Individual skill
incremental-implementation
Build in small, individually-verified increments that each leave the system working — instead of big-bang changes that fail mysteriously at the end. Use when implementing multi-part features, refactoring anything load-bearing, making large mechanical changes, or when past work produced huge diffs that were wrong somewhere unfindable. Produces the same end state as the big bang, reached through verified checkpoints you can stop at, ship from, or roll back to.
skills/incremental-implementation/SKILL.md
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# Incremental Implementation Skill
The big-bang failure is always the same story: three hours of changes, then "it doesn't work", then an hour of spelunking to find WHICH of forty edits broke it. Incremental work makes the *last five minutes* the only suspect, always. The discipline: every increment ends with the system working and verified — not "will work once the rest lands."
## What This Skill Produces
- The target end state, reached via increments that were each verified green
- **Stoppable points**: any checkpoint is shippable, pausable, or a rollback target
- A change history where every step's intent is legible
## Increment Method
1. **Slice vertically to working states, not horizontally to layers.** "Data layer, then logic, then UI" means nothing works until everything does. Slice so each increment is a *thin working slice*: one endpoint end-to-end · one case handled fully · one call-site migrated. The test for a slice: after it lands, can you demonstrate something that works?
2. **Separate behaviour-preserving from behaviour-changing — always.** The cardinal rule: refactor OR change behaviour in one increment, never both. Prepare-with-refactor (verify: everything still passes, nothing changed) → then the behaviour change lands small and legible. Mixing them makes every regression a two-variable mystery.