LLM Mart Basic

Use when the user asks which /phx: command fits a task (review, plan, debug, test) or how two commands differ. Checks plans and git state, then recommends from the routing table. Not for bare /help, a tour, or ambiguous requests (intent-detection).

Initialize plugin in a project — install Iron Laws, auto-activation rules, and reference auto-loading into CLAUDE.md. Use when setting up or updating the plugin.

Walk through the Elixir/Phoenix plugin commands, workflow, and features in 6 interactive sections. Use when a new user wants to learn what the plugin offers or needs a refresher on available commands.

Find the root cause of Elixir/Phoenix bugs — crashes, exceptions, stack traces, compile errors, LiveView that won't update, silent failures. Use when something is broken or misbehaves. --parallel for 4 tracks.

Capture Elixir/Ecto/LiveView lessons and Hex API rules. Use after corrections or when asked to document learning, record a lesson, prevent a fixed mistake, or remember package guidance with --library.

Build LiveView: async data (assign_async), PubSub (check connected?), phx-change events, form components/modals/uploads, streams for lists, live_patch. Use when handling interactions, debugging events, or tracking Presence.

Reduce mix output noise (5-15% token savings) by installing rtk filters that compress mix test/credo/dialyzer/compile output before it reaches Claude. Use when long mix output floods context.

Detect N+1 query anti-patterns specifically — Repo calls inside Enum/for loops, missing preloads on associations. Use when N+1 is explicitly suspected, NOT for unrelated Ecto questions or wider database performance.

Narrow bare rescue in Elixir so real errors like KeyError and typos propagate instead of being swallowed. Use to audit rescues and refactor error handling.

Use when writing, scheduling, testing or debugging Oban jobs, even for a how-to question: workers, cron, retries, unique jobs, queues, Pro Workflow/Batch. Load it before touching job code; it holds the job rules.

Use when anything in a Phoenix app is slow, times out, or uses too much memory, even if the cause looks obvious. Load it before analyzing or fixing: it runs measured Ecto, LiveView and OTP checks and ranks fixes.

Recommend safe Bash permissions for Elixir mix commands in settings.json. Use when permission prompts slow workflow, "fix permissions", "reduce prompts", "auto-allow mix".

Phoenix context design — creating/splitting contexts, Scope (1.8+), Ecto.Multi, PubSub, routers, plugs, controllers. Use when editing contexts, routers, or designing boundaries.

Address feedback left on a GitHub pull request: fetch unresolved review threads, make agreed Elixir/Phoenix code fixes, reply, and resolve. Use for a PR URL/number or reviewer comments. NOT for pre-PR review, findings triage, or CI monitoring.

Implement small Phoenix changes without planning — add validations, update routes, fix components, create migrations. Use for single-file edits under 50 lines.

Recall prior work from past sessions — how a bug was fixed, what was decided, where a pattern lives. Use when asked 'have we done this before' or 'how did I fix X' in Elixir/Phoenix work. ccrider MCP when available, else git + solution docs.

Research Elixir/Phoenix/Ecto topics or evaluate Hex libraries (--library). Use when learning about libraries, patterns, or comparing approaches. Searches HexDocs, ElixirForum, GitHub.

Analyze Elixir/Phoenix technical debt — duplicates, refactoring opportunities, credo issues. Use when asked about code quality, cleanup, or what to improve.

Write or repair Elixir tests with ExUnit, sandbox isolation, async reliability, Mox, ExMachina, and LiveViewTest. Use for test files, test setup, or failing/flaky tests. NOT for investigating an application bug outside the test suite.

Tidewave MCP runtime tools — debugging, smoke testing, live state inspection, SQL queries, hex docs. Use when evaluating code in a running Phoenix app.

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.

Secular technology trends analysis — technology adoption cycles, disruption risk, strategic positioning

Turnaround vs stagnation analysis — performance inflection detection, operational metrics, leadership impact

Valuation methods analysis — multiples, DCF inputs, PEG integration, valuation assumption extraction

Porter-style competitive positioning analysis — strategic group mapping, differentiation analysis

Peer benchmarking — multi-ticker financial comparison, growth/value matrix, z-score ranking

Sector overview — TAM estimation, competitive concentration (HHI), regulatory landscape

Supply-chain map — supplier/customer dependency, geographic concentration, bottleneck identification

Currency Analysis — macro strategy analysis

Macro Regime — macro strategy analysis

Rate Cycle — macro strategy analysis

3-statement integrated financial model — IS/BS/CFS triangulation, 5 historical + 5 forecast years

Audit an Excel workbook — formula errors, hardcoded cells, calculation arc cross-validation

Trading comps analysis — peer-group selection, trading-multiple triangulation, implied-valuation range

DCF valuation model — 5-10 year projection, WACC construction, sensitivity tables

Earnings preview presentation — 4-6 slide deck with consensus estimates, historical surprises, forward catalysts

LBO model — sources & uses, debt schedule, exit-multiple analysis, sponsor IRR sensitivity

Investment thesis pitch deck — 12-16 slide presentation with sourced footers

Sum-of-the-parts valuation — segment-level multiples, conglomerate discount analysis

XBRL-to-Excel — proper number formatting, frozen headers, named ranges, calculation arc cross-validation

Income Strategies — options and derivatives analysis

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.