LLM Mart Basic

Code-health scan — dead code, bug-prone logic, resource leaks, concurrency bugs, silent failures, input-boundary issues, doc rot. Triggers on: "/rot-canary", "rot-canary", "code-health" (legacy aliases: "/rotcanary", "rotcanary"). Auto-runs at session end on touched files (QUICK,

Performance complexity and resource allocation canary — checks for O(N^2) loops, database N+1 query patterns, memory leaks (unbounded collections), and blocking calls in main event loop. Triggers on keywords: "/scale-canary", "scale-canary", "performance audit", "scale audit". Us

Verify version-sensitive facts against live authoritative sources before asserting them in code or answers. Triggers on: "/source-grounding", "source-grounding", "sourcing". Standing rule — always active via CLAUDE.md. Invoke for deep verification work (API signatures, CVEs, mode

Software supply chain audit — dependencies (CVEs, maintenance, licenses, transitive risk), build/CI integrity (SHA-pinned actions, lockfile, CI-only release), artifact integrity (checksums, signing, SBOM). Triggers on: "/supply-chain-audit", "supply-chain-audit", "dependency audi

Observability and structured logging canary — checks for structured logs (JSON), OpenTelemetry metrics/traces, proper error stack traces, and flags empty catches or silent log swallowing. Triggers on keywords: "/telemetry-canary", "telemetry-canary", "observability audit", "struc

Testability and design decoupling canary — checks for tight coupling, lack of Dependency Injection (DI), hardcoded constructors, Single Responsibility Principle (SRP) violations, and mockability gaps. Triggers on keywords: "/testability-canary", "testability-canary", "testability

Automate controlled local ArcGIS Pro and ArcPy workflows through arcgis-mcp-bridge: inspect .aprx projects and file geodatabases, run geoprocessing, projection, raster, network, spatial-statistics, editing, symbology, and layout export with path and mutation guards. Use when the

Always invoke before answering any request to create, compare, design, or review a user-facing map, even if the request is terse or underspecified. Covers publication maps, choropleths, map series and small multiples, comparable multi-date panels, proportional/bivariate/flow maps

Change analysis, once the observations are comparable. Not for cases whose blocker is comparability itself: mixed sensors, product levels or processing baselines to remote-sensing-analysis, undocumented vertical datums to point-cloud-lidar, multi-decade archive trends over large

Always invoke when geospatial data must be acquired, prepared, repaired, scaled, or moved through a repeatable pipeline. Covers open-data/OSM/STAC acquisition, spatial formats, CRS transforms, quality checks, and batch ETL architecture for growing or recurring joins. Invoke along

Invoke before recommending, training, or auditing a neural method for geospatial imagery, including vision transformers, U-Net/DeepLab/SegFormer, object detection, pixel classification, building/road extraction, and EO foundation-model fine-tuning. Also invoke for neural chip-spl

Route genuinely ambiguous or multi-stage geospatial work across specialist skills while enforcing shared CRS, validity, leakage, units, verification, and reproducibility rules. Use for requests spanning multiple stages such as acquisition, imagery, modeling, analysis, and map del

Turn scattered point measurements into continuous surfaces with quantified uncertainty: variogram modeling, ordinary/universal/regression kriging, IDW, and spatially honest cross-validation. Use when unobserved values must be estimated from sparse samples such as stations, wells,

Invoke when Earth Engine, GEE, ee., or geemap is named; when work needs its server-side catalog; or when choosing Earth Engine versus local xarray or desktop processing for a large area or long archive. Covers image collections, masking, compositing, reducers, zonal statistics, t

Always invoke for spatial suitability, site selection, AHP, criteria weights, or weighted-overlay work, including audits of inconsistent pairwise judgments and requests for only a final map. Covers consistency, standardization, constraints, ranked surfaces, shortlists, and sensit

Always invoke for training, validating, tuning, benchmarking, or claiming readiness of a predictive model. Covers leakage audits, spatial and grouped splits, metrics, reproducibility, and honest reporting. Invoke especially when spatial dependence, split design, or deployment geo

Movement and trajectory analytics from GPS/GNSS tracks: cleaning, stop/trip detection, road-network map matching, speed/direction, flow aggregation, and origin-destination construction. Use for fleets, human mobility, animal tracking, AIS, or sports tracks. Trigger on GPS points,

Always invoke for access to facilities or opportunities by walking, driving, cycling, or public transport, even for a conceptual question with no routing terms or data yet. Covers hospital and service access, transit/GTFS, routes, isochrones, OD matrices, closest facility, 2SFCA,

LiDAR and point cloud processing: PDAL pipelines, LAS/LAZ/COPC handling, ground classification, DTM/DSM/CHM generation, canopy and building metrics, and photogrammetric (SfM) point clouds. Use when the primary input is LAS, LAZ, COPC, LiDAR, or an unstructured 3D point cloud. Thi

Invoke whenever spatial SQL or its execution backend is the decision: PostGIS, DuckDB Spatial, SpatiaLite, ST_* functions, recurring spatial joins, concurrent/growing workloads, or large GeoParquet queries. Covers backend selection, schemas, GiST/BRIN indexes, KNN, geometry versu

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.

Scan for codebase bloat using 3-tier progressive analysis: dead code, duplication, God classes, and documentation waste.

Review the current working diff against the elegant-code decision ladder and propose deletions, honoring the negligence floor.

Suggest tier-1 filter commands for a log file before any compression or paste. Anchors on the log-debugging-hygiene module.

Analyze and optimize context window usage using MECW principles

Remove dead code, duplicate files, and unused dependencies with user approval at each step. Backs up before deleting.

The ONLY way to stop the egregore. Human-initiated graceful shutdown that saves all state.

Install the egregore watchdog daemon for automatic session relaunching

Show current egregore state and progress

Summon the egregore to autonomously process work items through the full development lifecycle. Runs indefinitely by default until dismissed.

Remove the egregore watchdog daemon and clean up files

Add or edit a knowledge annotation

Rebuild the knowledge base from the current codebase

Build, search, and query the code knowledge graph

Start or resume a guided onboarding path

Show challenge accuracy stats, weak areas, and streak

Run an ad-hoc gauntlet challenge session (5 questions, random scope)

Interactive interface to enable/disable rules

Convert Python SDK hooks to declarative rules

Display help and documentation

Create behavioral rules to prevent unwanted actions

Ultra-lightweight, open-source, self-hosted personal AI agent framework in Python with WebUI, tools, memory, MCP, multi-agent workflows, automation, and chat ap…

Governance framework for AI coding agents. It runs them through a five-step workflow (plan, build, review, test, ship) where no step counts as done without evid…

Ultimate Multi-Agent OS for Autonomous AI NPCs 2026

Personal AI Agent Hub 2026 — Build Your 24/7 Autonomous Assistant

Proven 2026 Multi-Agent AI Review System – Verdict-Driven Quality Control

Slash API Batch: Cut AI Costs by 50% in 2026

Web dashboard for Hermes Agent — multi-platform AI chat, session management, scheduled jobs, usage analytics

Agent Skills for Solopreneurs

AirLLM dramatically reduces inference memory usage, letting 70B large language models run on a single 4GB GPU card

Zero, your trustworthy AI teammate for real work.

一套 DSH runtime，Desktop、Web 与 TUI 三种开发体验。

Open-source operational advisor for ClickHouse — real-time monitoring plus AI-driven index/partition/materialized-view recommendations.

⚙️ TypeScript Style Guide and Agent Skill. A concise set of conventions and best practices for consistent, maintainable code.

Framework for AI agents to build and maintain a digital brain through Obsidian wiki

Apache Maka (Incubating) is a local-first AI agent workspace. Model messages, tool calls, tool results, permission decisions, and termination events are recorde…

Neo.mjs is a self-evolving software organism: a professional end-to-end AI engineering team whose cross-model swarm inhabits live apps via Neural Link, Active H…

Agentic development harness for Claude Code — SPEC-driven plan/run/sync, TRUST 5 quality gates, model+effort routing, and Claude×GLM multi-LLM cost control. Sin…

NocoBase is an open-source AI + no-code platform for building business systems fast. Instead of generating everything from scratch, AI works on top of productio…

An open-source AI coding agent that lives in your terminal.

PawWork — free, open-source desktop AI agent for macOS and Windows. Alternative to Codex App and Claude Cowork. BYOK with 75+ providers, ChatGPT OAuth, local mo…