LLM Mart Basic

Execute an implementation plan file produced by $draft-plan or $turboplan. Runs pre-implementation prep, then runs $implement to execute the steps and finalize once they are all done. Use when the user asks to "implement plan", "implement the plan", "execute the plan", "run the p

Interpret third-party feedback by running parallel internal and peer interpretations to surface intent, correctness concerns, and ambiguities. Use when the user asks to "interpret feedback", "interpret comments", "what does this feedback mean", "clarify reviewer intent", "underst

Systematically investigate bugs, test failures, build errors, performance issues, or unexpected behavior by cycling through characterize-isolate-hypothesize-test steps. Use when the user asks to "investigate this bug", "debug this", "figure out why this fails", "find the root cau

Deep architecture report that fans out parallel inspections across different aspects of the codebase (structure, tech stack, APIs, patterns, data flow, dependencies, testing) and synthesizes findings into a comprehensive document at .turbo/codebase-map.md and .turbo/codebase-map.

Capture an out-of-scope improvement opportunity so it doesn't get lost. Use when the user asks to "note improvement", "save improvement", "track this for later", "remember this improvement", "note this idea", "log improvement", "backlog this", or "park this idea". Also invoke pro

Developer onboarding guide that composes architecture mapping, tooling review, and agentic setup review with setup, troubleshooting, and next-steps agents to produce a comprehensive guide at .turbo/onboarding.md and .turbo/onboarding.html. Use when the user asks to "onboard me",

Run an independent peer review via Claude. Use when the user asks to "peer review", "peer review my code", "peer review my plan", "get a second opinion", or "independent review".

Fetch and rank open GitHub issues by community engagement, present the top 3 candidates, and plan implementation for the selected issue. Use when the user asks to "pick next issue", "next issue", "which issue should I work on", "top issues", "most popular issues", "prioritize iss

Stage, format, lint, test, review, smoke test, and re-run itself until stable. Use when the user asks to "polish code", "refine code", "iterate on code quality", "review loop", "clean up, test, and review loop", or "run the polish loop".

Stand up the project's live app and hand it to the user to try a change firsthand, then gate on their verdict before continuing. Use when the user asks to "preview the change", "let me try it", "spin up the app so I can test it", "set it up so I can poke at it", or before finaliz

Recall the reasoning behind a past change from Codex session history when available, falling back to commit diff and surrounding code. Use when the user asks to "recall reasoning", "find reasoning", "look up reasoning", "recall implementation reasoning", "find the rationale", "wh

Iteratively review and revise a plan until no new findings survive evaluation. Use when the user asks to "refine the plan", "iterate on the plan", "tighten the plan", or "improve the plan".

Draft, confirm, and post a single conversational reply to GitHub PR conversation comments (issue comments). The reply addresses all tracked items in one natural-prose message. Use when the user asks to "reply to PR conversation", "post PR conversation replies", or "draft PR conve

Draft, confirm, and post replies to GitHub PR review threads. Handles per-category reply formatting, re-fetches thread resolution state so auto-resolved threads are skipped, and posts via GraphQL. Use when the user asks to "reply to PR threads", "post PR thread replies", or "draf

Choose an implementation path (direct or plan) for evaluated findings and dispatch it. Direct path applies fixes directly; plan path runs $turboplan. Use after $evaluate-findings has tagged findings and they need to be implemented, or when the user asks to "resolve findings", "ap

Evaluate, fix, answer, and reply to GitHub pull request review comments and conversation comments. Handles both change requests (fix or skip) and reviewer questions (explain using reasoning recalled from past Codex session history). Use when the user asks to "resolve PR comments"

Detect agentic coding infrastructure in a project: CLAUDE.md, AGENTS.md, installed skills, MCP servers, hooks, and cross-tool compatibility (Claude Code and Codex CLI). Returns structured findings about agentic readiness without applying changes. Use when the user asks to "review

Review code for bugs, security vulnerabilities, API misuse, consistency issues, simplicity problems, or test coverage gaps by running internal reviews and a peer review in parallel and returning combined findings. Single-concern with a type argument, or full review with no argume

Detect package managers and discover outdated or vulnerable dependencies. Returns structured findings without upgrading. Use when the user asks to "review dependencies", "check for outdated packages", "check dependencies", "scan dependencies", or "dependency review".

Review a plan by running internal reviews and a peer review in parallel and returning combined findings. Use when the user asks to "review my plan", "check my plan", "critique my plan", or wants feedback on a plan.

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.

Analyze git diffs or staged changes and generate conventional commit messages that explain WHY a change was made. Supports auto-detecting type and scope, intelligent file staging, and interactive overrides. Use when asked to "write a commit message", "generate a commit", "describe my changes", "commit this", "summarize my diff", or "/commit".

SOC 2 compliance for Terraform — gap analysis, control implementation, evidence collection, and remediation guidance mapped to SOC 2 Trust Services Criteria.

Generate, review, secure, and test composite GitHub Actions following best practices — full repo scaffold, interview-driven generation, PR creation on existing repos, SHA pinning, secrets-as-inputs, job summaries, and actionlint validation.

Set up and troubleshoot Datadog — Agent deployment on Kubernetes, APM instrumentation, Log Management, Monitors, Dashboards, SLOs, Synthetic tests, and live incident investigation using the Datadog MCP server. Covers Terraform-managed Datadog resources.

Structured platform troubleshooting — classifies the problem layer, collects evidence, forms a root-cause hypothesis, and proposes a fix with validation and rollback steps.

Generate, format, and validate code documentation — docstrings, JSDoc, OpenAPI/Swagger specs, documentation sites, and developer guides.

Measure, benchmark, instrument, and debug DORA metrics (Deployment Frequency, Lead Time for Changes, Change Failure Rate, MTTR) for production engineering teams. Covers GitHub Actions instrumentation, Prometheus recording rules, Grafana dashboards, incident source integration, SaaS tool selection, and anti-pattern detection. Use when asked to "instrument DORA metrics", "benchmark our deployment frequency", "why is my MTTR data missing", or "generate a DORA dashboard".

Deploy and configure Dynatrace — OneAgent Kubernetes Operator, code-level instrumentation, Log Monitoring, custom metrics, SLOs, Dashboards, anomaly detection, Davis AI, and live incident investigation using the Dynatrace MCP server. Covers Terraform-managed Dynatrace resources.

FluxCD entry point — routes to the right workflow based on what you need. Live cluster issue → structured 5-workflow debug trace. Repo health check → 6-phase audit (discovery, validation, API compliance, best practices, security). Helm chart review → helmchart. Starts by asking one question to confirm the right mode.

Design, review, secure, and debug GitHub Actions workflows — reusable workflows, OIDC federation, SHA pinning, token scoping, promotion orchestration, and CI failure diagnosis.

Flux CD and Argo CD — two modes. debug: five structured debug workflows for live clusters (installation, source, HelmRelease, Kustomization, ResourceSet) producing a five-section report. audit: six-phase read-only repo analysis (discovery, validation, API compliance, best practices, security) producing a prioritised Critical/Warning/Info report.

Scaffold, lint, review, security-audit, test, and upgrade-verify Helm charts. Runs an interactive interview to build production-ready charts from scratch. Covers chart structure, values design, schema validation, kubeconform, helm diff, and multi-environment scaffolding. Use when asked to "create a helm chart", "lint my chart", "review my helm chart", "check helm security", "generate values schema", "run helm diff", or "add helm tests".

Design, install, debug, review, plan capacity, audit scaling history, migrate from Cluster Autoscaler, and upgrade Karpenter v1.x on EKS. Covers NodePool, EC2NodeClass, NodeClaim, Spot diversity, disruption strategy, Pod Identity/IRSA, interruption queue, private clusters, AMI rotation, and GitOps integration. Use when asked to "set up Karpenter", "debug why nodes aren't provisioning", "review my NodePool", "what would Karpenter provision for this workload", "why did this node terminate", "migrate from CA", or "upgrade Karpenter".

Design, debug, and review KEDA ScaledObject/ScaledJob autoscaling. Covers all major scalers (Prometheus, SQS, Kafka, Redis, Cron, HTTP Add-on, Azure Service Bus), TriggerAuthentication, scaling lifecycle tuning, GitOps integration, and troubleshooting. Use when asked to "add KEDA autoscaling", "debug why my ScaledObject isn't scaling", "review my KEDA config", or "generate a ScaledObject for <trigger>".

Find, live-validate, map the blast radius of, and revoke leaked secrets with Kingfisher (MongoDB) — across a local repo, Git history, a GitHub/GitLab/Bitbucket org, S3/GCS, Docker images, Slack, Jira, Confluence, Teams, or Postman. Covers local CLI scanning, direct validate/revoke without a scan, baseline management (track only new secrets), kingfisher.yaml policy, CI diff-scan gates, and pre-commit/Husky hooks. Use when asked to "scan for secrets", "is this key still live", "what can this credential reach", "revoke this token", "did we leak a secret", or "block new secrets in CI". Pattern-only secret scan bundled with a CVE pass → /platform-skills:trivy. Secrets-context safety in workflow YAML → /platform-skills:zizmor. Storing/rotating secrets inside the cluster → /platform-skills:secrets.

Cluster baseline scaffolding, RBAC diagnosis and generation, workload hardening, and structured pod/scheduling debug for plain Kubernetes across all distributions.

Generate, test, audit, debug, and migrate Kyverno policies using the new CEL-based policy types (ValidatingPolicy, MutatingPolicy, GeneratingPolicy, ImageValidatingPolicy — all apiVersion policies.kyverno.io/v1). Covers matchConstraints, matchConditions, CEL validations/mutations, generator.Apply(), Audit→Deny promotion, PolicyException, kyverno-cli testing, and migration from legacy ClusterPolicy or PodSecurityPolicy. Use when asked to "write a Kyverno policy", "test a ValidatingPolicy", "audit my cluster for violations", "why is my policy not firing", or "migrate from ClusterPolicy".

Linkerd-specific diagnostics — mTLS verification, proxy injection issues, authorization policy debugging, traffic management, and multi-cluster connectivity problems.

Linux administration and networking diagnostics — DNS, load balancing, VPCs, kernel tuning, and connectivity troubleshooting.

MCP server and client development — scaffold, implement tools/resources/prompts, validate schemas, debug protocol compliance, and deploy with auth and rate limiting.

Make any song you can imagine

Leading AI-powered video generation platform that specializes in creating hyper-realistic talking avatars

Hermes Agent is an open-source, self-improving autonomous AI agent developed by Nous Research

Kilo Code is a popular, open-source AI coding agent and "agentic engineering" platform designed to help developers build, refactor, and debug software faster

General-purpose agent in one static Go binary. ReAct loop, ACP server for IDEs, OpenAI-compatible REST API with embedded web UI, Telegram gateway, cron schedule…

Autonomous agent framework with structured memory, safety hooks, and loop management. Built by the agent that runs on it.

TSP自托管、零运维的 A 股「选股 + 监控 + 回测」量化工作台 | 基于 TickFlow 数据源 | LLM能力驱使策略定制+个股分析+复盘 | 自由接入第三方数据源与个性化扩展数据 | 个人开源 ,非TickFlow官方项目

Curated, verified Agent Skills powered by ModelStudio.

Run Claude Code, Codex, Antigravity, Cursor Agent and OpenCode as one runtime — persistent sessions, multi-agent councils, an OpenAI-compatible endpoint, an MCP…

pi had nothing (nothing), so I made something (something) — sorry mariozechner-senpai, I went ahead and lovingly soiled your pure pi for you. opinionated fork o…

A persistent workspace for development work that self-improves and continues beyond one session.

Open-source memory and context for user-aware agents: scoped memory, provenance, retrieval quality, correction, boundaries, evals, and MCP/HTTP access.

📚 A zero-dependency, git-backed micro-lesson library for AI Agents to asynchronously share and search verified debugging experience. Python stdlib only. | http…

Deterministic, local-first memory and guardrails for AI coding agents with no LLM in the hot path.

Deterministic spec-orchestration for local LLMs in the pi coding agent — drives prompts through refine→research→grill→compose→critique, with bundled web/docs/fe…

Native Safari browser automation for AI agents. 97 tools via AppleScript — zero overhead, keeps logins, runs silently in background. Drop-in alternative to Chro…

Agent OS: keep specialist agents in a hub, spin up a temporary orchestrator per task. Local-first, works with any model.

Git for agent memory. Branches, diffs, PRs, and rollback for what your agents know.

Multi-Provider AI Gateway - No personal logs by design. Model autodiscovery, Failover groups, High availability, Android companion app, and more - "Because we h…

Production-grade MCP server for MikroTik RouterOS with secure AI-native network automation.