lope-review

name	lope-review
description	Fan out a file review to every configured validator and collect N independent critiques — one per model. Use for cross-model code review, doc review, contract review, resume review, or any review-shaped task where multiple perspectives catch what a single model would miss. Optional --focus flag targets a specific concern (security, perf, tests, clarity, etc.).

Lope Review

Read a file. Send it to every configured validator in parallel with a review prompt. Print N critiques side-by-side. No sprint, no phases, no majority vote.

This is the lightest way to get cross-model review on a single artifact — sharper than pasting into one chat, because each model reviews independently without seeing the others' takes.

Invocation

Two paths — you must handle both:

1. Explicit slash command. User types /lope-review path/to/file.py. Route to lope review <file>.

2. Natural language. User says something like:

   • "Review this file across models" / "Get a multi-model review of auth.py"
   • "Have the other CLIs check my PR"
   • "Review this for security / performance / accessibility / correctness"
   • "What would gemini and claude say about this file?"

   Recognize the shape and invoke lope review <file> --focus <area> on their behalf. If no focus is obvious, leave --focus off — the default review prompt is "bugs, code-smells, design issues, improvements with line references."

Command shape

lope review <file>                        # general review, all validators
lope review <file> --focus "security"     # narrow the prompt
lope review <file> --focus "test coverage"
lope review <file> --json                 # machine-readable JSON
lope review <file> --validators claude,opencode  # restrict the pool
lope review <file> --timeout 120          # per-validator timeout

v0.7 superpowers (opt-in)

The default invocation above is the v0.6 raw fan-out. v0.7 layers a structured-mode pipeline on top. None of these flags change behavior unless explicitly passed.

# Consensus review — merge, dedupe, and rank findings across N validators
lope review auth.py --consensus
lope review auth.py --consensus --format markdown-pr   # PR-comment-shaped output
lope review auth.py --consensus --format sarif > review.sarif   # CI upload
lope review auth.py --consensus --json
lope review auth.py --consensus --include-raw          # also dump raw answers

# Tune the merger
lope review auth.py --consensus --similarity 0.9 --min-consensus 0.5

# Synthesis — primary rolls N answers into one executive summary
lope review auth.py --consensus --synth
lope review auth.py --consensus --synth --anonymous    # strip validator names

# Persistent memory — stores findings for cross-session recall
lope review auth.py --consensus --remember
# Then: lope memory hotspots / lope memory file auth.py / lope memory search "..."

# Divide a directory into per-file reviews, merge findings globally
lope review src/ --divide files --consensus

# Diff hunks — review each hunk and re-anchor findings onto post-change lines
lope review pr.diff --divide hunks --consensus --format sarif

# Role lenses — round-robin security/performance/tests/etc. across validators
lope review auth.py --roles security,performance,tests --consensus

# Brain-aware review (Makakoo OS only)
lope review auth.py --consensus --brain-context "auth decisions" --brain-log

--divide and --roles are mutually exclusive. Pass one or the other; the combination is reserved for a future phase and the CLI rejects it.

When the user says "give me the consensus view", "rank findings across the council", "post this as a PR comment", "upload to GitHub code-scanning", "which file keeps getting flagged?", "executive summary across models", "strip the model names" — recognize and invoke the relevant flags above.

For the formats, see docs/ci.md (SARIF + PR comment). For memory, see skill: lope-memory. For Brain integration, see docs/makakoo.md.

When to use review vs execute vs ask

• review: the user has an artifact (file) they want critiqued. Output = N critiques, not a code diff.
• execute: the user has a sprint doc with defined phases and wants lope to IMPLEMENT the deliverables. Output = code written + validator verdicts.
• ask: the user has a question, not a file. Output = N answers to the question.

If the user says "review", "critique", "check", "audit this file" → lope review. If they say "fix this file", "improve this file", "refactor this" → that's execute or a direct code edit, not review.

File semantics

lope review sends the file content inline inside the review prompt. For very large files (>10k lines / very long) this may exceed some validators' context windows — results will show [ERROR] for affected validators, and the rest will complete normally. For huge files, pre-extract the section you want reviewed and pass that file instead.

Binary files, images, and PDFs are not supported — the command reads as UTF-8 text. Encoding errors are tolerated via errors="replace" but non-text files will produce garbage prompts. Use harvey_describe_image / harvey_describe_video for non-text media.

Output shape

Same as lope ask — one ━━━ <validator> ━━━ section per model, printed in completion order (fastest first). Use --json for [{"validator": ..., "review": ..., "error": ...}].

Pairing with execute / negotiate

After a lope execute run, lope review <deliverable> is a useful sanity check — multiple models reviewing the final file, independent of the phase-review loop. Similarly, lope review SPRINT-plan.md before running lope execute catches planning bugs early.

Cost awareness

Same as lope ask — N× tokens per run. Long files multiply this further. For casual reviews, --validators claude,gemini keeps the cost bounded to two fast models.