LLM Mart Basic
@llm-mart · Joined Jun 2026
Review code for correctness, clarity, and security.
Write clean, correct, production-quality code.
Evaluate trade-offs, document options, and justify recommendations.
Read, create, edit, and organize files and directories.
Work with GitHub branches, PRs, issues, and reviews via the gh CLI.
Break problems into steps, identify dependencies, and estimate scope.
Gather information, evaluate sources, and synthesize findings.
Use skillfold to manage project and user skills for Claude Code, Codex, and Cursor. Declare skills in skillfold.yaml, pin them in skillfold.lock, and install them reproducibly.
Condense information with audience-appropriate detail levels.
Write and reason about tests, covering behavior, edge cases, and errors.
Produce clear, structured prose and documentation.
Implements code incrementally with quality gates. Use when the user says 'build' or 'implement', or when starting the implementation phase of an approved plan.
Monitor the CI pipeline for the current branch via a background Monitor script (GitHub or GitLab), reacting to pass, fail, and manual-gate states. Use when the user says 'watch CI', 'monitor the pipeline', 'is CI green', or after pushing a branch or creating a PR/MR.
Runs a structured production-incident investigation that forces evidence-first hypothesis ranking before any code change. Use when given an error message, Sentry alert, failing log, or an 'investigate <X>' request.
Creates or updates a diagram, picking mermaid vs drawio per rules/diagrams.md, writing the source file, and previewing via MCP. Use when the user says 'diagram' or '/diagram', or asks for a flowchart, architecture, sequence, or state diagram.
Drives a fleet of MRs/PRs to done with a manager loop plus the built-in /goal command, delegating all edit, review, rebase, and conflict work to worktree-isolated domain-expert subagents. Use when the user says 'drive fleet' or 'drive the fleet', has 2+ independent lanes to drive
Investigates and fixes a GitHub issue. Use when given an issue number or URL, or when the user says 'fix issue'.
Runs a grilling session that challenges a plan against the existing domain model, sharpens terminology, and updates the CONTEXT.md glossary inline as decisions are made. Use when the user wants to stress-test a plan against their project's language and documented decisions.
Compacts the current conversation into a handoff document another agent can pick up. Use when the user says 'handoff', 'hand off', or wants to continue this work in a fresh session.
Finds deepening opportunities in a codebase, informed by the domain language in CONTEXT.md and the decisions in docs/adr/. Use when the user wants to improve architecture, find refactoring opportunities, consolidate tightly-coupled modules, or make a codebase more testable and AI
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.
/create-database-migrations
Create database migrations
Create and manage database migrations
/create-docs
Create docs
Analyze GitHub issue and create technical specification with implementation plan
/create-feature
Create feature
Scaffold new feature with boilerplate code
/create-jtbd
Create jtbd
Create a Jobs to be Done (JTBD) document for a product feature focusing on user needs
/create-onboarding-guide
Create onboarding guide
Create developer onboarding guide
/create-pr
Create pr
Create a new branch, commit changes, and submit a pull request with automatic commit splitting
/create-prd
Create prd
Create a Product Requirements Document (PRD) for a product feature
/create-prp
Create prp
Create a comprehensive Product Requirement Prompt (PRP) with research and context gathering
/create-pull-request
Create pull request
Guide for creating pull requests using GitHub CLI with proper templates and conventions
/create-worktrees
Create worktrees
Manage git worktrees for open PRs and create new branch worktrees
/cross-reference-manager
Cross reference manager
Manage cross-platform reference links
/debug-error
Debug error
Systematically debug and fix errors
/decision-quality-analyzer
Decision quality analyzer
Analyze decision quality with scenario testing, bias detection, and team decision-making process optimization.
/decision-tree-explorer
Decision tree explorer
Explore decision branches with probability weighting, expected value analysis, and scenario-based optimization.
/dependency-audit
Dependency audit
Audit dependencies for security vulnerabilities
/dependency-mapper
Dependency mapper
Map and analyze project dependencies
/design-database-schema
Design database schema
Design optimized database schemas
/design-rest-api
Design rest api
Design RESTful API architecture
/digital-twin-creator
Digital twin creator
Create systematic digital twins with data quality validation and real-world calibration loops.
/directory-deep-dive
Directory deep dive
Analyze directory structure and purpose
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