UI Autoimprove

UI Auto-Improve

Iteratively grade a frontend interface, identify the weakest area, apply targeted fixes, and keep or revert based on whether the score improved. Each iteration produces a full grade file and changelog entry. Independent changes are batched; dependent changes are isolated.

Core Loop

SETUP → [GRADE → ANALYZE → FIX → RE-GRADE → KEEP/REVERT] × N → WRAP-UP

Workflow

Phase 0: Setup

0.1 Gather Context

Use AskUserQuestion to collect:

1. Target scope — Specific file(s), page(s), component(s), or the whole UI repo
2. Max iterations — How many improvement cycles (default: 5)
3. Focus areas — Specific categories to target (accessibility, typography, etc.) or "auto" to let the grader decide
4. Tolerance threshold — Minimum score drop before reverting (default: 2 points, to handle grading noise)

0.2 Git Safety Net

Check for git repo. If none exists, ask user to confirm, then:

git init → .gitignore → initial commit → create branch

If git exists, create a working branch: git checkout -b autoimprove-<target>-<date>

Commit any uncommitted changes first with user confirmation.

0.3 Start Local Server

Start a local HTTP server to serve the files for screenshot-based grading:

python3 -m http.server <port> &

0.4 Baseline Grade

Dispatch a grading subagent (see Grading Protocol below) to run /interface-grader on the target. This is the baseline. Record the grade file as grades/grade-001.md.

Phase 1: Improvement Loop

For each iteration (1..N):

Step 1: Analyze Previous Grade

Read the most recent grade file. Extract:

• Overall score (the number to beat)
• All FAIL criteria with evidence
• The weakest category or page

Step 2: Plan Changes

Identify which failures to target. Apply batching strategy from references/batching-strategy.md:

• Independent fixes (no interaction) → batch into single iteration
• Dependent fixes (CSS + HTML for new component) → keep atomic
• Previously-reverted areas → try a different approach or skip

Step 3: Checkpoint

git commit -am "checkpoint: before iteration N"

Step 4: Apply Fixes

Make targeted edits to the HTML/CSS/JS. Document what was changed and why.

Step 5: Re-Grade (Isolated)

Dispatch a fresh grading subagent to run /interface-grader. The subagent has NO knowledge of what changes were made — it only sees the current file state. This eliminates self-grading bias.

The subagent must:

• Take all required screenshots (desktop 1440px + mobile 375px per page type)
• Run full code pass on ALL criteria (not just the ones expected to change)
• Run visual pass on ALL criteria
• Produce a complete grade file with delta from previous

Step 6: Keep or Revert

Compare new_score vs baseline_score:

• If new_score >= baseline_score - tolerance: KEEP. Update baseline.
• If new_score < baseline_score - tolerance: REVERT with git checkout -- <files>. Log failure.

Step 7: Record

Append entry to grades/CHANGELOG.md (see references/grading-artifacts.md for format).

Step 8: Plateau Detection

If last 2 consecutive iterations showed no score improvement (kept but same score, or both reverted), stop early — the site has plateaued at the current approach.

Phase 2: Wrap-Up

1. Print the full improvement log showing all iterations
2. Show total improvement: final_score - original_baseline
3. Commit final state
4. Create learnings document at docs/solutions/ui/autoimprove-<target>-learnings.md
5. Kill the local HTTP server

Grading Protocol

Critical: Grading must be isolated from the fixing context.

Dispatch a subagent for every grade:

Agent(
  subagent_type: "general-purpose",
  description: "Grade UI iteration N",
  prompt: "Run /interface-grader on <target>.
    Take screenshots at desktop 1440px and mobile 375px for each page type.
    Grade ALL criteria — do not skip any.
    Write the grade file to grades/grade-<NNN>.md.
    Copy to grades/grade-latest.md.
    If grades/grade-latest.md already exists, include a DELTA section.
    Return the overall score percentage and the weakest category."
)

The subagent sees only the current file state. It does not know what changes were made or what the previous score was (it reads grade-latest.md for delta computation, but this is the previous grade, not knowledge of changes).

Additional Resources

Reference Files

• references/batching-strategy.md — When to batch independent changes vs isolate dependent ones. Includes dependency analysis framework.
• references/grading-artifacts.md — Grade file format, changelog entry format, learnings document template. All artifact specifications.

Key Principles

1. The grader is a black box. Never skip criteria. Never assume a change only affects one area.
2. Batch by independence, not by count. Group changes that can't interact. Isolate changes that can.
3. Every iteration produces artifacts. A grade file and a changelog entry. No mental re-grading.
4. Git is the safety net. Checkpoint before every change. Revert is always one command away.
5. Tolerance handles noise. A 1-2 point score fluctuation is grading variance, not regression.