* fix(caldav): don't prune the whole window when no objects could be parsed
The post-sync prune deletes local origin=="caldav" rows in the window whose UID
the server didn't just return. With an empty seen_uids it falls back to
`uid.isnot(None)` — a match-all delete. That's right when the calendar is
genuinely empty, but when the server returns objects and every one fails to
parse (malformed iCal / an icalendar error), seen_uids is empty only because
nothing could be read, so the match-all branch silently deletes every local
event in the 90-day-back/365-day-forward window.
Track whether any object failed to parse and gate the prune with a small pure
helper `_should_prune_window(seen_uids, parse_failed)`: prune when something was
read, or when the calendar is genuinely empty (no objects, no parse errors), but
never when objects came back unreadable.
Adds tests/test_caldav_prune_parse_failure.py for the three cases.
* fix(caldav): skip the prune on any parse failure, not just total
Review follow-up (#3454): _should_prune_window returned True whenever seen_uids
was non-empty, so a partial parse failure (say 48 of 50 objects parse) still
pruned the 2 unreadable-but-still-upstream events, because their UIDs were absent
from seen_uids. Any parse failure makes seen_uids an incomplete view of the
server, so pruning against it is unsafe whether the failure is total or partial.
Skip the prune on any parse failure (return not parse_failed); only prune on a
clean read (a genuinely empty window is still safe to prune). Tradeoff: one
permanently-unparseable event pauses deletion mirroring until it is fixed, which
is the safe direction (false-keep beats false-delete).
Replace the now-incorrect "partial failure still prunes" assertion with a
partial-failure regression: one object parses, one fails, so the prune is
skipped and the unparsed event's local copy is not deleted.
---------
Co-authored-by: Kenny Van de Maele <kenny@kvandemaele.be>
* fix(memory): only delete memories the model explicitly drops in tidy
The AI memory-tidy path computed deletions as the complement of the model's
`keep` list (`if mid not in keep_ids: continue`). When the model returned a
valid response that simply omitted some existing ids — a common LLM lapse — every
omitted memory was silently deleted, even though it was neither a duplicate nor
listed in `drop`.
Honor the explicit `drop` set instead: delete only ids the model dropped (minus
any it saw only truncated), and preserve everything else, still applying cleaned
text/category from `keep`.
Adds tests/test_consolidate_memory_explicit_drops.py: a memory the model omits
from both keep and drop survives; an explicitly dropped one is removed.
* refactor(memory): remove now-dead keep_ids from tidy
After deletion switched to drop_ids and text/category rewrites to cleaned_by_id,
keep_ids was written but never read. Remove the init, the .add(mid) in the keep
loop, and the truncated .update() (its truncated-protection is already covered by
`drop_ids -= truncated_ids`). Pure deletion, no behavior change; tests stay green.
Addresses review feedback on #3455.
---------
Co-authored-by: Kenny Van de Maele <kenny@kvandemaele.be>
maybe_extract_skill() sliced the LLM response from the first '{' to the
last '}'. When a model emits a stray brace in prose before the real
object (e.g. "uses {placeholder} then {...}"), the slice starts at the
prose brace, json.loads fails, and a valid skill is silently dropped.
Factor parsing into _extract_json_object(), which tries the whole
(de-fenced) string first and then each '{' start position, returning the
first candidate that parses to a JSON object.
Adds tests/test_skill_extractor_json.py.
