LLM Mart Basic
@llm-mart · Joined Jun 2026
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
Publish any markdown file from the vault to Confluence with format conversion and approval gate
Turn a product release (the list of shipped items plus real screen recordings) into a motion recap video and one explained demo per feature, with sound effects tied to on-screen motion and a composed music bed. Deterministic HTML scenes rendered frame by frame, ElevenLabs for sou
CP-7 retrospective: audit checkpoints, evidence quality, action items, and harvest candidates. Closes the V-model cycle and feeds the next run. Use via /retro after ship, escalate, or significant session.
Produce and continuously maintain ONE living review document for a multi-item session — a cockpit header (Progress checklist, Working folder, Context) plus per-item review cards that you approve or request changes on directly in the doc or side panel. Use whenever a session has m
Evaluate URLs and tools — check vault coverage, assess relevance, recommend save or skip
Deterministic pre-publish scan that refuses AI-slop tells in anything about to be written, published, or sent: files, artifacts, slide titles, table headers, diagram labels, chat messages, commit messages. Use before publishing or sending any deliverable, in CI, or wired as a Cla
Anti-slop frontend skill for landing pages, portfolios, and redesigns. The agent reads the brief, infers the right design direction, and ships interfaces that do not look templated. Real design systems when applicable, audit-first on redesigns, strict pre-flight check.
Generate daily team intelligence brief by cross-referencing GitHub, Linear, Slack, PostHog, meetings, and braindumps with two-way Linear sync-back
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.
/improve-agent
Improve agent
Improve an existing agent through performance baselines, prompt engineering, A/B testing, and staged rollout
/multi-agent-optimize
Multi agent optimize
Optimize multi-agent system performance through profiling, context window tuning, coordination efficiency, and cost and latency tradeoffs
/team-debug
Team debug
Debug issues using competing hypotheses with parallel investigation by multiple agents
/team-delegate
Team delegate
Task delegation dashboard for managing team workload, assignments, and rebalancing
/team-feature
Team feature
Develop features in parallel with multiple agents using file ownership boundaries and dependency management
/team-review
Team review
Launch a multi-reviewer parallel code review with specialized review dimensions
/team-shutdown
Team shutdown
Gracefully shut down an agent team, collect final results, and clean up resources
/team-spawn
Team spawn
Spawn an agent team using presets (review, debug, feature, fullstack, research, security, migration) or custom composition
/team-status
Team status
Display team members, task status, and progress for an active agent team
/api-mock
Api mock
Build realistic API mock servers with request stubbing, dynamic data, test scenarios, and contract testing
/performance-optimization
Performance optimization
Orchestrate end-to-end application performance optimization from profiling to monitoring
/feature-development
Feature development
Orchestrate end-to-end feature development from requirements to deployment
/block-no-verify
Block no verify
Set up PreToolUse hook to block --no-verify and other git bypass flags in Claude Code projects
/c4-architecture
C4 architecture
Generate comprehensive C4 architecture documentation (Context, Container, Component, Code) for a codebase using bottom-up analysis and four coordinated C4 agents.
/workflow-automate
Workflow automate
Automate CI/CD pipelines, releases, and development workflows with GitHub Actions, pre-commit hooks, and infrastructure automation
/code-explain
Code explain
Explain complex code, algorithms, and design patterns with step-by-step breakdowns, visual diagrams, and interactive examples
/doc-generate
Doc generate
Generate API, architecture, code, and user documentation from a codebase and automate keeping it current
/context-restore
Context restore
Restore saved project context and decisions to resume a session
/refactor-clean
Refactor clean
Refactor provided code for cleanliness, maintainability, and alignment with SOLID principles and modern best practices — no over-engineering.
/tech-debt
Tech debt
Analyze and remediate technical debt — inventory debt items, score by impact, and produce a prioritized remediation plan with estimated effort.
VCP 部署在 AI 模型 API 与前端应用之间,是面向AGI OS开发和探索的工业级基建示范项目。通过统一指令协议、多层级持久化记忆、分布式插件引擎及多 Agent 协作框架,将原本“无状态、无记忆、无工具调用能力”的大语言模型,彻底改造成拥有永久自我意识、物理世界操作权及群体协作智能的完整智能体系统。
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