User pushback: "что теперь делает сон? все связано?" — Sleep Phase B
was reading only `traces/`, ignoring the four tracking journals shipped
in the previous commit. Cloud agent had a partial view of what happened.
This commit closes the loop. Sleep now sees everything that's tracked.
PUSH SIDE — `kei-sleep-sync.sh` (called on every Stop event)
Now mirrors the full observability surface into the memory-repo:
~/.claude/memory/time-metrics/sessions.jsonl → time-metrics/
~/.claude/memory/time-metrics/tasks.jsonl → time-metrics/
~/.claude/memory/time-metrics/numeric-claims.jsonl → time-metrics/
~/.claude/memory/time-metrics/agent-toolstats.jsonl→ time-metrics/
~/.claude/agents/ledger.sqlite agents table → ledger/agents.jsonl
~/.claude/agents/ledger.sqlite skill_invocations → ledger/skill_invocations.jsonl
Format: JSONL (one row per object). The two ledger tables are dumped
via `sqlite3 + json_object()` so cloud agents can stream-parse into
pandas / duckdb without binary-file handling.
First sync moved 6 files / 638 rows from local to remote — verified
by `git show --stat` of the resulting `memory: session traces` commit.
CONSUME SIDE — `phase-b-rem.sh` REM-consolidation report
Each nightly `reports/sleep-YYYY-MM-DD.md` now ends with a "Tracking
observability (last 7 days)" section containing four jq-aggregated
digests:
1. Agent outcomes — per-model: n, functional/partial/scaffolding/fail
counts + total_cost_usd. Lets the agent see whether the model-tier
refactor (cb1fdde) actually paid off and whether Sonnet success
rate justifies routing more task classes to it.
2. Skill success rates — per-skill: n, successes, rate_pct. Drives
Phase D nightly decisions (archive unused / re-extract failing /
mark validated). Empty until Skill tool is invoked in the next
session.
3. Numeric-claims tier breakdown — REAL / FROM-JOURNAL / ESTIMATE-HTC
counts. High ESTIMATE-HTC ratio = orchestrator under-calibrated.
Cloud agent's job: spot frequent ESTIMATE-HTC categories and
propose conversion to FROM-JOURNAL via measured runs.
4. Agent tool-call patterns — mean tool_use_count, mean duration_ms,
per-tool total calls. Lets the agent see "this code-implementer
spawn made 30 Read but 1 Edit — was tier-allocation correct?".
All four sections gracefully skip if the source JSONL is missing or
empty. jq is the only new dependency (already present per existing
phase-b checks).
What is NOT yet automated:
- The cloud agent's prompt template doesn't yet INSTRUCT it to act
on these digests. Currently the digest is data; whether the agent
proposes rule + hook codification based on it depends on the
free-text instructions in the schedule. Follow-up: codify a Phase B
instruction block that maps each digest to a recommendation pattern.
- Idempotency on `cp` for time-metrics: I use plain `cp` (not `cp -n`)
so the latest local state always overwrites remote. The journals are
append-only on the local side, so this is safe — but if two machines
ever share one memory-repo it would corrupt. Out of scope for
single-machine setup.
=== STATUS-TRUTH MARKER ===
shipped: functional
stubs: 0
cargo-check: NOT-RUN (pure shell)
behaviour-verified: yes
follow-up-required:
- Phase B prompt template — instruct cloud agent to act on the four
digests (codify recurring patterns, calibrate ESTIMATE-HTC).
- skill_invocations.jsonl will populate from next session onward.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
