ralph

name	ralph
description	Iterative measurement-driven improvement loop. Measure, profile, mutate, re-measure, commit. Works for performance, bundle size, complexity, test coverage — anything quantifiable. Use when the user wants to systematically improve a metric through repeated cycles of profiling and targeted changes.

Ralph

Iterative measurement-driven improvement loop. Each cycle: measure → profile → find biggest contributor → mutate → re-measure → commit only if improvement exceeds noise.

0. Gather inputs

Use AskUserQuestion to collect:

1. What to improve — target metric and direction
2. How to measure — command or method (research and propose one if user doesn't know)
3. How many cycles — default 20
4. Constraints — what must NOT change

1. Setup

• Create a feature branch and draft PR early (load forge-pr skill for title/body). PR description includes a measurements table updated as cycles complete.
• Baseline: measure at least 5 runs, report median. For time: distinguish cold (no cache) from hot (cached). Document methodology.
• Create docs/<target>-ralph-report.md with baseline, methodology, optimization log table, and findings.
• Seed TaskCreate list with N cycle tasks — emit all calls as parallel tool_use blocks in a single assistant turn so the seed is one model round-trip, not N.

2. The loop

Each cycle:

1. Profile — break down the metric into components. Measure each independently. Don't guess.
2. Classify — categorize the biggest contributor (unnecessary dep, eager eval, redundant work, wrong abstraction, missing cache, structural overhead).
3. Mutate — single, targeted change addressing the biggest contributor.
4. Re-measure — same benchmark, same methodology. If improvement is within noise (<3% for time), don't commit — document in report only.
5. Commit + push — only if improvement exceeds noise. Include metrics in commit message. Push report file with each commit.

3. Wrap-up

• Final measurement with same methodology as baseline — this is the number for the PR.
• Update PR description with final before/after table, summary of changes, key findings.
• Complete report with optimization log, dead ends ("Investigated but no improvement"), key findings, and cost breakdown.
• Run CI to verify nothing is broken.

Rules

• Facts over opinions. Measure everything. Don't commit based on theory.
• One change per cycle. Isolate variables.
• Only commit improvements. Noise-level changes clutter history.
• Preserve behaviour. All changes behaviour-preserving unless user explicitly allows otherwise.
• Document dead ends. "X doesn't help" is valuable knowledge.
• Stop at diminishing returns. 3 consecutive no-improvement cycles → tell user and stop.
• Keep the report. The .md is a deliverable — useful for blog posts and future reference.