Mem Capture Worker (session teardown)

This skill is designed to run as an isolated worker; when your harness cannot fork, run it inline and keep its output separate from the caller's.

You capture session learnings as wicked-estate memories before the
session ends (FOLD-3, Phase 5-S7 — the port of brain's session-teardown).
You run in a fork context: review the conversation, distill, and persist
through the mem backend. Estate owns everything after the write (salience,
decay, consolidation) — your job is selection and distillation, not
lifecycle.

Runtime

Script-backed steps use the wicked-garden launcher: wicked-garden run <plugin-root-relative path, e.g. scripts/…> [args]. It is on PATH after npm i -g wicked-garden; otherwise use npx wicked-garden run …; inside a wicked-crew run it is "$WICKED_GARDEN_ROOT/scripts/wicked-garden".
If none of these is available, or Python 3 is missing, skip the script-backed step, say so, and follow the manual alternative where one is given next to it — never invent the script's output.
Relative paths in this skill are relative to the directory that contains this SKILL.md.

Step 1 — sweep the conversation

Scan for content matching these shapes:

• Decisions: "we decided…", "going with…", "chose X over Y because…"
• Patterns / conventions: "this always…", "the convention is…"
• Gotchas: "watch out for…", "this broke because…", "don't X because…"
• Discoveries: "turns out…", "found that…", "learned that…"
• Preferences: "I prefer…", "always use…", "never do…"

Skip trivia and anything a future session can trivially re-derive from the
repo. If nothing valuable was discussed, store nothing and say so — an
empty capture is a valid result.

Step 2 — distill and classify

For each finding write a 1–3 sentence summary capturing the why and
what (not the how), then map it onto the estate vocabulary
(full table: skills/mem/refs/scopes.md):

Finding	kind	tier (default)
decision, preference, gotcha, stable fact	fact	semantic
pattern, convention, how-to	skill	procedural
discovery, event ("X happened when Y")	episode	episodic

Add 2–5 about tags per memory (systems, components, topics mentioned) so
recall has hooks. Scope: leave the default (project:<cwd-basename>)
unless the learning clearly belongs to another scope.

Step 3 — batch write

One backend call, JSON on stdin (never argv — summaries contain quotes):

printf '%s' '{"memories":[
  {"content":"<summary 1>","kind":"fact","about":["tag1","tag2"]},
  {"content":"<summary 2>","kind":"skill","about":["tag3"]}
]}' | wicked-garden run \
  scripts/mem/estate_memory.py capture-batch -

Build the JSON with real encoding discipline (escape newlines/quotes); a
malformed payload aborts before any write. The backend returns
{stored, failed, ids, failures} — treat failed > 0 as a partial result
and say which items were dropped and why.

Step 4 — verify one

Recall the most important stored memory to prove the round-trip:

wicked-garden run \
  scripts/mem/estate_memory.py recall '{"query":"<key phrase from it>"}'

Step 5 — report

• {N} memories stored (+ {K} failed if any), with kinds
• one-line topic list
• if estate was unreachable (ok: false): report the degrade explicitly so
  the learnings can be re-captured later — never claim success on a
  fail-open response.

Rules

• 1–3 sentences per memory; the why, never implementation dumps.
• Don't duplicate what's already stored — recall first when unsure.
• Never store secrets, tokens, or credentials, even when discussed.