When you want a fast read on problem-discovery quality,Documentation Index
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/mos:grade scores the room on five components in under a minute. The lightweight cousin of a full calibrated assessment — it runs without Brain and delivers Larry’s honest evaluation of where your venture thinking stands right now.
Usage
--full to dispatch eight parallel grading agents — one per room section — for a comprehensive venture-wide assessment with cross-section coherence scoring.
autonomous_safe: true — this command runs without human-confirmation gates. Larry reads the room, scores it, and returns the result in one pass.What happens
Calibration distribution preview
Before scoring, Larry shows a preview of the real grade distribution from 100+ past projects. Grades are not absolute — they’re relative to that distribution. You see where the calibration anchor sits before you see your score.
Room state analysis
Larry reads every populated section in the room silently —
room/STATE.md plus all section artifacts. The entire venture is graded, not just one section.Five-component scoring
Each component is scored 1–10, then weighted into a final grade. The scoring table is always shown — no exceptions.
Grade computation
The weighted formula is applied and a letter grade with numeric score is produced.
The five grading components
These are the Brain Grading Engine’s rubric weights, used for calibrated scoring against the real project corpus.| Component | Weight | What it measures |
|---|---|---|
| Technical Feasibility | 25% | Can this actually be built or executed? |
| Logical Argument | 25% | Does the reasoning hold together without leaps? |
| Tool Usage | 25% | Were the right methodologies applied to the right problems? |
| Cognitive Bias | 12.5% | Has the thinking been stress-tested against confirmation bias? |
| QA | 12.5% | Are claims sourced, consistent, and internally coherent? |
Brain mode vs. standard mode
| Aspect | /mos:grade (no Brain) | /mos:grade (Brain connected) |
|---|---|---|
| Calibration data | None — static rubric | Real distribution from 100+ projects |
| Scoring model | 5-component static | Delegates to the Grading Agent |
| Percentile ranking | No | Yes |
| Assessment engine | Larry directly | Grading Agent → Larry wraps results |
brain_grade_calibrate against the live project corpus and returns a percentile ranking alongside the component scores. Larry then wraps those results with teaching context and next steps.
Top feedback patterns
The Brain Grading Engine surfaces recurring patterns across real projects. If you see these in your feedback, they point to structural gaps rather than surface-level issues:- Vision-to-Execution Gap — strong hypothesis, no path to the first customer conversation
- Framework Vomit — many methodologies applied but outputs not connected to each other
- Solution-First — jumped to building before validating the problem worth solving
- Single-Tool — relied on one methodology instead of a chained approach
Output / Artifacts
After scoring, Larry asks: “File this to problem-definition?” If confirmed, the grade artifact is written toroom/**/grades/ with full provenance metadata including component scores, the reality-check classifications, and the top-3 actions.
Example
Related commands
/mos:suggest-next
Get Larry’s ranked recommendations for what to work on after seeing your grade.
/mos:pipeline
Run a multi-step methodology chain to systematically address the gaps your grade reveals.
/mos:status
Fast room health check — section coverage, health glyphs, and governing thoughts at a glance.
/mos:find-connections
Explore cross-section coherence to strengthen the logical argument component score.