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Get Started Free →Three-Layer Memory System — automatic fact extraction, entity-based knowledge graph, and weekly synthesis. Manages life/areas/ entities with atomic facts and living summaries.
.claude/skills/jdrhyne-knowledge-graph/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-12 | ✗→✓ | ▲ Improved | -24% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -51% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 338% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 17% | 0% |
| case-09 | ✗→✓ | ▲ Improved | -19% | 0% |
Maintain a lightweight, append-only entity graph that compounds durable facts across sessions.
Store the graph under:
text<workspace>/life/areas/ people/<slug>/ companies/<slug>/ projects/<slug>/
Each entity folder should contain:
summary.md for the short, current snapshotfacts.jsonl for atomic, append-only factsUse one JSON object per line:
json{ "id": "<slug>-NNN", "fact": "Plain-English fact", "category": "relationship|milestone|status|preference|context|decision", "ts": "YYYY-MM-DD", "source": "conversation|manual|inference", "status": "active|superseded", "supersedes": "<older-id>" }
Durable facts usually include:
facts.jsonl.summary.md in 3 to 8 concise lines.summary.md first.facts.jsonl only if the summary is stale or the user asked for detail.Recall should be triggered, not automatic.
Create the core directories once:
bashmkdir -p life/areas/people life/areas/companies life/areas/projects
If multiple agents share one workspace, point them at the same life/ directory so they operate on the same entity store.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-12 | fail→pass | 10,071 | 2,822 | -72% | 1 | 1 | 0% | 1,553 | 1,185 | -24% | 0 | 0 | — |
case-13 | fail→fail | 5,843 | 2,727 | -53% | 1 | 1 | 0% | 815 | 1,191 | +46% | 0 | 0 | — |
case-01 | fail→fail | 4,246 | 5,138 | +21% | 1 | 1 | 0% | 577 | 1,005 | +74% | 0 | 0 | — |
case-02 | fail→fail | 3,080 | 8,209 | +167% | 1 | 1 | 0% | 420 | 1,165 | +177% | 0 | 0 | — |
case-03 | fail→pass | 13,959 | 2,191 | -84% | 1 | 1 | 0% | 2,232 | 1,089 | -51% | 0 | 0 | — |
case-04 | fail→pass | 2,840 | 9,317 | +228% | 1 | 1 | 0% | 391 | 1,712 | +338% | 0 | 0 | — |
case-05 | fail→fail | 4,523 | 7,918 | +75% | 1 | 1 | 0% | 667 | 1,081 | +62% | 0 | 0 | — |
case-06 | fail→fail | 2,579 | 8,320 | +223% | 1 | 1 | 0% | 338 | 1,196 | +254% | 0 | 0 | — |
case-07 | pass→fail | 4,077 | 9,677 | +137% | 1 | 1 | 0% | 523 | 2,203 | +321% | 0 | 0 | — |
case-08 | fail→pass | 6,194 | 2,157 | -65% | 1 | 1 | 0% | 955 | 1,116 | +17% | 0 | 0 | — |
case-09 | fail→pass | 9,197 | 2,405 | -74% | 1 | 1 | 0% | 1,358 | 1,104 | -19% | 0 | 0 | — |
case-10 | fail→pass | 11,455 | 3,953 | -65% | 1 | 1 | 0% | 1,770 | 1,426 | -19% | 0 | 0 | — |
case-11 | fail→pass | 6,866 | 2,743 | -60% | 1 | 1 | 0% | 1,044 | 1,117 | +7% | 0 | 0 | — |
case-14 | pass→fail | 7,046 | 2,372 | -66% | 1 | 1 | 0% | 1,077 | 1,117 | +4% | 0 | 0 | — |
case-15 | pass→pass | 5,431 | 3,785 | -30% | 1 | 1 | 0% | 922 | 1,406 | +52% | 0 | 0 | — |
case-16 | fail→fail | 3,199 | 5,434 | +70% | 1 | 1 | 0% | 436 | 1,060 | +143% | 0 | 0 | — |
case-17 | fail→pass | 28,652 | 5,332 | -81% | 1 | 1 | 0% | 2,257 | 1,632 | -28% | 0 | 0 | — |
case-18 | pass→pass | 5,549 | 4,284 | -23% | 1 | 1 | 0% | 780 | 1,418 | +82% | 0 | 0 | — |
case-19 | fail→pass | 3,036 | 3,666 | +21% | 1 | 1 | 0% | 373 | 1,374 | +268% | 0 | 0 | — |
case-20 | pass→pass | 11,953 | 6,605 | -45% | 1 | 1 | 0% | 1,716 | 1,762 | +3% | 0 | 0 | — |
case-21 | pass→pass | 12,251 | 4,173 | -66% | 1 | 1 | 0% | 1,859 | 1,442 | -22% | 0 | 0 | — |
case-22 | pass→pass | 5,208 | 3,509 | -33% | 1 | 1 | 0% | 771 | 1,362 | +77% | 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. 22 cases were attempted, and 18 counted toward the lift figure. The other 4 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 +32 percentage points is the difference between those two pass rates over the 18 comparable cases. 3 cases got worse with the skill loaded, and they are included in that figure.
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.