LLM Mart Basic

Build native-feeling, benchmark-quality mobile app screens (Expo / React Native). Use when designing or implementing any mobile UI — screens, flows, onboarding, paywalls, tab bars, sheets, settings, empty states — or when polishing motion, gestures, navigation, typography, dark m

Use the Appllama MCP (mcp.appllama.io) well — research real top-grossing mobile apps, their screens, flows, and UI elements, then build from what you learn. Load when the Appllama MCP is connected and the task involves building a mobile app or screen, researching app design patte

Direct game art and creative vision. Turn GAME_DESIGN into a production-level ART_DIRECTION defining a recognizable visual style, camera and composition, world and character grammar, functional colour/light/material, HUD feedback, motion and transition specs, audio direction, and

Build the game for its approved target runtime. Compress GAME_DESIGN and ART_DIRECTION into a minimal BUILD_BRIEF, hand it to the current coding agent or another strong model to implement a fully playable build, and iterate against real runs and captured evidence. Use for impleme

Design game concepts from a novel. From SOURCE_BIBLE and PRODUCT_BRIEF, generate three genuinely different directions on the dimensions still unlocked, then pick the most worthwhile playable prototype using hard vetoes and explicit trade-offs. Use for what game should this novel

Verify a game with evidence on its selected target runtime. Launch the actual build and prove real rendering, input, the core loop, at least one designed outcome, restart, and explicit limitations without dressing subjective fun up as a certain verdict. Use for test a generated g

Design game experience, systems, and levels. Converge the chosen concept into one GAME_DESIGN defining the player promise, core loop, how the world responds, the systems actually needed, level pacing, feedback, failure, and a fully playable prototype. Use for design the game worl

Deconstruct a novel for game adaptation. Compress a raw novel, deconstruction library, or writing project into a SOURCE_BIBLE with cited textual evidence, extracting world rules, player verbs, spaces, character will, systems, and visual anchors — without inventing a genre yet. Us

Turn a novel into a fully playable game on the selected target platform. Orchestrates the whole adaptation pipeline — requirements intake, gameable deconstruction, concept selection, world and visual design, target-runtime build, and evidence-based QA — for a novel in any languag

Imported from ikaijua/awesome-aitools/docs/agents/README-CN.md.

Imported from ikaijua/awesome-aitools/docs/agents/README.md.

Maintenance of the contributor issue pipeline for JSONbored/metagraphed — closing issues that are already done but not marked so, and keeping the contributor-available backlog at its 50-100+ steady-state floor with well-scoped new issues. Runs every ~8h via the scheduled task (ra

Upload source maps to PostHog Error Tracking for Vite

Systematic visual QA sweep of the live metagraphed frontend (apps/ui, served at metagraph.sh) — actually browsing pages at real viewports and looking for defects only visible on inspection (crowding, overflow, truncation, misalignment), not catchable by reading code or trusting a

Add PostHog MCP analytics to a TypeScript, JavaScript, or Python MCP server. Instruments the server so every tool call, agent intent, and failure is captured as a $mcp_* event — the @posthog/mcp Node SDK for TS/JS, or posthog.mcp (shipped inside the posthog package) for Python. D

Use when writing, validating, or preparing ANY contribution or pull request to the JSONbored/metagraphed repo — adding/enriching a subnet's public surfaces (the most common contribution), a code/schema change to the Worker API or build scripts, picking an issue, running the local

Use when a developer asks what a Bittensor subnet does, whether it's up right now, how to call/integrate its API, or what mining/validating one costs and earns — e.g. "which subnet does image generation", "is subnet 7 healthy", "call the Beam API for me", "does mining subnet 3 ne

Close a workspace session by reviewing a proposed summary, recording approved outcomes, updating state and decisions, and checking repository safety. Use only when the user explicitly asks to end, close, or hand off the current session.

Build, import, or refresh this workspace's identity, project, reusable-workflow, and weekly-state files through a guided review. Use only when the user explicitly asks to initialize or redo workspace context.

Load this workspace's current state, recent decisions, blockers, priorities, and session continuity, then give a concise briefing. Use only when the user explicitly asks to begin or resume a workspace session.

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.

Instrument services with structured logging, Prometheus metrics, and OpenTelemetry tracing. Build Grafana dashboards, write Prometheus alerting rules, run k6 load tests, and plan infrastructure capacity.

Generate, test, validate, explain, and debug OPA (Open Policy Agent) Rego policies and Conftest configurations. Covers deny/warn/violation rules, unit tests, regal linting, conftest fmt, namespace design, input shape analysis, and GitHub Actions integration. Use when asked to "write a policy", "test a rego file", "validate policies", "explain this rego", or "why is my policy not firing".

OpenShift SCC diagnosis and hardening, Route TLS patterns, OpenShift GitOps app delivery, and cluster upgrade validation.

Comprehensive PR review across six dimensions — cost impact, environment drift, ownership gaps, SOC 2 compliance, deprecated API / version hygiene, and rollback feasibility. Each mode inspects the diff and current file state, reports findings with severity, and recommends concrete fixes. Use when preparing a PR for merge, conducting a pre-deployment readiness check, or performing a post-merge risk assessment.

Production-readiness preflight check for a directory, repo, or single file. Auto-detects file types (Kubernetes manifests, Terraform, GitHub Actions workflows, Helm values/charts, Flux Kustomizations/HelmReleases, Dockerfiles, shell scripts) and applies type-specific checks across the whole scope. Returns a per-file summary table and aggregated verdict. Use before deploying, merging, or applying a folder of config. For PR diffs spanning multiple files use /platform-skills:pr-review instead. For deep Helm chart work use /platform-skills:helmchart instead.

Apply product thinking to platform work — DevEx audits, friction analysis, RFC/ADR drafting, incident communication, post-mortems, capacity planning, cost optimisation, and platform health review.

Generate renovate.json covering all dependency file types used in a repo, emit a GitHub Actions workflow that validates renovate.json on every PR, or generate a pre-commit hook for local validation.

Detect and respond to in-container threats at the syscall level using Falco (eBPF-based, CNCF, open-source, no license cost). Covers Falco installation on EKS/GKE with eBPF driver, custom rule authoring, alert routing via Falcosidekick, rule debugging, and bridging Falco runtime signals to Kyverno admission enforcement. Use when asked to "detect privilege escalation in containers", "set up runtime threat detection", "write a Falco rule", "route Falco alerts to Slack", or "debug why my Falco rule is not firing".

Secrets strategy, External Secrets Operator scaffolding, Sealed Secrets seal/rotate/backup, rotation runbooks, and Kubernetes-side secrets audit.

Bootstrap and operate a self-improving agent workspace. Scaffolds .learnings/ and memory/ directories, captures errors and learnings during a session, detects recurring patterns, recalls verified lessons, and promotes stable entries to scoped rule files (.claude/rules/ or ~/.claude/rules/). Also implements the Proactive Agent pillars — WAL protocol, working buffer, SESSION-STATE, daily notes, VBR, VFM scoring, ADL decision logic, heartbeat, and reverse prompting. Use when asked to "remember this lesson", "set up agent memory", "log that error", "what did we learn about X", "promote learnings", "revoke that rule", "capture session state", or "enable proactive mode".

Scaffold a multi-agent AI setup for any repo. Scans the codebase, interviews the developer, generates agent configs for whichever AI tools the repo uses (Copilot, Claude Code, Cursor, Codex, Windsurf). Use when asked to "set up agents", "scaffold Copilot agents", or "create an AGENTS.md".

Secure the software supply chain from source to running container. Covers Cosign keyless image signing (Sigstore/Rekor), SBOM generation and attestation (Syft), vulnerability scanning with severity gates (Trivy/Grype), SLSA Level 2 provenance, and Kyverno/OPA admission enforcement. All open-source, no license cost. Use when asked to "sign my image", "generate an SBOM", "scan for CVEs", "attest build provenance", "enforce image signatures in Kubernetes", or "implement SLSA".

Runs through the full Terraform validation pipeline — fmt, validate, tflint, security scan — and reviews a module or plan for blast radius, IAM risk, and state impact.

Triages a PR comment — from a bot (Copilot, CI) or a human reviewer. Routes to the `triage_helper.py` helper for identity checks, thread snapshotting, isolated-worktree fixes, and publish/reply/resolve mechanics; you classify the finding and apply a justified fix. `--dry-run` is fully read-only (investigation and a printed plan, zero mutations). `--no-resolve` runs the full fix/reply workflow but never resolves a thread. Run from inside the repo.

Scan container images, filesystems, git repos, and existing SBOMs for CVEs, secrets, and license violations using Trivy. Covers local CLI, CI severity gates with SARIF upload, and continuous monitoring via Trivy Operator (Flux HelmRelease). Use when asked to "scan my image", "check for CVEs", "scan this repo for secrets", "scan an SBOM", or "set up continuous cluster vulnerability monitoring". IaC misconfig → /platform-skills:checkov. Admission posture → /platform-skills:kyverno. Image signing/SBOM generation → /platform-skills:supply-chain.

Audit GitHub Actions workflows, composite actions, Dependabot configs, and pre-commit configs for security findings using zizmor — template injection, credential persistence, unpinned uses, over-broad permissions, impostor commits. Covers local CLI, auto-fix, zizmor.yml policy, severity-based CI gates, SARIF upload, and pre-commit. Use when asked to "audit my workflows", "run zizmor", "is this workflow safe", "check for template injection", "pin my actions", or "set up a zizmor CI gate". Workflow syntax and shell errors → /platform-skills:github-actions (actionlint). IaC misconfig → /platform-skills:checkov. Image and dependency CVEs → /platform-skills:trivy. Keeping SHA pins fresh → /platform-skills:renovate.

반복 작업을 `/이름` 으로 호출. 파일명 = 커맨드 이름(`fix-issue.md` → `/fix-issue`).

이슈 #$ARGUMENTS 를 처리한다(이슈 우선 워크플로):

AGENTS.md 생태계(rules·memory·agents·skills·commands·workflows)의 연결 구조를

이슈/기획서 기준 SDLC 한 사이클(이슈→개발→테스트→검증→PR/MR)을 사람 개입 없이 자동 실행.

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…