lich-skills

Use at the START of any task, or whenever you are unsure which lich-skill applies. The router for this collection — it maps every lich-skill to the phase of work it belongs to (Decide → Spec → Plan → Build → Debug → Fan-out → Research → Media → Analyze) and walks a decision tree to pick the right one. Prevents the two failure modes of a skill collection: forgetting a skill exists, and reaching for the wrong skill at the wrong phase. Triggered by "/skills-map", "which skill should I use", "what skills do I have", or any moment of skill-selection doubt.

Use when a build, typecheck, lint, or test command is failing and you want the agent to drive it to green on its own — run the check, read the errors, apply the smallest fix, re-run, repeat until exit code 0. Stops the human from being the while-loop (run → read error → fix one line → run again) and stops the agent from bulldozing huge speculative rewrites between checks.

Analyze Google Analytics 4 data — review website performance, identify traffic patterns, diagnose high-bounce pages, compare time periods, and suggest data-driven improvements. Use when the user asks about analytics, website metrics, traffic analysis, conversion rates, user behavior, or performance optimization.

Use BEFORE any spec, plan, or code exists — the Stage 0 gate that decides whether a project should start at all. NO-GO is the default. GO requires passing five framework checks (Differentiation · Audience–Market Fit · Acquisition Channel · Capacity · 7-Factor Wedge), a memory check against your own prior attempts, and a 24-hour pattern-interrupt if enthusiasm-high is detected. Output is a public commitment artifact with pre-mortemed kill criteria (D14/D30/D60/D90 review gates). Triggered by "/go-no-go [project]", any "I want to build / start / take on X" intent, or before opening any new repo or signing any new contract. Pairs with spec-driven-dev (this is Stage 0, that is Stage 1).

Use BEFORE invoking the Task or Agent tool to spawn a subagent. Anthropic does NOT share prefix cache across subagents — every subagent cold-starts and re-tokenizes its full prompt (system prompt + tool definitions + the context you handed it). Spawning N subagents with full context = N× token cost; a single fan-out can burn an entire Max-plan day. This skill enforces a pre-flight discipline: compress every subagent prompt into a ≤200-word brief before spawning. Triggers when the agent is about to call Task / Agent tool, especially with long files, full repo dumps, or N parallel subagents on similar work.

Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, or applications. Generates creative, polished code that avoids generic AI aesthetics.

Use when an agent will work for hours or days across many files and multiple vertical slices. Drives a three-level Project → Sprint → Task hierarchy with isolated per-task execution, a review round-loop, context packs for subagent reviewers, governance-as-code, and orchestrator-readable state. Designed for long-running drivers like /loop, autoresearch:ship, and goal-driven.

Use when you have N≥3 raw research artifacts (notes, podcast summaries, deep-research dumps, daily intel, paper analyses) on one topic and want to lift them into a single structured pack with cross-source claims and provenance — instead of one-shot summarization that loses 90% of intermediate evidence. Treats the N sources as an environment a lite aggregator agent navigates with `inspect` / `search` / `synthesize` tools, rather than concatenating into one prompt.

Use when debugging any non-trivial bug — wrong output, crash, flaky test, performance regression, or "it works locally but not in CI." Forces a scientific-method loop (Observe → Hypothesize → Experiment → Conclude) so the agent stops guessing and starts reasoning. Prevents the

Generate or edit images with Google's Nano Banana 2 (`gemini-3.1-flash-image-preview`). Use when the user asks to generate an image, edit an image, or create a picture. Supports 512 / 1K / 2K / 4K resolutions.

Use when starting any non-trivial feature, refactor, or new project that will touch more than one file. Drives an AI coding agent through a gated Spec → Plan → Build → Test → Review → Ship lifecycle so work is specified before it is built, verified before it is reviewed, and reviewed before it ships.

Web search and content extraction via the Tavily API. Use when you need real-time web results, citations, or raw article content without a browser. Requires TAVILY_API_KEY.