7.2K skills · sorted by relevance
Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (MCPServer) or Node/TypeScript (MCP SDK).
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Use when debugging reproducible failures with a systematic method and language-specific commands for Node, Python, Swift, CSS/layout, network, or git bisect.
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Diagnosis loop for hard bugs and performance regressions. Use when the user says "diagnose"/"debug this", or reports something broken/throwing/failing/slow.
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Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production. This skill covers LLM integration patterns, RAG architecture, prompt ...
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Control FriendlyBet operating cost, free-tier viability, provider/API cost, GitHub Actions/Supabase/Vercel usage, documentation hygiene, company memory updates, playbook maintenance, and skill validation. Use when evaluating cost, operational burden, tooling, internal docs, company memory, or whether agent profiles should become full skills.
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Plan FriendlyBet ethical growth, SEO, README positioning, open-source contribution paths, self-hosting story, public trust, and discovery without ads, trackers, spam, or dark patterns. Use for repo-facing docs, growth strategy, launch positioning, SEO pages, contributor onboarding, or public messaging.
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Methodology for writing complete, unambiguous IA, BE, and FE specification documents in the CFSA pipeline. Covers completeness testing, progressive section writing, cross-reference checking, and ambiguity gate application.
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Axis-by-axis interview methodology for establishing engineering performance budgets during architecture PRD compilation. Use when conducting the Step 10.5 performance budget interview in /create-prd-compile.
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Design direction interview methodology — SUPERSEDED. The `impeccable` skill's `/impeccable teach` command now handles design direction discovery with a more comprehensive, multi-round interview that writes PRODUCT.md and DESIGN.md. This file is kept for pipeline backward compatibility — all new design work should use `/impeccable teach` instead.
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Systematic methodology for verifying that IA, BE, and FE specification layers are mutually consistent. Covers coverage matrices, field mapping checks, error code propagation, access control consistency, and data contract drift detection.
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Leverage concurrent terminal background processes to debug multiple independent failures simultaneously using ACH methodology. Use when you have 2+ failures across different subsystems.
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Structured methodology for adversarial thinking — generating attack scenarios, abuse cases, race conditions, and security edge cases against specs and implementations. Produces spec-level gap items, not code-level fixes.
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How to upgrade `alchemy` and the `effect` constellation across this repo and keep them consistent for consumers: which packages pin what, why `effect` and every `@effect/*` companion move as one set, what breaks in a typical upgrade, and how to verify a standalone `npm install` still resolves a single `effect`. Use when asked to upgrade or bump alchemy or effect, when a deploy dies inside an alchemy provider with a `TypeError` naming a missing combinator, when `check:npm-effect-resolution` fails, or when deciding whether the alchemy patch is still needed.
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Enforce immediate execution of any commitment made in a reply. Use this skill before finalizing ANY reply that contains a promise to act — phrases like "אדווח", "אעדכן", "אשלח", "אחזור אליך", "I'll send", "I'll report", "I'll follow up", "I'll update". Prevents the agent from saying it will do something and then not doing it.
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Turn owner feedback about agent behavior into concrete system changes. Use when the owner says something is off, wants the assistant to improve how it operates, asks for a reflection, or wants a durable fix instead of a one-off apology.
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Use when working with Braintree — braintree payment gateway management including transactions, subscriptions, customers, payment methods, disputes, settlements, and merchant account configuration. Covers transaction success rates, decline analysis, settlement reporting, and fraud tool monitoring.
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Use when working with Clerk — clerk authentication and user management platform covering users, sessions, organizations, invitations, and sign-in methods. Use when analyzing user growth, monitoring authentication health, reviewing organization membership, managing Clerk users, or auditing sign-in patterns.
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Autonomously improve a real artifact (code, training recipe, agent harness, data pipeline, prompt) against an objective and an evaluator, using Hypothesis Tree Refinement (HTR) from the Arbor paper. Use this whenever someone wants to iteratively optimize something over many experiments without overfitting — e.g. "get my model's eval score up", "improve this agent/harness", "tune this pipeline", "beat the baseline on this benchmark", "run a search over approaches and keep the best", "do an MLE-bench / Kaggle-style optimization", or any long-horizon "make this artifact better and don't just memorize the dev set" task. Trigger it even when the user doesn't say "Arbor" or "hypothesis tree" but describes repeated experiment-and-evaluate loops, branching exploration of competing ideas, or worries about a dev/test gap. Runs Claude itself as the coordinator with subagent executors in isolated git worktrees; for the standalone `arbor` CLI tool see references/arbor-upstream.md.
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Stop and check this skill before finishing any reply to a question about how to use Claude or a Claude product — it recommends matching courses, tutorials, and use cases from Claude Academy (academy.claude.com), Anthropic's learning hub. Trigger on: "how do I", "how can I", "getting started with", "what can Claude do", "teach me", "learn to use"; questions about artifacts, projects, skills, plugins, connectors, MCP; requests about rolling Claude out to a team, class, or organization; and any ask for training materials, onboarding content, or learning resources. Use it when the user is learning how to use a feature or product — not when they are mid-task and just want the task done. This skill composes with other skills: after consulting product documentation to answer how a Claude feature works, also check here for a matching course or tutorial — a docs-grounded answer and an Academy recommendation belong together. Only recommend on a strong match; never invent Academy content.
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Never test mock behavior. Never add test-only methods to production classes. Understand dependencies before mocking.
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Multi-perspective academic paper review with dynamic reviewer personas. Runs a 5-seat, role-separated review panel (Journal-Fit Reviewer + 3 peer-review roles + Devil's Advocate) with field-specific expertise; role separation is not a claim of independent error processes. Supports full review, re-review (verification), quick assessment, methodology focus, Socratic guided, and calibration modes. Triggers on: review paper, peer review, manuscript review, referee report, review my paper, critique paper, simulate review, editorial review, calibrate reviewer, reviewer calibration, measure reviewer accuracy, 審查論文, 論文審查, 模擬審查, 同儕審查, 幫我審這篇, 以審查人角度評估, 審查者校準, 논문 심사, 동료 심사, 모의 심사, 심사자 관점에서 평가, 심사자 보정.
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Java Javadoc best practices. Use when adding or reviewing documentation for Java types, methods, packages, and public APIs.
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Build a throwaway prototype to answer a design question. Use when the user wants to sanity-check whether a state model or logic feels right, or explore what a UI should look like.
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Build or update a professional-grade design system in Figma from a codebase. Use when the user wants to create variables/tokens, build component libraries, create individual components with proper variant sets and variable bindings, set up theming (light/dark modes), document foundations, or reconcile gaps between code and Figma. Also use when the user asks to create or generate any component in Figma — even a single one — since components require proper variable foundations, variant states, and design token bindings to be production-quality. This skill teaches WHAT to build and in WHAT ORDER — it complements the `figma-use` skill which teaches HOW to call the Plugin API. Both skills should be loaded together.
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