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KANG TAI
KANG TAI

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Which Cursor rules deserve alwaysApply (and which should wait for a glob)

#cursor #ai #productivity #webdev

Someone asked me: how do you decide which rules earn the always-on slot, and which can wait until a matching file shows up?

That's the real product question. Not "how many rules can I zip."

The wrong answer

Putting everything in alwaysApply: true.

You pay context rent every turn. After compaction, long or generic rules get quietly deprioritized anyway. The model still invents a second form library.

The filter I use

A rule earns alwaysApply only if all three are true:

  1. It must survive compression — if the agent forgets it mid-session, you lose money or time (security, anti-scope-creep, "re-read AGENTS.md after compaction").
  2. It applies to almost every edit — not just React forms or Spring controllers.
  3. It stays tiny — a few imperative lines, not a style guide novel.

Everything else gets globs (or alwaysApply: false + a sharp description the agent can request).

Concrete split (React + Java repos)

Always-on (tiny core)

  • Anti-amnesia / session handoff: plan briefly, persist decisions on disk, re-read after compaction
  • Anti-scope-creep: if I asked for a bug fix, do not refactor the folder structure "while you're here"
  • Security baseline: no secrets in commits, no inventing auth bypasses

Globbed (stack layer)

  • React forms / TanStack Query / zod boundaries → **/*.{tsx,ts} under UI paths
  • Spring controllers / Bean Validation / no JPA entities in responses → **/controller/**/*.java, **/web/**/*.java
  • zod ↔ Bean Validation mapping → only when touching API contract files

Same methodology works if your stack is Next-only or Go — the filter is the product, not the file count.

Why free "900-skill dumps" still fail here

Breadth dumps optimize for GitHub stars. They don't decide what must stay in context after compression. Repo-native memory is a priority queue, not a junk drawer.

Try the tiny core first

I open-sourced a free Starter: three always-on rules + one PR-review Skill.

Landing + Starter: https://kangtai-cn.github.io/agent-memory-pack/?ref=devto-always-on

Full React/Java Bundle ($29 one-time): https://pancake.waffo.ai/store/facai-p8dgqpdw/product/PROD_3jvtO8nHvcOv2YPQSyOpzf?type=onetime&currency=USD&ref=devto-always-on

Install the Starter. After your next compaction, force a recovery from AGENTS.md. If that alone saves a session, the globbed pack is the upgrade — not another mega-ZIP.

No fabricated sales. Honest cold-start: methodology posts convert better than hard sell on a small account.

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