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---
name: harness-review
description: Review an agent's harness (scaffold) for quality, token efficiency, and correctness. Based on harness engineering research.
---
## The Core Insight
Same model with scaffold A scores 42%. Same model with scaffold B scores 78%. The harness IS the product.
## Review Checklist
### 1. Context Loading
- [ ] Bootstrap memory under 2,000 tokens?
- [ ] Skills loaded on-demand, not all upfront?
- [ ] Total input under 40% of context window? (the "dumb zone" is past 60%)
### 2. Tool Design
- [ ] Each tool has a clear, one-sentence description?
- [ ] Failed tool calls return actionable error messages (not stack traces)?
- [ ] Tool results are concise (no dumping raw HTML/JSON)?
### 3. Error Recovery
- [ ] Retry logic with exponential backoff?
- [ ] Graceful degradation (if tool X fails, try alternative)?
- [ ] Never silent swallowing of errors?
### 4. Model Routing
- [ ] Parsing/extraction tasks → cheapest tier (Haiku)?
- [ ] Reasoning/planning → middle tier (Sonnet)?
- [ ] Architecture/complex decisions → top tier (Opus)?
- [ ] Never sending 100K tokens to the expensive model?
### 5. Memory Management
- [ ] Important facts written to disk BEFORE compaction?
- [ ] Memory files have clear structure (not just append-only)?
- [ ] Cross-session memory actually loads at session start?
### 6. Planning
- [ ] Uses TodoWrite or equivalent for multi-step coherence?
- [ ] Each step has a testable definition of done?
- [ ] Can explain what it's doing and why at any point?
## Reference
Read `docs/vision/VISION.md` — search for "harness engineering evidence"
