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Hybrid memory strategy combining OpenClaw's built-in vector memory with Graphiti temporal knowledge graph.
Passed 12 of 17 test cases with the skill, and 4 of 17 without it.
and 8 more in the full report
10 fixed5 passed either way7 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 10 test cases the skill fixed. Same model, same prompt, with and without the skill.
Formulate the exact command line invocation to log that user 'Carol' declared 'Prefers Python over Rust' into the primary agent group context.
| Checked | Without skill | With skill |
|---|---|---|
| Uses graphiti-log.sh main-agent user "Carol" "Prefers Python over Rust" format. | Missed | Passed |
The instructions your model reads when the skill loads.
Two memory systems, each with different strengths. Use both.
| Question Type | Tool | Example |
|---|---|---|
| Document content | memory_search | "What's in GOALS.md?" |
| Curated notes | memory_search | "What are our project guidelines?" |
| Temporal facts | Graphiti | "When did we set up Slack?" |
| Conversations | Graphiti | "What did the user say last Tuesday?" |
| Entity tracking | Graphiti | "What projects involve Alice?" |
Semantic search over markdown files (MEMORY.md, memory/**/*.md).
memory_search query="your question"
Then use memory_get to read specific lines if needed.
Search for facts with time awareness:
graphiti-search.sh "your question" GROUP_ID 10
Log important facts:
graphiti-log.sh GROUP_ID user "Name" "Fact to remember"
test cases passed, vs 4 / 17 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.