LLM Mart Basic
@llm-mart · Joined Jun 2026
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
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.
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.
Use MCP Inspector to connect to local or remote servers, inspect capabilities, call tools, read resources, test prompts, and diagnose failures before release.
/rollback-deploy
Rollback deploy
Rollback deployment to previous version
/rsi
Rsi
Read project commands and documentation to optimize AI-assisted development process
/run-ci
Run ci
Run CI checks and fix any errors until all tests pass
/security-audit
Security audit
Perform comprehensive security assessment
/security-hardening
Security hardening
Harden application security configuration
/session-learning-capture
Session learning capture
Capture and document session learnings
/setup-automated-releases
Setup automated releases
Setup automated release workflows
/setup-cdn-optimization
Setup cdn optimization
Configure CDN for optimal delivery
/setup-comprehensive-testing
Setup comprehensive testing
Setup complete testing infrastructure
/setup-development-environment
Setup development environment
Setup complete development environment
/setup-formatting
Setup formatting
Configure code formatting tools
/setup-kubernetes-deployment
Setup kubernetes deployment
Configure Kubernetes deployment manifests
/setup-linting
Setup linting
Setup code linting and quality tools
/setup-load-testing
Setup load testing
Configure load and performance testing
/setup-monitoring-observability
Setup monitoring observability
Setup monitoring and observability tools
/setup-monorepo
Setup monorepo
Configure monorepo project structure
/setup-rate-limiting
Setup rate limiting
Implement API rate limiting
/setup-visual-testing
Setup visual testing
Setup visual regression testing
/share-your-story
Share your story
Open the Build with Claude contribution guide for writing a community story
/simulation-calibrator
Simulation calibrator
Test and refine simulation accuracy with validation loops, bias detection, and continuous improvement frameworks.
Persistent session memory for AI coding agents — local-first, with on-device inference, associative recall, and drift detection. Works with Claude Code, Cursor,…
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