LLM Mart Basic

Analyze and improve the improvement process. Use for detecting regressions and meta-optimization.

Surface expert frameworks. Use when creating or evaluating skills, hooks, or agents.

Build composable skill modules with hub-and-spoke loading. Use when token budget is tight.

Review plugin quality with tiered checks and dependency scoping. Use for PR and pre-release audits.

Evaluate Claude Code rules in .claude/rules/. Use for frontmatter, globs, and quality audits.

Audit Skill() refs; detect hubs, isolates, and dangling targets. Use when auditing skills.

Evaluate Claude skill quality through auditing. Use when reviewing or auditing skills.

Test skills via TDD in fresh subagents. Use when validating behavior or preventing bias.

Selects architecture paradigm via research before scaffolding. Use when architecture is undecided and the choice needs justification and documentation.

Polishes working code through successive quality passes in fresh subagents. Use after tests pass when code needs multi-dimension refinement before release.

Generates Makefiles with testing, linting, formatting, and automation targets. Use when starting a project or standardizing build automation.

Orchestrates full project lifecycle by auto-detecting state and routing to the correct phase. Use when starting or resuming a project mid-workflow.

Configures pre-commit hooks for linting, type checking, formatting, and testing. Use when setting up a new project or adding quality gates to an existing one.

Guides project ideation via Socratic questioning to produce a validated brief. Use before specification when requirements are unclear.

Scaffolds new projects with git, CI/CD workflows, pre-commit hooks, and build config. Use when starting a new Python, Rust, or TypeScript project from scratch.

Converts a specification into a phased, dependency-ordered implementation plan. Use after specification is complete and before execution begins.

Transforms project briefs into testable specifications with user stories and acceptance criteria. Use after brainstorming, before planning.

Build a project skill library in .claude/skills/ via discovery, parallel authoring, and review. Use when packaging tribal knowledge. Do not use for one skill.

Convenes a multi-LLM expert panel to pressure-test hard-to-reverse decisions. Use when reversibility score is low and adversarial review is warranted.

Assesses decision reversibility and risk at critical checkpoints. Use when a workflow reaches a high-stakes branch needing escalation check.

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.

hydra - Show detailed status of one or all worktrees

hydra - Live monitoring of background agents with status table

import - List all cached documentation

import - Search within cached documentation

import - Update cached documentation

import - Fetch URL (via Playwright if blocked) or copy local path to docs/

limit - Display all highscores for all plans

marketplace - Restore original plugin version from backup

marketplace - Install local plugin version for testing (backup original)

<claude-mem-context>

Aggregate SKMTC friction log entries into a review — cluster patterns, classify interventions, calculate convergence metrics, produce an action plan

<claude-mem-context>

Run a SKMTC retrospective on the current session — capture friction and wins to the friction log

CoalMine measurement dashboard — canary activity this session + rule-freshness status across the project's rules home

CoalMine self-update — check for a newer CoalMine version and offer to apply it, or set how updates are handled

CoalMine measurement dashboard — canary activity this session + rule-freshness status across the project's rules home

CoalMine self-update — check for a newer CoalMine version and offer to apply it, or set how updates are handled

CoalWash stats — current band, certain-fat reading, both break-evens, Memory-BMI (informational), pending-ask state, and the last run's trace for this project

CoalWash self-update — check for a newer version and offer to apply it, or set how updates are handled.

CoalWash stats — current band, certain-fat reading, both break-evens, Memory-BMI (informational), pending-ask state, and the last run's trace for this project

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PhysicsOS 是一个面向初高中物理学习的公益可视化智能体，通过 AI 理解题目并结合物理引擎，将抽象物理过程转化为可交互、可观察、可计算、可验证的真实物理场景。