Storage & Context Building

The CLI writes the three kinds of data pushed from the phone — notifications, recordings (including transcripts / summaries), and images — all to local files, then uses a set of commands to turn them into context an Agent can consume directly.

This chapter has two halves: the first covers how each of the three data types gets written to disk, and the second covers how the CLI turns that on-disk data into context. For where the storage directory sits within a profile, see Architecture & Implementation · Profiles; for how clientLabel tagging works across multiple accounts, see Multi API-Key Design.

Design philosophy: disk writes are the source of truth, reads are context

All three data types share the same underlying approach:

1. Everything is written to a local file first — notifications are per-day JSON, recordings / images each have an index.json plus entity files, and status is driven by a state machine. The files are the source of truth; whether the daemon is online has no effect on "reading."
2. Read and write control are separated — query commands (🟢) do pure disk reads, so they work right after a cold start, no daemon required.
3. A consistent trio across data types — each type has: a metadata index, a sync / transcription state, a clientLabel source tag, and a resolvable local path. An Agent either gets a complete result or a clear "not ready yet" error — it never gets a partial result.

profiles/<name>/
├── notifications/          # per-day JSON + dedup indexes
│   ├── 2026-05-25.json
│   ├── .ids/               # dedup by id
│   └── .keys/               # dedup by content fingerprint
├── recordings/
│   ├── audio/              # raw audio (.ogg)
│   ├── transcript-data/    # transcript JSON (primary storage)
│   ├── transcripts/        # transcript text .md (derived)
│   ├── summaries/          # summary .md (derived)
│   ├── index.json          # metadata + state index
│   ├── asr-config.json     # local ASR config
│   └── state/events.jsonl  # state event stream
└── images/
    ├── index.json          # metadata + sync state
    └── files/              # downloaded image files

Notifications: per-day JSON with double deduplication

Notifications are written per day to notifications/YYYY-MM-DD.json (a JSON array, append-only). Each StoredNotification looks like:

{
  "clientLabel": "phone-a",
  "appName": "com.tencent.xin",
  "appDisplayName": "WeChat",
  "title": "Mr. Wang",
  "content": "The 3pm meeting moved to 4pm",
  "timestamp": "2026-05-25T14:02:11+08:00",
  "senderName": "Mr. Wang",
  "conversationType": "private"
}

A few key points:

• appDisplayName is filled in by the backend's app-name-map — the package name com.tencent.xin is resolved to "WeChat" at write time, making the context more readable for both humans and models.
• IM apps like Lark get structured splitting — title / subtitle are normalized into senderName / conversationType / conversationName, so group chats and DMs can be distinguished.
• Double deduplication: .ids/ deduplicates by the notification's id, and .keys/ deduplicates by a SHA-256 content fingerprint of clientLabel+app+title+content+timestamp. The same message being re-pushed by the phone, or delivered twice under multiple keys, won't land twice. Dedup happens per clientLabel — the same content received by different accounts doesn't shadow each other.
• Retention: data files and both indexes are automatically cleaned up once retentionDays expires.

Recordings: audio → transcript JSON → summary

Recordings have the longest pipeline, written across a four-level directory structure strung together by index.json. Recording results are written by the app / cloud via recordings.result.write:

recordings.result.write (transcript/summary, optional ossUrl)
  ↓  entry written to index.json (status=synced on creation)
transcript-data/<id>.json   ← primary transcript storage (structured: title/summary/text/segments)
transcripts/<id>_<title>.md  ← body text (derived)
summaries/<id>.md           ← summary (derived)
  ↓  optional: if ossUrl is present, download the audio in the background → audio/<id>.ogg
  ↓  status=transcribed

When you need to re-transcribe locally on the daemon's machine, recordings.retranscribe triggers it using local / request-level ASR config: status=transcribing → ASR (api/model-proxy, falls back to the account ock- key by default) → transcribed.

Key design points:

• The JSON in transcript-data/ is the primary storage; the text in transcripts/ and the summary in summaries/ are both derived from it. readTranscript / readSummary prefer reading the JSON, falling back to Markdown only for legacy data — keeping old and new data on the same footing.
• title / summary built in: finishing a transcription also produces a short title and a summary, written into the index and summaries/. When an Agent wants a quick sense of "what's this recording about," reading the summary is enough — no need to pull the full text.
• A strictly validated state machine: transfer_status transitions synced → transcribing → transcribed (or transcribe_failed) via a state machine, and illegal transitions are rejected outright. Writing a result via result.write is an out-of-band disk write, and goes straight to transcribed. A transcribing state left over from a daemon restart is treated as interrupted and set to transcribe_failed.
• Event stream: every state change is appended to state/events.jsonl; yc recording events --id <id> --watch can follow it like tail -f, and the same event is also pushed back to the phone via Relay as recording.status.

ASR config is written to recordings/asr-config.json (generated by yc recording setup-asr); when mode=api is missing a key, it falls back to the account-level ock- key. The current Go beta only supports api / model-proxy; the local mode is kept in the schema for compatibility with older requests, but is rejected during validation.

Images: a download pipeline mirroring recordings

Images arrive via POST /images, are written to images/index.json, and then downloaded from OSS as a background stream to images/files/<id>.<ext>:

POST /images → entry written to index.json, status=syncing
  ↓  background fetch(ossUrl) streamed to files/<id>.<ext>
status=synced (failure → sync_failed, with lastError recorded)

image path <id> returns YOOOCLAW_IMAGE_NOT_READY rather than a partial file while the download is still in progress — so when an Agent feeds the local path to a multimodal model, it either gets the complete image or a clear "not ready yet." index.json also carries metadata like clientLabel, source_app, and caption.

How the CLI helps you build context

The disk writes are just raw material — the CLI's command layer turns them into context an Agent can use easily. There are six core mechanisms:

1. Presets for high-frequency scenarios (shortcuts)

The + prefix hardcodes the most common queries into a single command, so an Agent doesn't have to assemble a pile of flags:

yc notification +today      # today's notification summary
yc notification +recent     # the last hour
yc recording +latest        # the most recent recording (with title/summary/transcript)
yc image +latest            # details for the most recent image

2. Aggregation commands built for "summarize this"

notification summary doesn't just spit out notifications one by one — it returns aggregate statistics + recent samples together, exactly the shape an Agent needs to write a summary; notification stats supports multi-dimensional aggregation by date|app|sender|hour|client:

yc notification summary --from 2026-05-25T00:00:00+08:00 --sample 30 --top 10
yc notification stats --dim app --from 2026-05-20

3. Incremental cursors for feeding a memory system

The sync service (scan / fetch / commit) provides a cursor scheme designed for "continuously pipe notifications into a memory system": scan finds how much is pending per date, fetch gets a given date's details and returns endIndex, and commit marks that batch processed using that endIndex. Re-running doesn't re-ingest, and it can resume from where it left off.

4. Recordings read "summary-first"

recording status <id> / +latest return title + summary + transcript, so an Agent can look at the summary first and decide whether to expand the full text, saving tokens:

yc recording +latest                  # the latest recording's title + summary + text
yc recording list --status transcribed --client phone-a

5. Images provide a "local absolute path" for multimodal input

image +latest / image path <id> return the local file's absolute path directly, for an Agent to feed to a multimodal model; if the download hasn't finished, they return YOOOCLAW_IMAGE_NOT_READY instead of a partial file.

6. An Agent-facing output contract

All commands support a unified --format json|pretty|table|ndjson:

• ndjson puts one JSON result per line, ideal for an Agent to stream and consume with backpressure;
• Success { "ok": true, ... } / failure { "ok": false, "error": { "code": "YOOOCLAW_*", ... } } share stdout; local CLI errors additionally set a non-zero exit code, and raw HTTP results should also be checked for ok / HTTP status;
• --client <label> splits context by account; --client all means no filter.

Putting these together, building context for "summarize what happened on my phone today" looks like:

yc notification summary +today      → today's aggregated notifications + samples
yc recording list --status transcribed
  └─ yc recording status <id>       → read the summary for the recordings picked out
yc image +latest                    → the latest image's local path, for a multimodal model
            ↓  all with --format ndjson / json
        Agent assembles context → answers

Next steps

• Command Reference — every subcommand and flag for notification / recording / image / sync.
• Agent Skill — how an Agent drives these commands automatically.
• Architecture & Implementation — the full picture of the daemon, ingest, and the recording state machine.
• Multi API-Key Design — where clientLabel comes from, and how to switch between accounts.