LLM Mart Basic

Full-funnel paid advertising operations for B2B SaaS. Use this skill for PPC strategy, Google Ads optimization, LinkedIn Ads, social media advertising, programmatic buying, creative strategy, attribution modeling, budget allocation, budget pacing and planned-vs-delivered reconcil

Partner, channel, and ecosystem marketing for B2B SaaS. Use this skill for co-marketing with technology alliances and channel partners, integration launches, joint campaigns and content, partner tiering and enablement, and cloud-marketplace go-to-market (AWS, Azure, GCP listings,

Product marketing and go-to-market strategy for B2B SaaS launches and positioning. Use this skill when planning a product launch, developing positioning and messaging, analyzing competitive landscape, building customer advocacy programs, designing a customer referral program (who

Complete B2B SaaS marketing agency powered by 81 AI agents across 17 specialties. This is the entry point for ALL marketing requests. Routes to the right specialist team: content marketing, SEO, paid media, social media, email marketing, design, sales enablement (including techni

End-to-end SEO operations for B2B SaaS organic visibility. Use this skill when you need keyword research, technical SEO audits, content optimization, link building strategy, international expansion, AI/AEO optimization, schema markup, Core Web Vitals improvements, and organic tra

Babysitter enforces obedience on agentic workforces and enables them to manage extremely complex tasks and workflows through deterministic, hallucination-free s…

The most expensive bug in AI-assisted building is shipping the wrong thing, well. A CLAUDE.md for PM + eng teams using Claude Code- 7 principles for problem fra…

AI agent skills manager for Claude Code, Cursor, Codex and more — install, sync, and manage skills from one desktop app.

Business model classification, business model analysis, structural analysis of how a company makes money, product offering decomposition, distribution channel analysis, customer segment analysis, revenue model identification, market sizing TAM SAM SOM, competitive positioning, bu

Competitive landscape analysis, competitor comparison, peer positioning, market share dynamics, competitive moat assessment, Porter five forces, industry competition, competitive advantage analysis, market positioning, strategic group mapping, compare competitors

Earnings sentiment analysis, analyst estimates vs guidance, earnings surprise history, consensus sentiment, earnings revision trends, analyst rating changes, earnings beat miss track record, guidance accuracy, whisper numbers, pre-announcement sentiment

Recent quarter performance analysis, quarterly earnings review, last quarter results, quarterly financial performance, analyze recent quarter, Q4 earnings, quarterly revenue breakdown, EPS this quarter, margin analysis recent quarter, sequential growth, quarterly performance revi

Risk analysis, regulatory risk assessment, competitive risk, macro risk, technology risk, litigation risk, financial risk assessment, enterprise risk, operational risk, geopolitical risk exposure

Secular technology trends, technology adoption cycle, disruption risk, AI impact analysis, digital transformation, industry 4.0 trends, technology moat, innovation trajectory, R&D effectiveness, tech competitive positioning

Turnaround analysis, stagnation detection, performance inflection, operational improvement, restructuring analysis, management change impact, cost cutting effectiveness, business transformation, recovery trajectory, operational metrics improvement

Valuation methods analysis, DCF inputs, comparable multiples, P/E ratio, EV/EBITDA, price to book, valuation assumptions, relative valuation, intrinsic value, fair value estimate

Operational KPI tracking, headcount trends, utilization rates, backlog analysis, book-to-bill ratio, operational efficiency metrics, capacity utilization, productivity metrics, operational leverage, same-store sales

Revenue decomposition, segment breakdown, geographic revenue split, product-line waterfall, revenue mix analysis, business segment performance, divisional revenue, revenue concentration, customer revenue dependency, channel revenue analysis

Unit economics analysis, CAC LTV estimation, churn inference, gross margin per unit, customer economics, subscription economics, per-unit profitability, contribution margin, payback period, cohort economics

What-if scenario analysis, scenario tree construction, base bull bear case, sensitivity to macro variables, revenue scenario modeling, cost scenario analysis, margin impact scenarios, interest rate sensitivity, currency impact scenarios, commodity price scenarios

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.

Validate application configuration with schemas, per-environment rules, runtime checks, and secure handling of sensitive values

Preflight a DGX Spark system for an ML training or inference workload and emit env-report.json

Set up debugging and tracing with remote debugging, distributed tracing, debug logging, profiling, and production diagnostics

Generate API, architecture, code, and user documentation from a codebase and automate keeping it current

Analyze and resolve errors across the full application lifecycle — from stack traces to distributed tracing — using systematic root-cause analysis and observability tools.

Set up error tracking and monitoring — implement structured logging, configure alerts, and integrate with error tracking services for real-time error detection.

Coordinate specialized review agents in parallel or in sequence and synthesize their findings into one code review

Analyze and resolve errors across the full application lifecycle — from stack traces to distributed tracing — using systematic root-cause analysis and observability tools.

Set up error tracking and monitoring — implement structured logging, configure alerts, and integrate with error tracking services for real-time error detection.

AI-assisted smart debugging — parse error messages, stack traces, and failure patterns to identify root causes and produce a fix with automated observability steps.

Generate comprehensive migration plans and scripts for transitioning codebases between frameworks, languages, versions, or platforms with minimal disruption.

Plan and execute safe, incremental dependency upgrades with minimal risk — including breaking-change migration paths and proper test verification.

Orchestrate legacy system modernization using the strangler fig pattern with gradual component replacement

Scaffold React and React Native components with TypeScript, tests, styles, and Storybook stories

Scan React, Vue, Angular, and vanilla JavaScript code for XSS vulnerabilities and report fixes with secure coding examples

Orchestrate end-to-end full-stack feature development across backend, frontend, database, and infrastructure layers

Orchestrate git workflow from code review through PR creation with quality gates

Create a role-specific onboarding plan for a new team member, from pre-arrival setup through the first 90 days

Enhance a pull request with a generated description, review checklist, risk assessment, and test coverage report

Orchestrate multi-agent incident response with modern SRE practices for rapid resolution and learning

PiG (Pi in Go) is a faithful Go port of upstream Pi, the TypeScript codebase behind the Pi coding agent. It is a parity-bound translation, not a rewrite: upstre…

An AI Agent that lives in your pocket. Local-first and privacy focused.

Unofficial skill that teaches coding agents to build with TypeSafe AI's Jev: typed decisions, calibrated confidence, and prior art from 150+ community projects.

Adaptive Test-time Learning and Autonomous Specialization

Prediction-market trading engine — Wang Transform pricing on 291K+ contracts; paper-traded across Kalshi · Polymarket · Solana DFlow (Jito bundles) · 633 tests

Knowledge Management for Humans and Agents

Open-source Claude Cowork / Codex / WorkBuddy alternative — a local-first AI office agent that turns one request into real PPTX, DOCX, XLSX and HTML files. Runs…

DeepAgent Code: AI coding agent with persistent memory and control plane

Awesome Jev — evidence-graded index of TypeSafe System One: SDKs, MCP tools, agents, apps and open models. 20 languages, rebuilt every 2 hours.

CLI for Telegram — agent-friendly, daemon-based, with webhook event push.

AI deep-research agent that turns any question into a cited report: plans searches, reads real sources, verifies evidence. Self-hosted, multi-provider, Docker-r…

Event-stream AI Agent framework for building your persona bot 🍊

Give the agent a machine. Just not yours. Each AI coding agent gets its own isolated machine with root, Docker, and systemd - active defense detects and stops t…

Local Emperor-style AI agent with Vue WebUI, multi-provider LLMs, streaming chat, tools, skills, memory, and token telemetry.

Open-source AI reverse-engineering agent platform and MCP server for Ghidra, Frida, x64dbg and Rizin — automated PE/APK/binary analysis, CTF and malware researc…

"Never send a human to do a machine's job" - Open Source AI hacking agent

Prismer Cloud

My Personal Blog (Robotics)

Tau Coding Agent - like Pi, but twice as much

Open-source alternative to OpenAI Dots: self-hosted AI chat, tools, approvals, connectors, and computer tasks.