frontend-review-weekly

name	frontend-review-weekly
description	Use for the weekly AI review. Orchestrates all frontend-review-* skills in order, dispatches the 5 perspective sub-skills in parallel, diffs against last week's KPIs, and produces a weekly report that feeds the ratchet.

Frontend Review — Weekly Orchestrator

You are running the weekly AI review. Your job is NOT to make new judgments — it's to:

1. Re-run every frontend-review-* skill in the correct order.
2. Dispatch the 5 perspective sub-skills in parallel.
3. Diff the KPIs against last week's baseline.
4. File GitHub issues for repeat findings.
5. Propose static-rule promotions for patterns that appear 3+ weeks in a row.

Procedure

Phase 1: raw data collection

Run, in this order:

1. frontend-review-triage
2. frontend-review-ci
3. frontend-review-hygiene
4. frontend-review-deps
5. frontend-review-testing
6. frontend-review-security

Each of these writes to <client-repo>/.frontend-review/report/latest/.

Phase 1.5: architecture review (run when findings are suspected)

Run these on-demand, or always for the first weekly of a new engagement:

7. frontend-review-state
8. frontend-review-performance

These write to <client-repo>/.frontend-review/report/latest/md/.

Phase 2: perspective review (parallel)

Dispatch the 5 perspective skills concurrently using the dispatching-parallel-agents skill. Each reads the raw JSON and produces a perspective-specific markdown report:

• frontend-expert
• react-expert
• performance-expert
• security-expert
• frontend-ops-expert

Phase 3: KPI diff and ratchet

Compare <client-repo>/.frontend-review/report/latest/raw/*.json against <client-repo>/.frontend-review/kpi/baseline.json. Flag:

• Any regression (bad) — these must be fixed before the next weekly
• Any improvement (good) — these update the baseline (ratchet tightens)

Phase 4: repeat-finding detection

Compare this week's findings with the previous 2 report/weekly-*.md files. Any finding that appears in all three weeks is a candidate for static rule promotion: propose an eslint/biome custom rule, a codemod, or a CI gate that would make the check automatic. Write these proposals to <client-repo>/eslint-rules/proposals/<rule-name>.md (create the directory if needed) but do NOT implement them — that's a separate engineering task.

Phase 5: report

Write <client-repo>/.frontend-review/report/weekly-$(date +%Y-w%V).md with:

• KPI delta table (per category)
• Regressions (must fix)
• Improvements (ratchet updates)
• Perspective summaries (1 paragraph per perspective)
• Static rule promotions (pointer to proposals)
• Issues filed (gh issue create output)

Trend Monitoring

Alongside KPI diffing, check the following external signals once per weekly cycle to detect ecosystem drift early.

Monitoring sources

Source	Cadence	What to look for
jser.info	Weekly (Sunday publish)	Major releases, RFCs, breaking changes, security advisories affecting the project's dependencies
State of JS (yearly, ~Dec)	Annual	Usage/satisfaction trends; two consecutive years of satisfaction decline is a switch-trigger
State of CSS (yearly)	Annual	CSS adoption trends, Tailwind / CSS-in-JS sentiment
JavaScript Rising Stars (yearly, ~Jan)	Annual	GitHub star growth; early signal for emerging tools
Official release blogs	On release	Track the project's direct dependencies (framework, bundler, test runner, linter, TypeScript) for major releases

For the weekly run, WebFetch https://jser.info/ and scan for any mention of packages listed in package.json. Flag anything relevant under an Ecosystem Signals heading.

Switch triggers

Recommend investigating a tool replacement when any two of these conditions are met:

1. Satisfaction score has declined for 2 consecutive years in State of JS / State of CSS.
2. No major release in the past 6 months and GitHub issue accumulation is trending up.
3. A maintainer departure or deprecation notice was reported.
4. A clear superior alternative exists: feature parity + significant performance or DX improvement + realistic migration path.

AI / Human Responsibility Split

AI can act on (without human pre-approval):

• Auto-fixable lint / typecheck errors
• Expanding test coverage for existing patterns
• Dependency version bumps (after lint + test pass)

Human must decide:

• Test failure triage: is the spec wrong, the implementation wrong, or the test wrong?
• New library additions or removals
• Architecture boundary changes (new state layer, new routing pattern, new async boundary)
• Any change that modifies what the app does, not just how it does it

Flag any AI-generated PRs in the weekly report that appear to cross into the human-decision zone.

Boundaries

• Do NOT skip any of the Phase 1 domain skills, even if time is short.
• Do NOT modify source code in the client repo.
• Do NOT silently update kpi/baseline.json on a regression. Only update on improvement.

Reference

• Checklist: 11-ai-review.md, 13-kpi-tracking.md
• Phase: week-4-ai-review.md, ongoing.md
• Related skills: all frontend-review-* and sub-skills in review-perspectives/
• External skill: superpowers:dispatching-parallel-agents