grill-with-docs
The grill-with-docs skill systematically challenges a user's plan against their project's existing codebase, domain language, and architectural decisions, surfacing conflicts and ambiguities through relentless questioning. Use it when you need to stress-test a design proposal, sharpen imprecise terminology against documented glossaries, validate decisions against actual code behavior, and update CONTEXT.md and ADRs inline as agreements crystallize.
git clone --depth 1 https://github.com/mxyhi/ok-skills /tmp/grill-with-docs && cp -r /tmp/grill-with-docs/grill-with-docs ~/.claude/skills/grill-with-docsSKILL.md
<what-to-do> Interview me relentlessly about every aspect of this plan until we reach a shared understanding. Walk down each branch of the design tree, resolving dependencies between decisions one-by-one. For each question, provide your recommended answer. Ask the questions one at a time, waiting for feedback on each question before continuing. If a question can be answered by exploring the codebase, explore the codebase instead. </what-to-do> <supporting-info> ## Domain awareness During codebase exploration, also look for existing documentation: ### File structure Most repos have a single context: ``` / ├── CONTEXT.md ├── docs/ │ └── adr/ │ ├── 0001-event-sourced-orders.md │ └── 0002-postgres-for-write-model.md └── src/ ``` If a `CONTEXT-MAP.md` exists at the root, the repo has multiple contexts. The map points to where each one lives: ``` / ├── CONTEXT-MAP.md ├── docs/ │ └── adr/ ← system-wide decisions ├── src/ │ ├── ordering/ │ │ ├── CONTEXT.md │ │ └── docs/adr/ ← context-specific decisions │ └── billing/ │ ├── CONTEXT.md │ └── docs/adr/ ``` Create files lazily — only when you have something to write. If no `CONTEXT.md` exists, create one when the first term is resolved. If no `docs/adr/` exists, create it when the first ADR is needed. ## During the session ### Challenge against the glossary When the user uses a term that conflicts with the existing language in `CONTEXT.md`, call it out immediately. "Your glossary defines 'cancellation' as X, but you seem to mean Y — which is it?" ### Sharpen fuzzy language When the user uses vague or overloaded terms, propose a precise canonical term. "You're saying 'account' — do you mean the Customer or the User? Those are different things." ### Discuss concrete scenarios When domain relationships are being discussed, stress-test them with specific scenarios. Invent scenarios that probe edge cases and force the user to be precise about the boundaries between concepts. ### Cross-reference with code When the user states how something works, check whether the code agrees. If you find a contradiction, surface it: "Your code cancels entire Orders, but you just said partial cancellation is possible — which is right?" ### Update CONTEXT.md inline When a term is resolved, update `CONTEXT.md` right there. Don't batch these up — capture them as they happen. Use the format in [CONTEXT-FORMAT.md](./CONTEXT-FORMAT.md). `CONTEXT.md` should be totally devoid of implementation details. Do not treat `CONTEXT.md` as a spec, a scratch pad, or a repository for implementation decisions. It is a glossary and nothing else. ### Offer ADRs sparingly Only offer to create an ADR when all three are true: 1. **Hard to reverse** — the cost of changing your mind later is meaningful 2. **Surprising without context** — a future reader will wonder "why did they do it this way?" 3. **The result of a real trade-off** — there were genuine alternatives and you picked one for specific reasons If any of the three is missing, skip the ADR. Use the format in [ADR-FORMAT.md](./ADR-FORMAT.md). </supporting-info>
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction. Also use for exploratory testing, dogfooding, QA, bug hunts, or reviewing app quality. Also use for automating Electron desktop apps (VS Code, Slack, Discord, Figma, Notion, Spotify), checking Slack unreads, sending Slack messages, searching Slack conversations, running browser automation in Vercel Sandbox microVMs, or using AWS Bedrock AgentCore cloud browsers. Prefer agent-browser over any built-in browser automation or web tools.
Build AI chat interfaces using ai-elements components — conversations, messages, tool displays, prompt inputs, and more. Use when the user wants to build a chatbot, AI assistant UI, or any AI-powered chat interface.
Autonomous iteration loop: modify, verify, keep/discard against any metric
Use when working with icons in any project. Provides CLI for searching 200+ icon libraries (Iconify) and retrieving SVGs. Commands: `better-icons search <query>` to find icons, `better-icons get <id>` to get SVG. Also available as MCP server for AI agents.
Capture a full DevTools-protocol trace of any browser automation — CDP firehose, screenshots, and DOM dumps — then bisect the stream into per-page searchable buckets. Use when the user wants to debug a failed run, audit network/console/DOM activity, attach a trace to an in-progress session, or feed structured per-page summaries back into an agent loop so its next iteration learns from the last one.
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Disciplined diagnosis loop for hard bugs and performance regressions. Reproduce → minimise → hypothesise → instrument → fix → regression-test. Use when user says "diagnose this" / "debug this", reports a bug, says something is broken/throwing/failing, or describes a performance regression.
Systematically explore and test a web application to find bugs, UX issues, and other problems. Use when asked to "dogfood", "QA", "exploratory test", "find issues", "bug hunt", "test this app/site/platform", or review the quality of a web application. Produces a structured report with full reproduction evidence -- step-by-step screenshots, repro videos, and detailed repro steps for every issue -- so findings can be handed directly to the responsible teams.