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Get Started Free →A hybrid memory system that provides persistent, searchable knowledge management for AI agents.
.claude/skills/sickn33-agent-memory/SKILL.md| Model | Eval pass | Runs |
|---|---|---|
| gemini-3.6-flash | 100% | 12 |
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | 73% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 0% | 0% |
| case-08 | ✗→✓ | ▲ Improved | -49% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 28% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 20% | 0% |
Use this skill when you need a hybrid memory system that provides persistent, searchable knowledge management for AI agents.
This skill extends your capabilities by providing a persistent, searchable memory bank that automatically syncs with project documentation.
agentMemory is already installed in the project:bash ls -la .agentMemory
bash npm install
bash npm run compile
You need to run the MCP server to interact with the memory bank. bash npm run start-server <project_id> <absolute_path_to_workspace> Note: This skill typically runs as a background process or via an mcp-server configuration. ensuring it is running is key.
Once the server is running, you can use these tools:
memory_searchSearch for memories by query, type, or tags.
query (string), type? (string), tags? (string])memory_search({ query: "authentication", type: "pattern" })memory_writeRecord new knowledge or decisions.
key (string), type (string), content (string), tags? (string])memory_write({ key: "auth-v1", type: "decision", content: "..." })memory_readRetrieve specific memory content by key.
key (string)memory_read({ key: "auth-v1" })memory_statsView analytics on memory usage.
memory_stats({}).kilocode/, .clinerules/, or .roo/.User request:
> Use @agent-memory for this task: A hybrid memory system that provides persistent, searchable knowledge management for AI agents.
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 6,523 | 5,370 | -18% | 1 | 1 | 0% | 1,145 | 977 | -15% | 0 | 0 | — |
case-02 | fail→fail | 10,525 | 8,088 | -23% | 1 | 1 | 0% | 1,659 | 1,286 | -22% | 0 | 0 | — |
case-03 | fail→fail | 5,242 | 11,358 | +117% | 1 | 1 | 0% | 766 | 1,489 | +94% | 0 | 0 | — |
case-04 | fail→pass | 3,767 | 4,325 | +15% | 1 | 1 | 0% | 510 | 884 | +73% | 0 | 0 | — |
case-05 | fail→fail | 8,283 | 1,560 | -81% | 1 | 1 | 0% | 1,191 | 848 | -29% | 0 | 0 | — |
case-06 | fail→pass | 7,004 | 3,453 | -51% | 1 | 1 | 0% | 1,158 | 1,156 | -0% | 0 | 0 | — |
case-07 | fail→fail | 3,327 | 3,256 | -2% | 1 | 1 | 0% | 519 | 976 | +88% | 0 | 0 | — |
case-08 | fail→pass | 10,375 | 1,497 | -86% | 1 | 1 | 0% | 1,751 | 896 | -49% | 0 | 0 | — |
case-09 | pass→pass | 4,754 | 2,940 | -38% | 1 | 1 | 0% | 768 | 1,044 | +36% | 0 | 0 | — |
case-10 | fail→pass | 5,022 | 2,263 | -55% | 1 | 1 | 0% | 758 | 971 | +28% | 0 | 0 | — |
case-11 | pass→pass | 13,252 | 2,929 | -78% | 1 | 1 | 0% | 2,232 | 1,068 | -52% | 0 | 0 | — |
case-12 | fail→pass | 4,245 | 3,220 | -24% | 1 | 1 | 0% | 679 | 818 | +20% | 0 | 0 | — |
case-13 | fail→pass | 5,135 | 1,413 | -72% | 1 | 1 | 0% | 973 | 863 | -11% | 0 | 0 | — |
case-14 | fail→pass | 8,438 | 2,083 | -75% | 1 | 1 | 0% | 1,254 | 1,002 | -20% | 0 | 0 | — |
case-15 | pass→pass | 10,292 | 2,025 | -80% | 1 | 1 | 0% | 1,714 | 963 | -44% | 0 | 0 | — |
case-16 | fail→pass | 11,022 | 1,896 | -83% | 1 | 1 | 0% | 1,746 | 951 | -46% | 0 | 0 | — |
case-17 | fail→pass | 12,978 | 6,365 | -51% | 1 | 1 | 0% | 1,919 | 1,732 | -10% | 0 | 0 | — |
case-18 | fail→fail | 5,271 | 9,748 | +85% | 1 | 1 | 0% | 820 | 1,218 | +49% | 0 | 0 | — |
case-19 | pass→pass | 3,659 | 1,110 | -70% | 1 | 1 | 0% | 526 | 771 | +47% | 0 | 0 | — |
case-20 | pass→fail | 5,832 | 4,171 | -28% | 1 | 1 | 0% | 1,177 | 1,430 | +21% | 0 | 0 | — |
case-21 | pass→pass | 6,505 | 4,198 | -35% | 1 | 1 | 0% | 1,291 | 1,518 | +18% | 0 | 0 | — |
case-22 | pass→pass | 10,178 | 6,765 | -34% | 1 | 1 | 0% | 1,885 | 1,972 | +5% | 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 +36 percentage points is the difference between those two pass rates over the 18 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
The publisher has shipped newer versions since this run, so these numbers describe v1, not the version currently listed.
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.