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Get Started Free →Lisa - intelligent assistant for memory and tasks. Triggers on 'lisa', 'hey lisa', or addressing lisa directly.
.claude/skills/majiayu000-lisa/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-12 | ✗→✓ | ▲ Improved | 101% | 0% |
| case-14 | ✗→✓ | ▲ Improved | 2% | 0% |
| case-15 | ✗→✓ | ▲ Improved | 115% | 0% |
| case-20 | ✓→✗ | ▼ Worse | 138% | 0% |
| case-22 | ✓→✗ | ▼ Worse | 280% | 0% |
Primary interface for project memory, tasks, and knowledge. Routes natural language requests to appropriate capabilities.
Use when the user addresses "lisa" directly:
1) Parse user intent from "lisa" request 2) Route to appropriate underlying command:
lisa memory load --cachelisa memory load --cache --query "<topic>"lisa memory add "<text>" --cachelisa tasks list --cachelisa tasks add "<text>" --cachelisa storage status --cachelisa storage switch <mode> --cachelisa compile-skills3) Summarize results conversationally
When user asks for a retrospective, follow these steps:
1) Gather Changes: Run git diff HEAD~20 --stat or git log --oneline -20 to see recent changes 2) Analyze Patterns: Review the changes and identify:
3) Format Findings: Create a concise summary covering:
4) Save to Memory: Use memory add command to save findings: lisa memory add "RETROSPECTIVE: <findings>" --cache 5) Report: Summarize what was learned and saved
| User Says | Intent | Route To | |-----------|--------|----------| | "show memories", "recent memories", "what's stored" | recall | memory load | | "what do you know about X", "recall X", "search X" | search | memory load --query X | | "remember that X", "save this", "note that X" | remember | memory add X | | "tasks", "what are we working on", "todo" | list tasks | tasks list | | "add task X", "new task X", "create task X" | add task | tasks add X | | "what storage", "current mode", "storage status" | storage status | storage status | | "switch to local", "use docker", "local mode" | switch local | storage switch local | | "switch to zep", "use cloud", "zep-cloud mode" | switch zep-cloud | storage switch zep-cloud | | "do a retrospective", "retrospective on session" | retrospective | git diff + memory add | | "what did we learn", "session learnings" | retrospective | git diff + memory add | | "compile skills", "rebuild skills" | compile skills | compile-skills | | "merge skill extensions", "apply local skills" | compile skills | compile-skills |
👧 lisa-> (emoji, space, "lisa >>", space)👧 lisa-> Recent Memories: for memory listings👧 lisa-> Tasks: for task listings👧 lisa-> for conversational responses👧 lisa-> Tasks: (neo4j) or 👧 lisa-> Recent Memories: (zep-cloud) 👧 lisa-> Recent Memories: (neo4j)
Underlying scripts return JSON:
{ status: "ok", action: "load", facts: [...] }{ status: "ok", action: "add", text: "..." }{ status: "ok", action: "list", tasks: [...] }{ status: "ok", action: "add", task: {...} }{ status: "ok", action: "status", mode: "local|zep-cloud", isConnected: true|false }{ status: "ok", action: "switch", previousMode: "...", newMode: "...", verified: true|false }{ status: "ok", action: "compile-skills", skillsDir: "...", results: [...], merged: N, skipped: N, errors: N }| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 11,346 | 17,525 | +54% | 1 | 1 | 0% | 831 | 1,977 | +138% | 0 | 0 | — |
case-02 | fail→fail | 9,473 | 9,699 | +2% | 1 | 1 | 0% | 596 | 1,790 | +200% | 0 | 0 | — |
case-03 | fail→fail | 10,398 | 10,130 | -3% | 1 | 1 | 0% | 1,697 | 1,753 | +3% | 0 | 0 | — |
case-04 | fail→fail | 4,113 | 15,757 | +283% | 1 | 1 | 0% | 534 | 1,835 | +244% | 0 | 0 | — |
case-05 | fail→fail | 15,064 | 15,069 | +0% | 1 | 1 | 0% | 2,285 | 1,804 | -21% | 0 | 0 | — |
case-06 | fail→fail | 4,444 | 6,566 | +48% | 1 | 1 | 0% | 593 | 1,937 | +227% | 0 | 0 | — |
case-07 | fail→fail | 2,459 | 11,828 | +381% | 1 | 1 | 0% | 427 | 1,895 | +344% | 0 | 0 | — |
case-08 | fail→fail | 3,940 | 15,764 | +300% | 1 | 1 | 0% | 596 | 1,765 | +196% | 0 | 0 | — |
case-09 | fail→fail | 8,111 | 5,199 | -36% | 1 | 1 | 0% | 474 | 1,747 | +269% | 0 | 0 | — |
case-10 | fail→fail | 6,084 | 10,942 | +80% | 1 | 1 | 0% | 1,175 | 1,913 | +63% | 0 | 0 | — |
case-11 | fail→fail | 12,610 | 11,090 | -12% | 1 | 1 | 0% | 1,026 | 1,798 | +75% | 0 | 0 | — |
case-12 | fail→pass | 11,278 | 7,184 | -36% | 1 | 1 | 0% | 912 | 1,830 | +101% | 0 | 0 | — |
case-13 | fail→fail | 4,717 | 16,094 | +241% | 1 | 1 | 0% | 650 | 1,824 | +181% | 0 | 0 | — |
case-14 | fail→pass | 22,577 | 7,286 | -68% | 1 | 1 | 0% | 1,719 | 1,755 | +2% | 0 | 0 | — |
case-15 | fail→pass | 10,207 | 9,402 | -8% | 1 | 1 | 0% | 803 | 1,726 | +115% | 0 | 0 | — |
case-16 | fail→fail | 10,130 | 5,694 | -44% | 1 | 1 | 0% | 815 | 1,815 | +123% | 0 | 0 | — |
case-17 | fail→fail | 11,751 | 6,510 | -45% | 1 | 1 | 0% | 928 | 1,808 | +95% | 0 | 0 | — |
case-18 | fail→fail | 8,289 | 16,685 | +101% | 1 | 1 | 0% | 432 | 1,952 | +352% | 0 | 0 | — |
case-19 | pass→pass | 1,791 | 8,251 | +361% | 1 | 1 | 0% | 283 | 1,912 | +576% | 0 | 0 | — |
case-20 | pass→fail | 4,828 | 10,982 | +127% | 1 | 1 | 0% | 781 | 1,857 | +138% | 0 | 0 | — |
case-21 | pass→pass | 20,581 | 26,136 | +27% | 1 | 1 | 0% | 2,955 | 5,801 | +96% | 0 | 0 | — |
case-22 | pass→fail | 8,289 | 4,318 | -48% | 1 | 1 | 0% | 532 | 2,019 | +280% | 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 6 counted toward the lift figure. The other 16 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 +5 percentage points is the difference between those two pass rates over the 6 comparable cases. 4 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.