Diagnose a crashed, hung, or erroring deployed Databricks App. Use when: (1) an app is crashed / UNAVAILABLE / 502s, (2) an app hangs or is unresponsive, (3) you need the stack trace behind a failure, (4) questions about reading app logs or the diagnostics env toggles.

Enforces GitHub Actions security and compliance for this monorepo. Use when adding third-party actions, handling secrets, defining permissions, and reviewing CI security trade-offs.

Linear issue conventions: naming, labels, priorities, status transitions and PR links. Use when creating or updating Linear issues, breaking a feature into Linear tasks, or resuming work tracked in Linear.

Keeps the team's memory: where what you learned goes (your memory, shared with the team when it is about the project, a lesson in .opencastle/lessons/, or a team skill for a procedure), and how lessons are cited, re-verified and graduated. Use when you confirm something true for anyone working in the repository (a fix after a failure, a convention, a decision, a quirk, a correction about the code), when you work out a procedure no skill covers, when you touch a lesson copied from memory, when doctor reports a lesson's cited code changed, or when the lessons index grows long.

Performance optimization for React applications — rendering, re-renders, data-fetching waterfalls, bundle size, and the JavaScript hot paths inside components. Use when the user mentions slow or janky renders, unnecessary re-renders, memoization (React.memo / useMemo / useCallback), lazy loading, code splitting, bundle size, request waterfalls, duplicate or repeated API requests, request deduplication, caching fetched data in localStorage, scroll or touch event listeners, laggy scrolling or typing, or asks to review, profile, or speed up React code. Not a general React authoring guide — for folder and layer structure use react-feature-sliced-design, for language and typing questions use typescript-development.

Sketch types, signatures, and module structure before code, then stay in the loop while implementation fills in. Use for /architect, 'architect this', 'design this', or non-trivial work where jumping to code would lock in the wrong shape.

用于字帖生成器、练字助手、儿童练字、姓名练字、拼音字帖、汉字描红、笔顺练习、基础笔画、控笔、数字描红、数字书写、古诗字帖、田字格或米字格、A4 打印字帖等需求，包括“给孩子做可打印姓名字帖”“做带拼音的田字格”“不知道孩子该练什么”等自然提问。不用于书写障碍诊断或书写评分。

Design system conventions for the Phoenix frontend — layout, dialogs, error display, BEM CSS class naming, and CSS design tokens. Use when building UI, naming CSS classes, creating or consuming tokens, handling errors, or designing dialog interactions in js/app/src/.

Add Paddle usage-based billing (early access) to a Next.js app — early access check, usage balance units, meters, price allowances, usage events, balance gating, and `usage_balance.depleted`, using the Paddle API directly.

Audit server-side paid-media measurement including server-side tag management, platform conversion APIs, event taxonomy, browser/server deduplication, consent, hashing, data quality, observability, and privacy. Use for server-side tracking, sGTM, server-side tagging, Meta CAPI, TikTok Events API, event_id, deduplication, pixel debugging, first-party measurement, or conversion data loss.

Plan and review paid-media budgets, bidding, pacing, marginal return, targets, constraints, and allocation across supported platforms. Use for ad budget allocation, media budget, bidding strategy, scaling, spend pacing, ROAS target, or investment tradeoffs. Use ads-math for calculations and forecasts.

Run a source-grounded paid-advertising audit for one or more of Google, Meta, YouTube, LinkedIn, TikTok, Microsoft, Apple, Amazon, Reddit, Pinterest, Snapchat, and X. Use for full ad checks, account health reviews, paid-media diagnostics, partial audits after authentication or worker failure, missing-platform weighting, beta-feature eligibility and scoring, spend audits, tracking audits, or prioritized opportunities and risks.

Audit Amazon Ads profiles, regions, Sponsored Products, Sponsored Brands, Sponsored Display, DSP, portfolios, targeting, search terms, retail readiness, creative, budgets, ACOS, TACOS, reporting, and policy. Use for Amazon Ads, sponsored ads, Amazon PPC, ACOS, TACOS, ASIN advertising, Amazon DSP, or retail-media optimization.

[02] META. Create new skills when existing ones don't cover the task. Analyze unique requirements, build framework (Frame → Research → Plan → Execute), integrate risks, and declare new skill. Use when facing novel problems that existing skills can't address.

Diagnose and fix stalled agent loops using the joelclaw CLI. Use when loops appear stuck, stories aren't progressing, or the event chain broke. Triggers on "loop stalled", "why isn't the loop progressing", "diagnose loops", "fix stuck loop", "loop not moving", "what happened to the loop", "stories stuck at pending", or any request to debug loop infrastructure.

Search, register, connect, transfer, and renew domain names on Vercel using the CLI or Domains Registrar API. Use for domain availability and pricing, custom domains, DNS records, nameservers, ownership verification, and domain orders.

Neo4j Python Driver v6 — driver lifecycle, execute_query, managed and explicit transactions, async (AsyncGraphDatabase), result handling, data type mapping, error handling, UNWIND batching, connection pool tuning, and causal consistency. Use when writing Python code that connects to Neo4j via GraphDatabase.driver, execute_query, execute_read, execute_write, AsyncGraphDatabase, neo4j.Result, or RoutingControl. Package name is `neo4j` (not neo4j-driver) since v6. Python >=3.10 required. Does NOT handle Cypher query authoring — use neo4j-cypher-skill. Does NOT cover driver upgrades or breaking changes — use neo4j-migration-skill. Does NOT cover GraphRAG pipelines (neo4j-graphrag package) — use neo4j-graphrag-skill.

Act on a Caveman learn report - review the ranked token sinks, apply cost-lowering fixes with per-edit consent, and report what those fixes returned. Use when asked to lower an agent's token cost, what caveman has saved, to trim a heavy CLAUDE.md, or to offload re-pasted context into cavemem.

Use whenever creating or editing PyMC-Marketing documentation, including small updates to existing notebooks. Apply the repository's documentation conventions and notebook visibility policy.

Fixing and validating Jupyter notebooks in pymc-marketing. Use when notebooks fail CI, need arviz_plots 2.x migration fixes, or when source code changes require notebook output updates.