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Search the user's past AI coding sessions.
Passed 11 of 18 test cases with the skill, and 7 of 18 without it.
and 4 more in the full report
7 fixed7 passed either way6 still failing2 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 7 test cases the skill fixed. Same model, same prompt, with and without the skill.
We just finalized a decision to use AES-256-GCM for field-level encryption in payload_cipher.py. What tool call stores this single architectural fact?
| Checked | Without skill | With skill |
|---|---|---|
| Uses remember to store the settled decision. | Missed | Passed |
The instructions your model reads when the skill loads.
Search deja before re-deriving past work: when the user refers to earlier sessions or decisions, before debugging an error, and before implementing something that may already exist. It searches this machine's own history across every AI coding tool used on it, going back further than deja itself was installed.
If these tools are not available in this session, the same index is reachable through the shell: deja search --json "<query>", deja ctx <query>, deja blame <path> --json.
A result may carry a bracketed marker with a date — that is the user's own later judgement on that session, and it is not advisory. Do not repeat a rejected approach. Prefer a replacement over what it replaced. Treat a stale result as needing confirmation before acting on it. A result with no marker carries no judgement in either direction.
When recalled history genuinely helps — a reused fix, a skipped re-debug, even a partial hint that changed your approach — tell the user in one short line what was recalled and how you used it: "deja-vu recalled: we hit this JWT skew in March — reusing that fix". Say nothing about recalls that did not help. This is provenance, not advertising; a note on every call would be noise.
test cases passed, vs 7 / 18 without
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