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Give the agent durable, local memory with engram — recall past decisions before answering, and persist new decisions, preferences and facts as they happen.
Passed 10 of 16 test cases with the skill, and 4 of 16 without it.
and 10 more in the full report
11 fixed3 passed either way6 still failing2 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 11 test cases the skill fixed. Same model, same prompt, with and without the skill.
Record that we switched our primary CDN vendor to Cloudflare on June 10, 2026.
| Checked | Without skill | With skill |
|---|---|---|
| Formats the date as 2026-06-10 at the start of the stored text. | Missed | Passed |
The instructions your model reads when the skill loads.
You have access to a local, private memory layer (engram). Treat it as your long-term memory: read it before you answer, write to it when something durable happens. Everything stays on the user's machine.
If the engram_recall / engram_remember MCP tools are available, use those.
Otherwise use the CLI (zero install):
npx @jnmetacode/engram serve & # HTTP API on :7077, or
npx @jnmetacode/engram mcp # as an MCP server
Before answering anything that may depend on prior context, search memory first:
npx @jnmetacode/engram recall "pricing decision" --since month
Quote the recalled passage with its citation (file/date) rather than paraphrasing from your own context — the citation is the point.
Persist a memory when the conversation produces something with a shelf life:
test cases passed, vs 4 / 16 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.