LLM Mart Basic

Use when making a small, quick change — a bug fix, typo, minor feature, tweak, or anything the user calls "vibe coding" — that looks like 1-3 files in the same module

Make AI-generated writing read as human-written, in fiction and in professional prose. Repairs the narrative architecture of fiction and stories (based on StoryScope, arXiv:2604.03136); routes professional text through domain rules for release notes, announcements, PR and issue r

Use when a user explicitly requests Sepia recreate for a full rewrite.

Use when a user explicitly requests Sepia refactor for minimal in-place prose revision.

Use when a user explicitly requests Sepia review to diagnose prose without editing.

Use when a user explicitly requests Sepia write to create new prose.

Instructs agents to control Stratawright DAW via the daw-cli command-line IPC interface. Covers session state, transport, track management, gain staging, VST3/AU plugin hosting, signal routing, clips, MIDI timeline editing, and non-visual DSP analysis & audio intelligence. Requir

Adaptive 12-week training plan generator using Garmin Connect data. Creates structured workouts and schedules them on your Garmin calendar. Use this skill whenever the user asks about training plans, workout scheduling, race preparation, building fitness for upcoming events, or w

Garmin Connect CLI for activities, health, body composition, workouts, devices, gear, goals, and more.

Use when creating, refreshing, packaging, inspecting, promoting, or deprecating a named per-seat starting point — Agent Starter manifest authoring, the 6-state lifecycle (captured → named → inspectable → used → promoted → deprecated), provenance honesty, and refusal rules. NOT a

Use when designing or auditing how an agent becomes useful after launch — AGENTS.md overlays, role files, skills, rig specs, workflow specs, startup checklists, refocus messages, "rig context" surface. Covers the 4 failure modes that make startup context fail (old rig spec misses

Use when issuing `rig` commands against a remote host via `--host <id>` flag (single-hop SSH to a host declared in `~/.openrig/hosts.yaml`). Covers the 4 structured failure modes (ssh-unreachable / permission-gate / remote-daemon-unreachable / remote-command-failed), the `--verif

Use when classifying a slice closeout (auto-continue / human gate / park), routing a real decision to a human, or designing a human queue/dashboard surface. Treats humans as durable network participants with attention surfaces, queues, and decision records — escalation lands as a

Use when a human decision, access grant, external action, or escalation must survive the operator's absence, or when an orchestrator or PM has a judgment-worthy update for the operator.

Use when opening OpenRig fleet terminals into cmux — turning a rig, pod, mission, slice, or saved view into live agent tiles via `rig terminal --provider cmux`, or driving cmux on an agent's request. Same OpenRig view semantics as openrig-herdr (the verbs, honest-partial/degrade,

Use when opening OpenRig fleet terminals as a herdr wall — turning a rig, pod, mission, slice, or saved view into live interactive agent tiles via `rig terminal`, watching another rig read-only, or driving herdr on an agent's request ("open all my rigs + a mission as views"). Cov

Use when you're operating OpenRig and need the right skill or context for fleet recovery, seat handover, new-seat orientation, a watchdog wake, cross-host reach to an agent on another machine, rig packaging, an OpenRig upgrade, systematic debugging, queue triage, or implementatio

Use when upgrading the OpenRig CLI/daemon on a host with running rigs, especially when preserving tmux-backed agent sessions through a daemon restart or documenting hot-upgrade SOP evidence.

Use when authoring or installing a rig bundle (packaged, shareable artifact that instantiates an opinionated OpenRig topology + workflow), reasoning about the bundle vs extension boundary, or auditing a bundle for portability. Covers the 4 failure modes that prevent bundles from

Use when reasoning about the rig lifecycle operations family (create / start / stop / resume / restore / snapshot / release / unclaim / destroy), reading or trusting `rig ps` / lifecycle projections after recovery, or designing proof for a lifecycle scenario. Covers the 4 failure

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 an existing agent through performance baselines, prompt engineering, A/B testing, and staged rollout

Optimize multi-agent system performance through profiling, context window tuning, coordination efficiency, and cost and latency tradeoffs

Debug issues using competing hypotheses with parallel investigation by multiple agents

Task delegation dashboard for managing team workload, assignments, and rebalancing

Develop features in parallel with multiple agents using file ownership boundaries and dependency management

Launch a multi-reviewer parallel code review with specialized review dimensions

Gracefully shut down an agent team, collect final results, and clean up resources

Spawn an agent team using presets (review, debug, feature, fullstack, research, security, migration) or custom composition

Display team members, task status, and progress for an active agent team

Build realistic API mock servers with request stubbing, dynamic data, test scenarios, and contract testing

Orchestrate end-to-end application performance optimization from profiling to monitoring

Orchestrate end-to-end feature development from requirements to deployment

Set up PreToolUse hook to block --no-verify and other git bypass flags in Claude Code projects

Generate comprehensive C4 architecture documentation (Context, Container, Component, Code) for a codebase using bottom-up analysis and four coordinated C4 agents.

Automate CI/CD pipelines, releases, and development workflows with GitHub Actions, pre-commit hooks, and infrastructure automation

Explain complex code, algorithms, and design patterns with step-by-step breakdowns, visual diagrams, and interactive examples

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

Restore saved project context and decisions to resume a session

Refactor provided code for cleanliness, maintainability, and alignment with SOLID principles and modern best practices — no over-engineering.

Analyze and remediate technical debt — inventory debt items, score by impact, and produce a prioritized remediation plan with estimated effort.

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.