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Get Started Free →Incremental rescan of project folder. Detects new/changed files since last scan, extracts knowledge from CLAUDE.local.md, memory files, and doc inventory, and updates the brain DB. Requires an existing project_brain.db — run /brain-init first if none exists.
.claude/skills/coco-research-brain-rescan/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-11 | ✗→✓ | ▲ Improved | -16% | 0% |
| case-23 | ✗→✓ | ▲ Improved | -42% | 0% |
| case-16 | ✗→✓ | ▲ Improved | -21% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 34% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 15% | 0% |
Detects what changed since the last scan and updates the brain DB with new knowledge.
project_brain.db must exist in the project folder (or parent). If not found, tell the user: "No brain DB found. Run /brain-init first."bashpython3 ~/.claude/skills/brain/scripts/brain/brain_cli.py scan
Check the output:
is_first_scan: true → this folder was never scanned. Tell the user and proceed with full scan.new_files: 0 and changed_files: 0 → "Everything up to date. No changes since last scan ({last_scan})." → done.RESCAN DELTA
============
Last scan: {last_scan}
New files: N
Changed files: N
Removed files: N
Unchanged: N
New/changed files:
+ docs/NewDoc.html (new)
~ CLAUDE.local.md (changed)
+ emails/Latest_Thread.txt (new)
Knowledge sources:
CLAUDE.local.md: found / not found (changed: yes/no)
Memory files: N files (N new/changed)
Documents: N new, N changed
Emails: N new, N changedFollow the same extraction logic as /brain-init Step 4, but only for files in the delta:
upsert_entity so existing entities get updated rather than duplicateddocument entities via upsert_entityRESCAN RESULTS
==============
Project: {name} ({slug})
New/updated entities: N (list)
New decisions: N (list)
New events: N (list)
New document entities: N (list)
Skipped (unchanged): N files
Total proposed writes: NNAsk: "Write to brain? Y/n/adjust]"
Same write order as /brain-init Step 6:
After all writes, sync to MemPalace and brain.json:
pythonfrom brain.memory_bridge import full_sync full_sync("project_brain.db", project_slug)
Then update the manifest:
bashpython3 ~/.claude/skills/brain/scripts/brain/brain_cli.py scan-update
BRAIN UPDATED (rescan)
======================
Entities: +N, ~N updated (total: N)
Decisions: +N (total: N)
Events: +N (total: N)
Documents: +N (total: N)
Manifest updated: N files tracked
Next scan will only process changes after {now}.| Scenario | Use | |----------|-----| | End of conversation session | /brain-update (extracts from conversation) | | New files added to project folder | /brain-rescan (extracts from files) | | Updated CLAUDE.local.md | /brain-rescan | | First time in a project with existing brain | /brain-init (handles both init + scan) | | Periodic refresh | /brain-rescan |
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-11 | fail→pass | 10,279 | 7,714 | -25% | 1 | 1 | 0% | 1,713 | 1,447 | -16% | 0 | 0 | — |
case-22 | pass→pass | 17,520 | 2,927 | -83% | 1 | 1 | 0% | 2,202 | 1,476 | -33% | 0 | 0 | — |
case-23 | fail→pass | 17,869 | 7,193 | -60% | 1 | 1 | 0% | 2,253 | 1,301 | -42% | 0 | 0 | — |
case-01 | fail→fail | 5,082 | 15,863 | +212% | 1 | 1 | 0% | 893 | 1,221 | +37% | 0 | 0 | — |
case-02 | fail→fail | 3,755 | 4,467 | +19% | 1 | 1 | 0% | 389 | 1,153 | +196% | 0 | 0 | — |
case-16 | fail→pass | 13,846 | 2,013 | -85% | 1 | 1 | 0% | 1,636 | 1,299 | -21% | 0 | 0 | — |
case-03 | fail→fail | 14,340 | 4,512 | -69% | 1 | 1 | 0% | 1,319 | 1,262 | -4% | 0 | 0 | — |
case-04 | fail→pass | 12,004 | 8,146 | -32% | 1 | 1 | 0% | 1,078 | 1,444 | +34% | 0 | 0 | — |
case-05 | fail→pass | 14,778 | 4,816 | -67% | 1 | 1 | 0% | 1,529 | 1,764 | +15% | 0 | 0 | — |
case-06 | fail→pass | 18,213 | 12,178 | -33% | 1 | 1 | 0% | 1,970 | 2,092 | +6% | 0 | 0 | — |
case-07 | fail→pass | 21,426 | 11,262 | -47% | 1 | 1 | 0% | 2,345 | 2,049 | -13% | 0 | 0 | — |
case-08 | pass→pass | 8,670 | 8,977 | +4% | 1 | 1 | 0% | 1,496 | 1,523 | +2% | 0 | 0 | — |
case-09 | fail→pass | 14,573 | 10,343 | -29% | 1 | 1 | 0% | 2,223 | 1,999 | -10% | 0 | 0 | — |
case-10 | pass→pass | 14,850 | 14,813 | -0% | 1 | 1 | 0% | 2,345 | 2,541 | +8% | 0 | 0 | — |
case-12 | pass→pass | 10,396 | 3,061 | -71% | 1 | 1 | 0% | 896 | 1,503 | +68% | 0 | 0 | — |
case-13 | pass→pass | 14,915 | 7,171 | -52% | 1 | 1 | 0% | 1,376 | 1,328 | -3% | 0 | 0 | — |
case-14 | fail→pass | 9,151 | 7,922 | -13% | 1 | 1 | 0% | 1,536 | 1,546 | +1% | 0 | 0 | — |
case-15 | fail→pass | 12,935 | 3,716 | -71% | 1 | 1 | 0% | 1,214 | 1,388 | +14% | 0 | 0 | — |
case-17 | fail→pass | 16,518 | 2,285 | -86% | 1 | 1 | 0% | 1,823 | 1,309 | -28% | 0 | 0 | — |
case-18 | pass→pass | 5,332 | 3,945 | -26% | 1 | 1 | 0% | 826 | 1,387 | +68% | 0 | 0 | — |
case-19 | fail→pass | 18,265 | 2,621 | -86% | 1 | 1 | 0% | 2,225 | 1,406 | -37% | 0 | 0 | — |
case-20 | fail→pass | 14,472 | 2,745 | -81% | 1 | 1 | 0% | 1,517 | 1,495 | -1% | 0 | 0 | — |
case-21 | fail→pass | 7,287 | 7,045 | -3% | 1 | 1 | 0% | 1,078 | 1,353 | +26% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted, and 20 counted toward the lift figure. The other 3 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +61 percentage points is the difference between those two pass rates over the 20 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.