Detect Overengineering

detect-overengineering

Audit an implementation plan document for LLM-style overengineering. Two passes (rubric-driven Claude pass + Codex adversarial pass) merge into a single report of specific edits to slim the plan before code is written.

Sits between writing-plans and executing-plans in the superpowers workflow. Cheaper to fix bloat in a plan than in merged code.

Prerequisites

Verify codex is on PATH:

codex --version

If missing, instruct user to install Codex CLI (npm install -g @openai/codex) and stop.

Process

1. Discover the plan

Look for plan files in this priority order; pick the most recently modified:

1. docs/superpowers/plans/*.md
2. docs/superpowers/specs/*.md
3. docs/plans/*.md

If zero matches, ask the user for the path. If multiple matches in the top-priority directory have mtimes within 24h of each other, present a numbered list and ask which to review.

2. Pass A — Rubric pass (Claude in-session)

Read ./rubric.md and ./examples.md.

For each H2 section in the plan, walk every rubric pattern:

• Look for the pattern's plan-level signal in the section text
• If matched, check examples.md for "don't flag" look-alikes — suppress if the matched text fits a don't-flag case better than a flag case
• Otherwise emit a finding

Each finding is a structured record:

{
  source: "rubric",
  pattern_id: 3,
  plan_section: "## 4. Service layer",
  evidence: "Define IUserRepository...",
  rationale: "One consumer, one implementation. The interface is a name without substance.",
  suggested_edit: "Strike the interface (lines 47-58). Replace IUserRepository references with UserRepository directly.",
  confidence: "high"
}

3. Pass B — Codex adversarial pass

Run Codex with this exact command (matches codex-plan-review for consistency — non-spark model, high effort, read-only):

codex exec \
  -m gpt-5.4-codex \
  --config model_reasoning_effort="high" \
  --sandbox read-only \
  --skip-git-repo-check \
  --full-auto \
  2>/dev/null \
  "<prompt>"

The prompt sent to Codex must follow this template:

You are auditing an implementation plan for LLM-style overengineering —
features that LLM-authored plans tend to over-include but that don't
serve the actual goal. Use this rubric as a lens but feel free to flag
patterns it misses:

<RUBRIC CONTENT FROM rubric.md>

PLAN:

<FULL PLAN CONTENT>

For each issue, return:
- Plan section (exact heading from the plan)
- Quoted evidence (a phrase from the plan, verbatim)
- Why it's overengineering for THIS plan's stated goals
- Specific suggested edit (which lines to cut, collapse, or rewrite)

Skip generic architectural advice. Focus only on bloat the author should
cut before writing code. Be direct and opinionated.

Parse Codex's prose response into the same finding shape with source: "codex".

4. Merge findings

For each rubric finding F, scan codex findings for matches:

• Same plan_section AND ≥50% phrase overlap in evidence → merge into one finding tagged source: "both", confidence high
• Same plan_section, different evidence → keep separate, retain individual sources
• Different sections → keep separate

Sort the final list:

1. source: "both" first, in plan order
2. source: "rubric", sorted by confidence desc, then plan order
3. source: "codex", sorted by plan order

5. Present the report

Render this markdown directly in the conversation:

# Overengineering review: <plan path>

<N> findings (<X> cross-validated, <Y> rubric-only, <Z> codex-only)

──────────────────────────────────────────────────────────────────────
## Finding 1 — <pattern name>  [<SOURCE> · <confidence>]

Section: <H2 heading from plan>
Evidence: "<quote>"
Why bloat: <rationale>

Proposed edit:
  <specific change>

──────────────────────────────────────────────────────────────────────
## Finding 2 — ...

If zero findings:

# Overengineering review: <plan path>

Plan looks clean against the current rubric. Codex's adversarial pass
also found nothing actionable.

6. Apply approved edits

After the report, prompt:

"I have N proposed edits. Apply all, walk through one-by-one, or cherry-pick by number?"

Modes:

Edit safety: every Edit call uses an exact-match old_string derived from the finding's evidence quote and a precise new_string derived from the suggested edit. No regex, no replace_all. If old_string doesn't match (user edited the plan between report and apply), stop and re-prompt that finding. Never silently proceed.

After all edits land, summarize:

Applied <X> of <N> edits. Plan reduced: <before> → <after> lines (<delta>%).
Skipped: <list of finding numbers>.
Re-run /detect-overengineering to verify? (y/N)

Edge cases

• Zero findings: report says so, no edit prompt.
• Plan has uncommitted changes: warn the user but proceed; they own the diff.
• Plan file >2000 lines: chunk the rubric pass by H2 section. Codex still gets the full plan (handles long context natively).
• Codex unavailable / fails: continue with rubric pass only. Report header notes "adversarial pass unavailable."
• No plan auto-discovered: prompt the user for a path. Do not pick a default.

Key rules

• Always read rubric.md AND examples.md before the rubric pass — examples suppress false positives.
• Always use exact-match edits — never regex or replace_all.
• Never apply edits without explicit user approval — even in "all" mode, the user is approving the batch.
• Present Codex output faithfully — don't filter criticisms; merge what overlaps with rubric findings, keep the rest.
• One plan per invocation.