Autoresearch Agent

Valuation analysis for a single stock via Longbridge — current PE / PB / PS / EV-EBITDA snapshot, historical percentile (1–3 years), industry median + relative premium, industry rank. Triggers: "估值贵不贵", "是不是被低估", "PE 历史百分位", "PB 分位", "行业溢价", "行业折价", "X 现在适合买不", "估值水平", "估值貴不貴", "是否被低估", "PE 歷史分位", "行業溢價", "行業折價", "is X expensive", "is X undervalued", "PE percentile", "industry valuation premium", "valuation snapshot".

A comprehensive starting point for AI agents to work with Capacitor. Covers core concepts, CLI, app creation, plugins, framework integration, best practices, storage, security, testing, troubleshooting, upgrading, and Capawesome Cloud (live updates, native builds, app store publishing). Pair with the other Capacitor skills in this collection for deeper topic-specific guidance.

Full PR lifecycle in a fresh task-owned git worktree: implement via the ulw-loop skill with mandatory evidence-bound manual QA → reviewer-readable English PR → verification loop (CI + Cubic, where Cubic is skipped only when its quota is exhausted) → merge by default → worktree cleanup. Decomposes one task into the smallest atomic, independently-mergeable PRs and builds the independent ones concurrently via one worktree per PR driven by parallel subagents or a team. Unbounded loop: any failing gate sends you back to fix-and-re-QA inside that PR's worktree. Use whenever implementation work needs to land as a PR. Triggers: 'create a PR', 'implement and PR', 'work on this and make a PR', 'implement issue', 'land this as a PR', 'split into atomic PRs', 'parallel PRs', 'work-with-pr', 'PR workflow', 'implement end to end', even when user just says 'implement X' if the context implies PR delivery.

Calibrate an LLM judge against human labels using data splits, TPR/TNR, and bias correction. Use after writing a judge prompt (write-judge-prompt) when you need to verify alignment before trusting its outputs. Do NOT use for code-based evaluators (those are deterministic; test with standard unit tests).

Cross-functional what-if modeling for cascading multi-variable scenarios. Unlike single-assumption stress testing, this models compound adversity across all business functions simultaneously. Use when facing complex risk scenarios, strategic decisions with major downside, or when the user asks 'what if X AND Y both happen?'

Premium FIFA World Cup 2026 market & match intelligence — a hosted, read-only layer that fuses official match truth (fixtures, standings, squads, injuries, player performance) with live prediction markets (Kalshi + Polymarket: prices, order books, price history, movers, cross-venue edges) and AI-grounded context (prematch briefs, move explanations, fan/social pulse). Every entity carries a canonical machina URN cross-walked across api-football, sportradar, opta, entain and ESPN, so a market resolves to a fixture resolves to two teams. This skill is prompt-only and premium: it routes the agent to the hosted World Cup Intelligence project (a per-project Machina MCP server) via `machina-cli`. It runs no code locally and ships no API keys. Use when: the user wants World Cup 2026 odds + match context together, asks "what moved and why", wants a grounded market brief or fan-sentiment read on a fixture, or needs one stable id that joins markets ↔ fixtures ↔ teams across providers. Don't use w