LLM Mart Basic

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

Always invoke for classical analysis, classification, validation, or comparability of satellite, aerial, or drone imagery. This skill owns sensor, product, processing-level and processing-baseline harmonization, including multi-date inputs; add change-detection only after compara

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.

One-screen inventory of skills, agents, commands, MCP servers, and hooks counts.

List the file-based memory store grouped by project (auto-memory) plus the CLAUDE.md files.

Show current Claude Code spend versus a budget number

Quick month-end spend projection from the daily trend

List the most expensive sessions pushing your spend up

Print the Agent Monitor dashboard URL and how to start/open it

Check Agent Monitor reachability and print UP/DOWN with latency

One-line Agent Monitor health + counts summary from /api/stats

Quick connectivity + health probe of the Agent Monitor dashboard.

Export Agent Monitor data (sessions/events/analytics/costs/all) as json/csv/md.

Show the latest N ingested events with timestamp, event_type, and tool_name.

List current cost and token outlier sessions via z-score

Compare two sessions side-by-side with cost and workflow deltas

Surface the top 3 data-backed insights about your Claude Code usage right now

Read-only inventory of CCAM alerts, webhook targets, and remote sources

CCAM platform status across hooks, config, updates, and MCP prerequisites

One-screen focus snapshot — avg turn duration, thinking-block usage, and longest sessions.

Quick daily standup from today's Claude Code sessions — grouped by project, with cost and errors.

Suggest the next action from your most recent in-progress sessions and recent errors.

List the most recent APIError events with their session and a summary

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Android in docker solution with noVNC supported, video recording, mcp server and AI-agent

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runs anywhere. uses anything

Open-source dots for the web: an AI agent with its own browser, one that does not get blocked.

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Turn PDFs, books and papers into interactive learning webpages｜将复杂材料转化为可追溯、可测验、可做笔记的学习网页

PhysicsOS 是一个面向初高中物理学习的公益可视化智能体，通过 AI 理解题目并结合物理引擎，将抽象物理过程转化为可交互、可观察、可计算、可验证的真实物理场景。