DevOps Toolkit

Audit the configured MCP servers (user + project scope) via the Agent Monitor Config Explorer API: transport (stdio vs http), command/args and env variable names, headers, and the source file each definition came from. Reads /api/cc-config/mcp

Inventory the installed skills and which plugins contribute them, then flag overlap with the user's own skills — read through the Agent Monitor Config Explorer API. Reads /api/cc-config/skills and /api/cc-config/plugins

Review the configured cost alert rules and the alerts currently fired on the Agent Monitor dashboard, then explain exactly what tripped and why. Uses /api/alerts (fired feed) and /api/alerts/rules (definitions)

Run a daily spend check-in against a daily budget on the Agent Monitor dashboard — today's spend vs the daily target, pace through the day, and the projected overage if the current pace holds. Uses /api/pricing/cost and /api/sessions (grouped by started_at = today).

Forecast Claude Code spend to the end of the week or month from the daily session trend on the Agent Monitor dashboard — moving average of daily spend × days remaining, added to spend-to-date. Uses /api/analytics daily_sessions, /api/pricing/cost, and /api/sessions for a per-day cost curve

Trace the full event chain for one Claude Code session into an ordered timeline of every event type with tool_name and summary, highlighting gaps, out-of-order events, and failures. Reads /api/events?session_id= and /api/sessions/:id from the Agent Monitor dashboard

Detect quality and efficiency regressions over time using Agent Monitor data — rising error rate (APIError events), falling cache hit rate, growing compaction frequency, and climbing cost-per-session.

Compile a month-over-month retrospective from Agent Monitor data — sessions, cost, token volumes, completion rate, top projects by working directory, and notable shifts versus the prior month. Uses daily_sessions/daily_events (365d) from analytics, the session list, and the pricing cost breakdown

Produce a detailed report on APIError events from Agent Monitor data — counts over time, which sessions and models are affected, and the likely root cause (rate limits, overload/529, or context-window pressure) inferred from each event's summary and data payload

Scan recent Claude Code activity for errors and failure signals across all sessions using Agent Monitor data — APIError events and PreToolUse→PostToolUse gaps (tools that started but never completed) — then group failures by tool and model and rank them by frequency

Define and check simple service-level objectives for Claude Code from Agent Monitor data — session completion rate, tool success rate (PostToolUse/PreToolUse), and error rate (APIError/total) — then compare each to its target and report the error budget remaining

Roll up Claude Code sessions by working directory (project) from Agent Monitor data — session count, total cost, total tokens, and last-active timestamp per cwd — so per-project activity can be compared at a glance

Identify stale and empty Claude Code sessions in the Agent Monitor and explain the cleanup endpoint (POST /api/settings/cleanup), always showing the exact list of what WOULD be removed before anything is deleted.

Find Claude Code sessions tracked by the Agent Monitor by project (cwd), model, status, or date, then rank the matches by cost or recency. Pulls the session list and the distinct cwd / facet values so filters use real values rather than guesses

Render an ordered timeline of one Claude Code session's events (every event type) with per-event durations and tool names, reconstructed from Agent Monitor data. Pairs PreToolUse with PostToolUse to compute tool durations and surfaces gaps, errors, and compaction points

Walk a Claude Code session transcript turn-by-turn from Agent Monitor data, summarizing each user, assistant, and tool message in order so a long conversation can be reviewed quickly. Anchors the recap to the session header (model, cost, turn_count)

Plans zero-downtime migrations with compatibility validation, rollback strategies, and phased execution plans

Real-time Claude Code usage monitor with predictions and warnings

Dockhand MCP sidecar: adds MCP capabilities to manage dockhand by agents

Open-source CLI that measures AI coding-assistant impact (Claude Code, Copilot, Cursor, Aider) across Bitbucket+Jira, GitHub, GitLab, Azure DevOps, or Linear — and renders a reveal.js exec deck. Runs locally, no SaaS, no data leaves your environment.

Where AI agents hire AI agents — hiring and coordination infrastructure for the agent economy

Ultimate Context Engineering Infrastructure, starting from MCPs and Integrations

MCP server exposing SSH control for Linux servers via Model Context Protocol.

Deploy and use Cursor - The AI Code Editor directly in a web browser

Use when packaging Docker deployments as 1Panel local app store apps, including GitHub projects, docker-compose.yml files, docker run commands, app metadata, version directories, icons, README files, and 1Panel validation.

Source for the drop2run CLI and MCP server — publish a static site or an agent's output to an HTTPS link, no git and no build config.

A curated list of awesome developer tools and services — from cloud platforms and IDEs to AI-powered coding assistants and productivity utilities.

语雀 Model Context Protocol (MCP) 代理服务器

Mastermind monitor — long-running executor that watches Linear, GitHub, Monotask boards and folders for new tasks, claims and runs them with the right agent, and reports progress.

闲鱼商品监控与捡漏工具。定时搜索闲鱼，发现新商品后推送通知。Use when: user wants to monitor 闲鱼/Xianyu/Goofish for specific items, set up price alerts, track second-hand goods, or find rental listings on 闲鱼. Supports keyword search, price range, text filters (exclude/include), time filter, dedup via SQLite.

Self-host Supabase in production, AI-ready: read-only MCP for your coding agent with zero secret exposure, SSO, PITR backups, monitoring, disk encryption, and one-command migration off Supabase Cloud.

Generate and run real-infrastructure pytest tests for a User Story.\ \ Reads acceptance criteria from docs/USER-STORIES.md, picks the deepest testable\ \ layer (integration > agent > unit \u2014 E2E only for full scan flows), presents\ \ a test plan for approval, then writes and runs tests against r…

MCP server that gives AI assistants access to terminal, files, git, services, databases, monitoring, and deployment on servers.

Use when running ANY terminal command - enforces clear progress logging, output reading, and error-stop behavior so terminal processes are never left unchecked

Self-hosted knowledge for humans and agents.

The ultimate home lab dash with agentic AI features bolted in. Single sign-on, HTTPS enforced, dockerised and modular. Can be run completely offline too!

Open-source AI study companion — Google Classroom sync, Gemini AI plans, SRS flashcards, GPA predictor, AI quiz, MCP server for Claude Code. Try demo at shiori-v1.vercel.app

Cloud-agnostic Kubernetes infrastructure with Terraform & Helm for homelabs, edge, and production clusters.

Cloud-agnostic Kubernetes infrastructure with Terraform & Helm for homelabs, edge, and production clusters.

Cloud-agnostic Kubernetes infrastructure with Terraform & Helm for homelabs, edge, and production clusters.