LLM Mart Basic

Align a fine-tuned model with preference data using DPO, ORPO, KTO, or SimPO. Use when preference pairs or thumbs-up/down feedback exist, when choosing between preference-optimization methods, or when a DPO run needs hyperparameters or debugging.

Export a promoted fine-tuned model in the right deployment format — merged safetensors, LoRA-only, GGUF with imatrix, or FP8. Use after a checkpoint passes promotion, when choosing a quantization format for a target device, or when an exported model fails its smoke test.

Convert evaluation traces and production logs into SFT examples and preference pairs. Use when graded traces or failure examples exist and need to become training data, when applying rejection sampling to model outputs, or when building DPO pairs from passing and failing runs.

Fine-tune vision-language models (VLMs) with supervised learning on image+text data. Use when adapting a VLM to a visual domain or task, configuring frozen-vision-tower LoRA, or debugging a VLM fine-tune that trains without learning.

Build end-to-end MLOps pipelines from data preparation through model training, validation, and production deployment. Use when creating ML pipelines, implementing MLOps practices, or automating model training and deployment workflows.

Design composable recommendation, ranking, and feed pipelines using the six-stage Source→Hydrator→Filter→Scorer→Selector→SideEffect framework popularized by xAI's open-sourced X For You algorithm. Use when building any system that picks "the top K items for a (user, context)" — c

Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.

Implement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts.

Master dbt (data build tool) for analytics engineering with model organization, testing, documentation, and incremental strategies. Use when building data transformations, creating data models, or implementing analytics engineering best practices.

Optimize Apache Spark jobs with partitioning, caching, shuffle optimization, and memory tuning. Use when improving Spark performance, debugging slow jobs, or scaling data processing pipelines.

Create structured incident response runbooks with step-by-step procedures, escalation paths, and recovery actions. Use this skill when building a service outage runbook for a payment processing system; creating database incident procedures covering connection pool exhaustion, rep

Master on-call shift handoffs with context transfer, escalation procedures, and documentation. Use this skill when transitioning on-call responsibilities between engineers and ensuring the incoming responder has full situational awareness, when writing a shift summary that captur

Implement distributed tracing with Jaeger and Tempo to track requests across microservices and identify performance bottlenecks. Use when debugging microservices, analyzing request flows, or implementing observability for distributed systems.

Create and manage production Grafana dashboards for real-time visualization of system and application metrics. Use when building monitoring dashboards, visualizing metrics, or creating operational observability interfaces.

Set up Prometheus for comprehensive metric collection, storage, and monitoring of infrastructure and applications. Use when implementing metrics collection, setting up monitoring infrastructure, or configuring alerting systems.

Define and implement Service Level Indicators (SLIs) and Service Level Objectives (SLOs) with error budgets and alerting. Use when establishing reliability targets, implementing SRE practices, or measuring service performance.

Implement GitOps workflows with ArgoCD and Flux for automated, declarative Kubernetes deployments with continuous reconciliation. Use when implementing GitOps practices, automating Kubernetes deployments, or setting up declarative infrastructure management.

Design, organize, and manage Helm charts for templating and packaging Kubernetes applications with reusable configurations. Use when creating Helm charts, packaging Kubernetes applications, or implementing templated deployments.

Create production-ready Kubernetes manifests for Deployments, Services, ConfigMaps, and Secrets following best practices and security standards. Use when generating Kubernetes YAML manifests, creating K8s resources, or implementing production-grade Kubernetes configurations.

Implement Kubernetes security policies including NetworkPolicy, PodSecurityPolicy, and RBAC for production-grade security. Use when securing Kubernetes clusters, implementing network isolation, or enforcing pod security standards.

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.

Repeatable pass upgrading an Angular admin dashboard into a compact black-and-cyan developer-cockpit PWA

Run the drift-detection checklist (incl. agent-drift signals); report + fix in-turn

Orchestrate the final review fan-out (integration + diversity + risk + release readiness)

Run the AI-augmented lint self-improvement loop on the current project. Scans `.lint-history/` for recurring violation patterns (≥3 hits in 30d window), drafts a Claude-ready prompt to author a new semgrep rule for the top candidate, and surfaces the proposal under `.lint-history/proposals/<ts>.md`. Non-blocking analysis. See rules/lint-doctrine.md § Self-improving.

Bootstrap industry-leading lint+autofix+commit-hygiene stack on the current project. Drops in lefthook, oxlint, ESLint, Prettier, Stylelint, markdownlint, ruff, shellcheck, shfmt, yamllint, hadolint, actionlint, jscpd, knip, semgrep, gitleaks, commitizen + git-cz-emoji (emoji-mandatory commits), and semantic-release. Idempotent — re-runs upgrade safely. See rules/lint-doctrine.md.

Surface all retrospective documents with key shape metrics; compare arcs deliberately.

Progressive multimedia enrichment pass — add high-value audio/video/image/interactive to a site, run again and again

Run the autonomous-engineering operating loop on a task (plan→implement→verify→repair→report)

Capture the cumulative output of a /loop arc into a single auditable retrospective document; scans the heymegabyte-claude-skills plugin for modified files, categorizes by directory, counts LOC delta, extracts tool counts from MCP servers, and writes a timestamped report to retrospectives/

Turn a comma-separated list of file paths into a fully structured Pattern A agent brief — ordered writes, per-file schemas, and a verification step baked in.

Turn Pattern B from agent-resilience-discipline into a one-keystroke agent brief for a single-file deliverable < 300 lines.

Chain the full Superpowers process flow — brainstorm → plan → worktree → build → review → finish — on one slash command

Generate a timestamped arc retrospective from the past 7 days of git history in `~/.agentskills`.

Review ~/.claude/CLAUDE.md + rules for contradictions, stale guidance, duplication; consolidate

Batch-run all LLM eval cases in tools/evals/cases/*.json; aggregate pass/fail, cost, regression vs last run; exit nonzero in CI mode

One-line SaaS — from a description, scaffold a complete CF-native multi-tenant SaaS (Hono + D1 + Drizzle + Better Auth + Stripe + shadcn) deployed to a real URL

Unified supply-chain audit. Checks GitHub Actions SHA-pinning (`sha-pin:check`), package.json git+https deps (per `no-gitlab-megabytelabs-deps` semgrep), gitleaks scan, and trufflehog verified-only sweep. Surfaces any tag-mutable, git-URL, or secret-exposed surface. Per rules/ai-agent-security.md § Supply chain.

Run a learning pass after a major run; fold reusable lessons into global config

Summarize recent CHANGELOG.md entries for context restoration. Parses the canonical heading shape `## YYYY-MM-DD — pass-N — summary`. Filters: last N (default 10), YYYY-MM date prefix, or "today". Supports --json for machine-readable output.

Run quality-scores + token-budget + dep-graph, interpret results, flag missing budgets, orphans, and oversize skills

MCP server for reusable prompt templates, multi-step workflow chains, and quality gates. Compose agentic workflows with an operator syntax; export as native ski…

中国股票数据基础设施，覆盖行情、研报、资金面、筹码、公告、龙虎榜、ETF/期权、舆情互动、基本面、行业板块、宏观与风险事件等核心数据。42+A股数据集，日更、自托管、MCP 原生，零注册、零 API Token

OKF (Open Knowledge Format): Durable, structured memory for AI agents. Author, validate, consume, and maintain portable knowledge bundles through an ecosystem o…

토스증권을 AI 에이전트와 터미널에서 다루는 도구. CLI 와 MCP 서버로 계좌·시세·주문은 물론 웹앱 전용 기능(수급·AI 시그널·스크리너·배당)까지, JSON·CSV 구조화 출력으로 AI 도구·자동화에 바로 연동.

Claude skills for LinkedIn. 11 Claude Code and Codex skills that write human-sounding LinkedIn posts, craft comments that get noticed, analyze your feed, and bu…

MCP server connecting AI agents to ITASCA engines (PFC, FLAC, 3DEC, MPoint, MassFlow) — run geotechnical & geomechanics simulations through natural conversation

A curated list of amazingly awesome articles, people, applications, software libraries and projects related to the knowledge management space

One local dashboard for every Claude Code, Codex, Cursor, Antigravity, and Kilo Code session on your Mac. Spawn in parallel, ship in parallel. Open source, MIT.

Building blocks for frontier OpenAI agents in Rust. Nanocodex empowers you with Codex-level performance anywhere.

The AI agent that lives in your framework/browser

Self-Programming AI Assistant. Capture, automate, and refine all your workflows.

Consider it done. The open-source AI agent that works out of the box · 想到，就能做到。开源、开箱即用的 AI Agent。

Make Any Website into CLI & Use your logged-in browser by AI agent.

ISO 24495 Plain Language skills and Claude Code plugin

Deterministic, fail-open tool routing for Hermes Agent with an optional local-model fallback.

1인 사업가 생산성 키트 — 직원 없이 49개를 자동화했고, 그중 바로 쓸 수 있는 AI 에이전트 스킬 26개(+실행 스크립트)를 공개합니다

AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agent…

The open-source, self-hosted video conferencing software. Scalable, customizable, and with a powerful AI Meeting Agent.

Business operating system for Claude Code — 57 skills, 21 agents, smart daemon. Unified inbox (WhatsApp/Email/Slack/Telegram), autonomous PR merge, full-AWS mon…

A local-first, cross-platform Electron desktop workspace for Pi Coding Agent, with sessions, project files, browser tools, skills, plugins, and messaging integr…