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Orchestrator for the experimentation skill suite — turn assumptions and product questions into rigorous, well-instrumented experiments and decision-grade readouts. Routes through backlog → spec → runbook → readout based on user need and existing artefacts. Platform-agnostic with PostHog as the primary binding. Load when the user asks to design an experiment, A/B test something, set up an experiment, run a holdout, test a hypothesis, decide what to test next, read out experiment results, analyse a test, or says "should we A/B test this", "experiment on the landing page", "is this lift real", "ship or kill this test", "what should we test next", "build an experiment backlog", "test the pricing page", "validate this with an experiment".

日本語の概要は準備中です。原文の説明を表示しています。

Orchestrator + builder for distinctive, production-grade frontends that don't look AI-generated. Derives stack and design context from product-soul/PRD/specs, then runs the anti-slop chain — explore distinct directions, lock a DESIGN.md system, build from golden examples with mandatory polish + every interactive/empty/loading/error state, then review. Load when the user asks to build a UI, design a frontend, build a landing page or dashboard or web app, beautify or redesign a page, make a UI look premium/playful/editorial, says "build me a frontend", "make this not look AI-generated", "design this interface", "give this real polish", or "frontend design". Routes to design-direction, design-system, design-review.

日本語の概要は準備中です。原文の説明を表示しています。

Strip a problem to its irreducible fundamental truths and rebuild the solution from the ground up — free from analogy, convention, and inherited assumptions. Load when the user feels constrained by how something has always been done, when existing solutions feel expensive or inefficient for no good reason, when the user asks to think from first principles, challenge the fundamentals, or rebuild this from scratch. Also triggers on "why does it have to work this way", "what are the actual constraints here", "ignore what everyone else does", or when deep-thinking diagnoses a convention-break frame. Based on Aristotle's first principles method, popularised by Musk and Feynman. Produces genuinely novel solutions by eliminating convention.

日本語の概要は準備中です。原文の説明を表示しています。

Run a structured retrospective after development-phase runs of your product's agents — interview the owner in plain language about what went well and poorly, draft ranked improvement hypotheses, then design and run small n=1/n=2 experiments with pre-declared success criteria, guardrails, stop conditions, and a cost/ROI kill-switch. Load when the user says how did that run go, retro this run, the agent output was bad, what should we improve, draft hypotheses, run a small experiment, or after repeated dev runs of an agentic system produce uneven quality. Priority: output quality over performance over cost, each with diminishing-returns stops. NOT a product A/B test (experimentation), NOT coding-agent harness repair (harness-evolution), NOT production-scale learning (runtime-learning-loop).

日本語の概要は準備中です。原文の説明を表示しています。

Orchestrator for the eval-output skill suite — evaluate LLM and agent outputs for quality, accuracy, helpfulness, and safety using structured rubrics and LLM-as-judge techniques. Load when the user says "evaluate this output", "score this response", "run an eval", "LLM as judge", "evaluate agent output", "how good is this response", "rate this answer", "eval this", or provides an LLM output that should be assessed for quality. Single entry point for all output evaluation workflows.

日本語の概要は準備中です。原文の説明を表示しています。

Decompose an unknown quantity into 3-5 estimable factors and produce a defensible order-of-magnitude answer without needing precise data. Load when the user needs to size something without data — market size, resource requirements, effort estimates, user numbers, costs — or when a decision is blocked by "we don't know the numbers". Also triggers on "ballpark this", "rough estimate", "how big is this market", "how long would this take", "how many users", or when deep-thinking diagnoses a sizing/estimation frame. The goal is not precision — it is a defensible answer that enables a decision to be made. Based on Enrico Fermi's estimation method.

日本語の概要は準備中です。原文の説明を表示しています。

Set a deliberate visual direction before any UI is built — the single biggest lever against generic AI output. Derives a posture from product-soul/PRD/specs, scores a curated archetype palette, then generates 2-3 GENUINELY DISTINCT directions and compares them side-by-side before committing to one. Load when the user asks to pick an aesthetic, choose a design direction, decide what a UI should feel like, explore visual options, says "what should this look like", "make it feel like [Linear/Apple/ Duolingo]", "give me design directions", "explore some looks", or when frontend-design routes here. Replaces design-archetype. Sub-skill of frontend-design.

日本語の概要は準備中です。原文の説明を表示しています。

Orchestrate one or more thinking frameworks to work through any problem, decision, document, or idea rigorously. Diagnoses which frameworks fit — inversion, pre-mortem, assumption-mapping, socratic, adversarial-hat — then guides the user through them in the right sequence. Load when the user asks for deep thinking, says "help me think through this properly", "apply your best thinking frameworks", "I need to think carefully before deciding", or "what thinking tools should I use here". Also the entry point for any complex problem where the right framework is unclear. Covers product decisions, engineering tradeoffs, personal decisions, strategy, creative challenges — any domain.

日本語の概要は準備中です。原文の説明を表示しています。

Compress an oversized SKILL.md to under 200 lines without losing effectiveness. Load when a skill exceeds 200 lines, when AGENTS.md triggers compression after a skill edit, or when the user asks to compress, shrink, slim down, or optimize a skill. Also triggers on "this skill is too long", "reduce skill size", "make this skill shorter". Applies to all skills including meta skills — the 200-line rule has no exceptions. Preserves hard gates, gotchas, output format, routing triggers, and at least one example. When genuinely CORE content cannot be compressed away, invokes split-skill instead of degrading the skill.

日本語の概要は準備中です。原文の説明を表示しています。

Write a rigorous, decision-grade experiment spec — falsifiable hypothesis, primary metric, guardrails, randomisation unit, exposure definition, method (A/B, holdout, switchback, quasi-experiment, MAB), MDE/duration plan, peek policy, validity threats, and pre-committed decision rule. Platform-agnostic. Load when the user has a candidate experiment and needs to spec it before launch, or says "spec this experiment", "write the test plan", "design this A/B test", "what's the hypothesis", "how big a sample do we need", "how long should we run this", "define the metrics for this test", or when the experimentation orchestrator routes here.

日本語の概要は準備中です。原文の説明を表示しています。

Review a built frontend against its chosen direction, catch drift back to generic AI defaults, enforce state coverage, ethical patterns, UX heuristics, and polish, and check contrast with APCA (not the legacy WCAG ratio). Produces specific, prioritized fixes — never vibes-based feedback. Works with pasted screenshots or Playwright MCP automated capture. Load when the user asks to review a UI, audit a design, check if a frontend looks generic or vibecoded, evaluate visual quality or polish, says "review this UI", "is this design good", "audit my frontend", "does this feel like [product]", "design QA", or when frontend-design routes here. Sub-skill of frontend-design.

日本語の概要は準備中です。原文の説明を表示しています。

Build the smallest, highest-signal context package for an AI coding task — goal, constraints, repo facts, boundaries, and a verification plan. Load when prompts are underspecified, the agent is missing key files or decisions, the user says "use the right context", "here's the repo", or when work is drifting due to missing constraints. Also triggers on "context engineering", "gather context", "what do you need from me", "before you start". Not for cross-session continuity (use memory-startup/memory-recall).

日本語の概要は準備中です。原文の説明を表示しています。

Instructions for translating Google Stitch designs (HTML/Tailwind) into production-ready Flutter/Dart code.

日本語の概要は準備中です。原文の説明を表示しています。

Triage customer support tickets/emails/chats into categories, priority, and next action; draft responses and create reproducible steps; use for Zendesk/Intercom/Help Scout exports or pasted threads.

日本語の概要は準備中です。原文の説明を表示しています。

Write and debug spreadsheet formulas (Excel/Google Sheets), pivot tables, and array formulas; translate between dialects; use when users need working formulas with examples and edge-case checks.

日本語の概要は準備中です。原文の説明を表示しています。

Automated PR review and CI auto-fix for GitHub and GitLab using the Composio CLI. Pulls diffs, fetches failing job logs, posts review comments, and loops fix commits until checks go green.

日本語の概要は準備中です。原文の説明を表示しています。

Picks random winners from lists, spreadsheets, or Google Sheets for giveaways, raffles, and contests. Ensures fair, unbiased selection with transparency.

日本語の概要は準備中です。原文の説明を表示しています。

Automatically creates user-facing changelogs from git commits by analyzing commit history, categorizing changes, and transforming technical commits into clear, customer-friendly release notes. Turns hours of manual changelog writing into minutes of automated generation.

日本語の概要は準備中です。原文の説明を表示しています。

To access Anthropic's official brand identity and style resources, use this skill.

日本語の概要は準備中です。原文の説明を表示しています。

Automate Canva tasks via Rube MCP (Composio): designs, exports, folders, brand templates, autofill. Always search tools first for current schemas.

日本語の概要は準備中です。原文の説明を表示しています。

How to use a Win32 build of BusyBox to run many of the standard UNIX command line tools on Windows.

日本語の概要は準備中です。原文の説明を表示しています。

Extract structured domain knowledge from AI models in-session or from local open-source models via Ollama. No API key needed.

日本語の概要は準備中です。原文の説明を表示しています。

Project Guidelines Skill (Example)

日本語の概要は準備中です。原文の説明を表示しています。

Automate Cal.com tasks via Rube MCP (Composio): manage bookings, check availability, configure webhooks, and handle teams. Always search tools first for current schemas.

日本語の概要は準備中です。原文の説明を表示しています。