript scaffold
Scaffold a TypeScript project (Next.js, React with Vite, Node.js API, or library) with pnpm, testing, and dev tooling
/ai-assistant
Ai assistant
Build AI assistant application with NLU, dialog management, and integrations
/langchain-agent
Langchain agent
Create LangGraph-based agent with modern patterns
/prompt-optimize
Prompt optimize
Optimize prompts for production with CoT, few-shot, and constitutional AI patterns
/finetune
Finetune
Run the eval-gated fine-tuning lifecycle end to end — eval harness, method selection, data, environment, training, checkpoint gate, export
/promote-checkpoint
Promote checkpoint
Re-gate an existing fine-tuned checkpoint against the current eval harness and export it on PROMOTE
/ml-pipeline
Ml pipeline
Orchestrate specialized agents to build a production ML pipeline from data analysis through training, deployment, and monitoring
/find
Find
Quick gallery search. Use when user runs /meigen-ai-design:find with keywords to browse inspiration.
/gen
Gen
Quick image generation. Use when user runs /meigen-ai-design:gen with a prompt. Skips intent assessment, generates directly.
/multi-platform
Multi platform
Orchestrate cross-platform feature development across web, mobile, and desktop with API-first architecture
/monitor-setup
Monitor setup
Set up monitoring and observability with Prometheus metrics, Grafana dashboards, distributed tracing, log aggregation, and alerting
/slo-implement
Slo implement
Implement SLOs with SLI selection, error budgets, burn-rate alerting, dashboards, and reporting
/ai-review
Ai review
Run an AI-assisted code review that combines static analysis tools with AI review of security, performance, and architecture
/multi-agent-review
Multi agent review
Coordinate specialized review agents in parallel or in sequence and synthesize their findings into one code review
/certify
Certify
Full quality certification with badge
/compare
Compare
Compare two skills head-to-head
/eval
Eval
Evaluate a plugin or skill for quality
/audit-chain
Audit chain
Verify every receipt in ./receipts/receipts.jsonl against the signer's public key. Detects tampered or malformed receipts across the audit trail.
/verify-receipt
Verify receipt
Verify a single Ed25519-signed receipt file against the signer's public key. Returns exit 0 if valid, 1 if tampered, 2 if malformed or the key is missing.
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