Rodin
cfcad67baa
- review-prompts/generic/sonnet.md: language-agnostic structural review - review-prompts/generic/gpt5.md: language-agnostic semantic/domain review - review-prompts/generic/opus.md: language-agnostic design coherence review - review-prompts/GENERATE.md: meta-prompt for tailoring to any repo - review-prompts/ORCHESTRATION.md: multi-model review orchestration pattern
105 lines
4.1 KiB
Markdown
105 lines
4.1 KiB
Markdown
# Multi-Model Review Orchestration
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When Rodin is asked to review a PR (e.g., "review PR 630", "look at PR #625"), use this
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orchestration pattern instead of a single-pass review.
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## Source of Truth
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Specialized prompt files live at: `~/.openclaw/workspace/review-prompts/`
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- `sonnet.md` — structural/pattern review (Sonnet's mandate)
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- `gpt5.md` — semantic/domain/concurrency review (GPT-5's mandate)
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- `opus.md` — design coherence/contradiction review (Opus's mandate)
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These same files are used by CI (review-bot via `system-prompt-file`). Update one place → both
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paths improve.
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## Decision: How Many Models?
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| PR touches... | Models to run |
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|---------------|---------------|
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| Tests only, config, deps | Sonnet only (structural) |
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| Application code (non-core) | Sonnet + GPT-5 |
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| Core domain (order_management, ledger, risk, decision_engine) | Sonnet + GPT-5 + Opus |
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| Architecture docs or design docs | GPT-5 + Opus (skip Sonnet) |
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| Kill switch, reconciliation, or financial calculations | ALL THREE + narrow deep-pass |
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## Orchestration Steps
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### 1. Gather Context (do this yourself, don't delegate)
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- Fetch PR metadata, diff, existing reviews (same as Phase 0-1 of pr-review skill)
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- Identify what files are touched → determines which models to spawn
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- Fetch linked issue/AC if present
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### 2. Spawn Specialized Sub-Agents
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Spawn sub-agents in parallel. Each gets:
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- The full diff
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- The relevant prompt file content (read from review-prompts/)
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- Conventions file (CLAUDE.md)
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- Patterns (from elixir-patterns/phoenix-conventions repos if applicable)
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- Instruction: "Output structured findings as JSON. Do not post to Gitea."
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```
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sessions_spawn(model="sonnet", task="<sonnet prompt + diff + context>")
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sessions_spawn(model="gpt5", task="<gpt5 prompt + diff + context>")
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sessions_spawn(model="opus", task="<opus prompt + diff + context>") # if design PR
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```
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### 3. Synthesize Results
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After all sub-agents complete:
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1. **Deduplicate** — if Sonnet and GPT-5 found the same issue, keep GPT-5's version (deeper
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explanation) and note "(also caught by Sonnet)"
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2. **Rank by severity** — BLOCKER > MAJOR > MINOR > NIT
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3. **Group by category:**
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- 🏗️ Structural (from Sonnet)
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- 🧠 Semantic/Domain (from GPT-5)
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- ⚖️ Design Coherence (from Opus)
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4. **Call out unique contributions** — "Only GPT-5 caught: ..." / "Only Opus caught: ..."
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5. **Actionable fix list:** Real bugs → must fix. Theoretical → discuss. Style → fix if cheap.
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### 4. Present to Aaron
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Format as a unified report with clear sections. Include:
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- Overall verdict (APPROVE / REQUEST_CHANGES)
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- Per-model findings (deduplicated, categorized)
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- Recommended actions
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- Any unresolved existing feedback from other reviewers
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### 5. Post (if requested)
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If Aaron says "post it" or "looks good, post":
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- Use the pr-review skill's Phase 6 posting mechanics
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- Post as a single unified review (not three separate ones)
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- Use the rodin Gitea token for posting
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## Narrow Deep-Pass (for financial/safety PRs)
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After the main review, if the PR touches financial logic:
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1. Extract ONLY the changed financial logic (strip test code, config, docs)
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2. Ask GPT-5 a single focused question:
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- "Can this code produce a silently incorrect financial calculation? Show the specific input
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that produces a wrong number."
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3. If findings emerge, add them to the report under a "🎯 Deep Analysis" section
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## Timing Expectations
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| Configuration | Expected time |
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|--------------|---------------|
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| Sonnet only | ~30s |
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| Sonnet + GPT-5 | ~60s (parallel) |
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| All three | ~90s (parallel, Opus may be faster) |
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| + Deep pass | +45s (sequential after main review) |
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## What This Replaces
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This replaces the old single-pass pr-review for on-demand reviews. The pr-review skill is still
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used for its Phase 0 (PR identification), Phase 1 (context gathering), Phase 4 (existing feedback),
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Phase 6 (posting mechanics), and Phase 7 (walk-through). The REVIEW itself (Phase 3) is now
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multi-model.
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The CI twins (review-bot) continue running independently — they're the automated safety net.
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On-demand reviews are the deep-dive when Aaron wants human-quality analysis.
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