LLM Mart Basic

Write warm, direct collaboration comments. Trigger: PR feedback, issue replies, reviews, Slack messages, or GitHub comments.

Use Gentle AI harness discipline for Pi work: clarify first, preserve OpenSpec artifacts, use strict TDD where available, delegate through subagents when useful, and protect review workload.

Create and triage GitHub issues from repository evidence. Trigger: issue creation, bug reports, feature requests, or issue approval.

Trigger: judgment day, judgement day, dual review, adversarial review, juzgar. Run explicit blind dual review with at most two scoped fix/re-judgment rounds.

Trigger: RDD, receipt-driven development, review authority, receipt/lineage, correction/recovery, delivery gate/kill switch, bounded review defects. Guide work.

Trigger: /skill-creation, skill creation, skill creator, create skill, new skill. Create LLM-first skills with valid frontmatter.

Trigger: improve skills, audit skills, refactor skills, skill quality. Audit and upgrade existing LLM-first skills.

Trigger: update skills, skill registry, actualizar skills, after skill changes. Index available skills by trigger and path.

Plan commits as reviewable work units. Trigger: implementation, commit splitting, chained PRs, or keeping tests and docs with code.

Master router for the SOTA engineering skills library. Use this skill whenever the user asks to build, design, implement, refactor, harden, optimize, review, or audit an application, service, or codebase and the request spans more than one domain — or when you are unsure which sp

State-of-the-art API design and audit guidance (2026) covering REST/HTTP, GraphQL, gRPC, WebSockets/SSE/realtime, webhooks, versioning/evolution, and API security/operations. Use when designing or building any API surface (endpoints, schemas, protos, realtime channels, webhook se

State-of-the-art software and system architecture rules (2026) for both building and auditing. Use when designing, building, refactoring, or extending system architecture — boundaries, DDD, hexagonal/clean architecture, event-driven design, CQRS, sagas, messaging, caching, shardi

State-of-the-art rules for writing and auditing asynchronous and concurrent code across runtimes (Python asyncio, JS/Node, Go, Rust, JVM). Use when building anything with async/await, threads, processes, event loops, task groups, channels, or queues — and when auditing existing c

State-of-the-art C and C++ engineering rules (2026 baseline) that Claude applies when writing or auditing C/C++. Covers modern idioms (RAII, value semantics, smart pointers, C++23), memory safety (lifetimes, bounds, sanitizers, hardening flags), undefined behavior, security (SEI

State-of-the-art CLI and developer-tool UX guidance (2026) covering command and flag design, output and interaction (stdout/stderr, --json, TTY detection, exit codes, prompts), runtime behavior and lifecycle (signals, dry-run, idempotency, XDG paths, completions, telemetry), and

State-of-the-art cloud infrastructure architecture (2026). Applies when designing, building, or auditing cloud environments on AWS, GCP, or Azure — account/project structure and landing zones, IAM and workload identity, VPC/network design, DNS/TLS/CDN, compute selection (serverle

Secure coding and security auditing rules (2026 baseline). Use whenever BUILDING or modifying code that crosses a trust boundary — endpoints, handlers, auth/login/signup, sessions, JWT/OAuth, file uploads, payments, multi-tenant features, crypto/secrets handling, parsers, CLI/exe

State-of-the-art confidential computing and cryptographic PETs (2026) for BUILDING and AUDITING systems that protect workloads and data in use from the infrastructure they run on — the inverse of sandboxing. Covers TEE selection (AMD SEV-SNP, Intel TDX, ARM CCA realms, SGX enclav

State-of-the-art marketing copywriting and content guidance (2026) covering positioning and value propositions, headlines and landing-page copy, CTAs and social proof, SEO content (search intent, E-E-A-T, Google spam policies), and the accuracy/legal layer — claim substantiation,

State-of-the-art data engineering rules (2026) for building and auditing data pipelines and analytics infrastructure. Covers architecture and modeling (ELT, lakehouse vs warehouse, dimensional models, medallion layering), pipeline and orchestration discipline (idempotency, increm

Use MCP Inspector to connect to local or remote servers, inspect capabilities, call tools, read resources, test prompts, and diagnose failures before release.

Build an MCP server in TypeScript with focused tools, validated schemas, local and remote transports, Inspector tests, and production security controls.

An MCP server exposes tools, resources, or prompts through a standard protocol so an AI application can discover and use external capabilities.

Treat an AI agent skill as both an instruction package and a software dependency: inspect what it says, what it runs, what it can access, and how it updates.

Add remote HTTP or local stdio MCP servers to Claude Code, choose the right scope, protect credentials, verify the connection, and test with least privilege.

Skills teach Claude a repeatable method, connectors provide governed access to apps and live data, and plugins package related capabilities for installation and sharing.

Use an agent skill to package reusable know-how and workflow instructions. Use an MCP server when an agent needs live, governed access to external data or actions.

Custom commands and skills can both create a slash-invoked workflow in Claude Code. The important choice is how the workflow is discovered, shared, and permissioned.

A useful Claude skill solves one recurring engineering job, is easy to inspect, and saves more time than it creates in setup and review.

Claude skills can live in your Claude account, your local Claude Code setup, or a repository. Install them where the sessions that need them can load them.

Build a portable AI agent skill from one repeatable job: a precise description, concise instructions, focused resources, and tests that prove it works.

AI agent skills package instructions, scripts, references, and templates into portable folders an agent loads only when the task calls for them.

AI made publishing cheap, which is exactly the problem. What separates a page worth ranking from a competent summary of the first ten results.

A prompt that works once isn't a quality system. Five cases, an observable rubric, and a regression set will tell you whether a change helped.

One character of YAML, four pods that never started, and two safety nets I didn't know were holding. Every restart is an audit. Schedule them before they schedule you.

"Verify your work" isn't an instruction. It's a mood. Here's the version that's an instruction. Verify with a different mechanism than the one that made the claim.

A prompt that works once may still fail in production. A lightweight eval set gives you repeatable cases, a clear rubric, and a way to see whether a prompt change actually improved the workflow.

The best AI tool is not the one with the longest feature list. It is the one that solves a defined job reliably, fits the workflow, handles data appropriately, and remains useful after the novelty wears off.

Use AI to speed research without losing trust. Learn to find primary sources, verify claims, preserve uncertainty, and keep an auditable source trail.

Better prompts aren't magic wording. They're short briefs that hand the model a task, the context it can't infer, the limits, and a quality bar.

Discover testnet↔mainnet subnet lineage from repo configs and open a PR for review (pass --dry-run to report only)

Triage raw inbox notes into reviewed repository destinations without deleting their sources.

Audit and rewrite content to remove AI writing patterns

Log a completed piece of content to content/log.md after the user confirms it was published.

Validate a dream artifact, review each proposal, and apply only individually accepted changes.

Run a curator pass against the validated memory directory and produce a proposal artifact.

End a session — log what happened, update state and the decision log, propose memory updates, and check for uncommitted or unpushed work

Find relevant context files by topic. Use when you need to load files for a topic without a slash command, or when a task spans multiple domains.

Inventory and migrate selected Gemini CLI workflows with dry-run review and parity checks.

Find repeated workflows in selected Gemini CLI sessions and draft portable skills after review.

Scan multi-session drift and offer individually reviewed fixes only after explicit approval.

Scan orphaned worktrees and stale branches, then offer explicit approval-gated cleanup.

Guided onboarding or import for durable workspace context

Start a session — load state files, flag staleness, and give a briefing on current priorities, deadlines, and blockers

Create a morning heartbeat from repository state and update the local heartbeat log.

Mid-session checkpoint — append progress to today's session log and update state files if a priority shifted, without ending the session

Maintainer-only. Find every file that would newly ship to adopters, classify each one against the written distribution-boundary categories, default to withhold on no clean match, and ask the maintainer only where the taxonomy does not settle it. Drives the release CLI, which refuses to produce a manifest until every shipping file has an answer.

Audit memory, wiki, and auto-loaded files for duplication, conflicting instructions, and stale content. The default path researches, then asks you a single Apply / Discuss / Decline question; on Apply it applies the report, files any out-of-scope problem as a tech-debt issue, then commits, opens a PR, and merges it on a main-branch run like /update-deps. Pass --apply to re-run the apply-and-publish stage against the most recent report.

Fix the tech-debt backlog, a single issue or a recommended related batch, highest severity then oldest first, on a fresh isolated branch through the audit gate, closing the issue(s) on merge. Pass `list` to see the ordered backlog, `why <issue-number>` to explain the recommendation, or a bare `<issue-number>` to fix that issue directly.

Health-check and auto-heal this project's Claude integration, triage, heal, verify, and report an F-to-A+ grade.

Your efficient agentic AI coding CLI assistant

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

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Verifiable program-level self-evolution for AI-for-Science agents: Skills and typed Operators grow from replay-verified executions.

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Unofficial, agent-friendly Mercadona shopping CLI (Go) — search products, read prices, build a cart and prepare checkout from the terminal. BYO credentials.

Local-first AI canvas for visual workflows, AI short drama, image/video generation, storyboarding and asset management. ComfyUI, AI agents, MCP & Blender. 本地优先…

Let any AI coding tool — Claude Code, Cursor, Codex — drive your real Chrome. One-prompt setup, muscle memory, local-first.

runs anywhere. uses anything