LLM Mart Basic
@llm-mart · Joined Jun 2026
从游戏客户端(安装包/APK/IPA/EXE 或 dump.cs、lua、usmap、抓包等)反推服务端协议并复现可部署服务端。含阅读路径分派、原理层(primer:三要素/数据包协议/协议表/热更源码)、四阶段路线图(workflow-roadmap:静态分析→建工具+登录链→重定向→补包循环→清单迭代)、11 种反推方法选择器(含内联服务端路线)、接口清单提取器(tools/)、协议规格模板(protocol.spec.yaml)、wire 级定点改写(不等 schema 齐就能跑)、客户端地址来源清查、三轴状态与验收体系、发布运维清单、进度清单(T
Apply the Minimum Sufficient Work (MSW) principle through the MSW Kernel to scope, execute, verify, and stop agent work. State the requested outcome and smallest proof, admit a claim only when deleting it would leave the contract unmet or unproven, do and prove each necessary cla
Run an authorized task inside an Available Work Time (AWT) window with a shorter Closeout Grace Period (CGP), fixed deadlines, forecast checks, proportional convergence points, and a hard stop. Use when the user explicitly requests timeboxing, supplies an AWT/CGP pair, says AWT o
Apply the Minimum Sufficient Language (MSL) principle through the MSL Kernel to write anything a reader must act on. Bind the reader and what they already know, partition facts from the machinery that produced them, emit each admitted fact as an action, a verification, a judgment
Run the Codex Optimized Development, Evaluation, and Remediation (CODER) Loop with an orchestration-only coordinator, non-overlapping task-family owners, fresh independent reviewers, evidence-scoped remediation, and final coordinator acceptance. Discover and compose optional MSW,
Optimize ChatGPT Voice in the Codex desktop app into an ear-first control plane for free-form task coordination and opt-in workflows. Apply spoken synthesis, routing-only coordination, owning-task role contracts, project placement, explicit authority, safe speech, current-state v
Skill compilation specialist — the forge master. Use when the user asks to "talk to Ferris" or requests the "Skill Forge agent."
Initialize forge environment, detect tools, and set capability tier (Quick/Forge/Forge+/Deep). Use when the user requests to "set up" or "initialize the forge".
Discover what to skill in a large repo and produce recommended skill briefs. Use when the user requests to "analyze source for skills" or "discover skill opportunities."
Design a skill scope through guided discovery. Use when the user requests to "create a skill brief" or "brief a skill".
Compile a skill from a brief. Supports --batch for multiple briefs. Use when the user requests to "create a skill" or "compile a skill."
Fast skill from a package name or GitHub URL — no brief needed. Use when the user requests a "quick skill" or "skill from URL" or "skill from package."
Consolidated project stack skill with integration patterns — code-mode (analyzes manifests) or compose-mode (synthesizes from existing skills + architecture doc). Use when the user requests to "create a stack skill", "forge a stack", or "stack this project".
Smart regeneration preserving [MANUAL] sections after source changes. Use when the user requests to "update a skill" or "regenerate a skill."
Drift detection between skill and current source code. Use when the user requests to "audit a skill" or "audit skill" for drift.
Cognitive completeness verification — quality gate before export. Use when the user requests to "test a skill" or "verify skill completeness."
Rename a skill across all its versions — transactional copy-verify-delete with platform context rebuild. Use when the user requests to "rename a skill."
Drop a specific skill version or an entire skill — soft (deprecate) or hard (purge) with platform context rebuild. Use when the user requests to "drop" or "remove a skill."
Pre-code stack feasibility verification against architecture and PRD documents. Use when the user requests to "verify a tech stack" or "verify stack."
Improve architecture doc using verified skill data and VS feasibility findings. Use when the user requests to "refine skill architecture" or "improve architecture doc."
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.
Build an MCP server in TypeScript with focused tools, validated schemas, local and remote transports, Inspector tests, and production security controls.
An MCP server exposes tools, resources, or prompts through a standard protocol so an AI application can discover and use external capabilities.
Treat an AI agent skill as both an instruction package and a software dependency: inspect what it says, what it runs, what it can access, and how it updates.
/editable-figure
Editable figure
Design concise overview, mechanism, or workflow figures as editable PowerPoint objects
/implement-review
Implement review
Run the implement-review staged-change review loop
/my-router
My router
Detect the work type (papers, proposals, code, figures, admin) and dispatch to the right domain skill
/prun
Prun
Run prun: parallel delegation fan-out on Agy workers (the session coordinates)
/readme-polish
Readme polish
Audit a GitHub README and rewrite it with modern patterns for a scannable ten-second skim
/vet
Vet
Vet the staged change: run the implement-review review loop (short alias)
/learn
Learn
Extract a learning from the recent conversation and add it to the appropriate instruction file
/learn
Learn
Extract a learning from the recent conversation and add it to the appropriate instruction file
/create-pipeline
create-pipeline
Create a new pipeline from a task description. Fans out agent, skill, and hook scaffolding in parallel, then integrates into the routing system.
/d
D
Jev-first router: A/B variant of /do. One TypeSafe call replaces the manifest read; falls back to /do when unavailable or unconfident.
/do
Do
Smart router: classify requests and route to the correct agent + skill
/generate-claudemd
Generate claudemd
Generate project-specific CLAUDE.md from repo analysis.
/github-notifications
Github notifications
Triage GitHub notifications: fetch, classify, report actions needed.
/github-profile-rules
Github profile rules
`github-profile-rules` — extract programming rules and coding conventions from a GitHub user's public profile via API.
/gm-brilliant-implementation
Gm brilliant implementation
Run the complete 34-stage implementation workflow for a large, multi-system, multi-wave, or CPU-delegated 5 Star Booker GM program.
/install
Install
Verify installation, diagnose issues, and guide first-time setup
/pr-review
Pr review
Comprehensive PR review using specialized agents, with automatic retro knowledge capture
/reddit-moderate
Reddit moderate
Reddit moderation: fetch modqueue, classify content, take mod actions
/retro
Retro
Learning system interface: stats, search, graduate learnings. Backed by learning.db (SQLite + FTS5).
/system-upgrade
system-upgrade
Systematic upgrade pipeline for adapting agents, skills, and hooks when Claude Code ships updates, user goals change, or retro learnings accumulate.
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