Apply when you catch yourself writing the same instruction a second time, or notice a recurring correction. Encode the rule as a lint, metadata flag, runtime check, or script instead of more text.
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Encode recurring fixes in mechanisms (tools, code, metadata, automation) instead of textual instructions. Every error, human correction, and unexpected outcome is a learning signal. Capture it, route it, and close the loop.
Why: Textual instructions are easy to miss. They require the reader to notice, remember, and comply. Structural mechanisms (lint rules, metadata flags, runtime checks, automation scripts) enforce the rule without cooperation.
Pattern: When you catch yourself writing the same instruction a second time:
Pick the strongest mechanism. When more than one mechanism would work, choose the strongest the situation allows (an unrepresentable state that cannot compile, then a lint or banned API that fails CI, then a canonical helper, then a runtime check), because agents copy whatever the surrounding code already does and a weaker guard becomes the next template.
Corollary: If the fix is structural, only use the structural fix. The instruction is the symptom.
Feedback loop:
Anti-patterns:
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Use when a user says "add a ty diagnostic", "write this new ty diagnostic", "change a ty error message", "review ty diagnostics", or asks to add, update, or review ty checks, diagnostic messages, subdiagnostics, or concise output behavior.
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Use when adding A2UI rendering to any AG-UI-supported framework or custom AG-UI application, scaffolding an AG-UI app that should render A2UI, adapting an AG-UI integration to emit A2UI surfaces, or wiring the AG-UI A2UI middleware/toolkit with a compatible renderer.
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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.
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Evaluate an open source project's AGENTS.md and produce a corpus entry for the /agents-md directory. Use when adding a project to the directory, refreshing an existing entry, or running a batch of candidate repositories.
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Execute Anarlog work immediately while recording issues, decisions, progress, and lessons in Linear. Use for Anarlog repository or Anarlog desktop, web, mobile, and API work, including related worktrees and ANLG issues. Explicit brainstorming stays discussion-first. Do not use for unrelated repositories or meeting-data queries.
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Use only for reviewing completed Biome PRs, branches, commit ranges, diffs, or working trees against business logic and requirements. Excludes broad code-quality and process audits, triage, reproduction, and implementation.
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