Iterate

Skill byerlikaya/claude-starter-kit/claude-starter/skills/iterate

Enterprise engineering workflow for Claude Code — not just prompts. AI agents that plan, build, audit, and ship with security gates, privacy checks, and approval-controlled commits. Safely adopt it into new or existing repositories.

Install

npx -y skills add byerlikaya/claude-starter-kit --skill iterate

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

20 stars20 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

Copied from the file, not written here

Refine-to-Done loop: repeat until tests green + review clean + nothing deferred; bounded. Not the harness /loop. Trigger phrases: "iterate", "loop until done", "keep going until"

SKILL.md

2.6 KB, as published. Nobody here has run it

Iterate — refine to Done, don't stop at the first attempt

When

A task has an objective acceptance criterion (tests, a review gate, a spec) and the first attempt may not meet it. This is a single-session refinement loop — NOT the harness /loop, which schedules a prompt on an interval. Open-ended exploration with no checkable target does not belong here.

The loop

Name the exit test first — the concrete, checkable condition that means "done": tests green, review-agent-csk clean, the spec's acceptance criterion met, zero SonarQube findings. No exit test → go to spec-planning first; a loop without a target never terminates. Prefer an external, machine-grounded verifier — a test exit code, a schema match, a lint/quality gate — over an LLM's self-assessment. A model grading its own output inflates; an "it looks done" or even a single "review clean" with no objective check is a weak verifier. When the only available check is a judgment call, ground it (a second agent with a distinct lens, an explicit rubric) rather than trusting the loop's own say-so. For a generative task with no exit code, the eval-grader skill is that external verifier — a two-layer scorecard (code metrics + LLM-judge) over a fixed set, read as signed deltas vs a pinned baseline.
Run one round: change → verify (drive the real flow, not only tests) → check the exit test.
Report the gap every round — state what still fails and why. Never loop silently.
Repeat until the exit test passes. Stop early and surface it if: two rounds pass with no new progress (you are stuck — report, don't spin), the exit test itself is wrong, or a blocker needs a decision from the user.
Close at the DoD gate, not at a commit. commit-agent-csk still proposes and waits for §4.4 approval. The loop never commits, pushes, or deploys on its own.

Guardrails

Bounded, not infinite. A fixed exit test plus a no-progress stop is the whole point; "keep trying forever" is a bug, not diligence.
Token discipline ([[token-budget]]): each round re-pays for context. Keep a round's output a summary, push heavy logs to docs/*.md, and don't fan out a subagent per round unless isolation demands it.
Don't move the goalposts. Never weaken the exit test to end the loop — fix the work, or stop and ask.