Bolt-Pet/CLAUDE.md

CLAUDE.md

This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.

What this is

A desktop pet (PySide6 window) that is a voice/click UI on top of an external Bolt server's desk API — same brain, memory, tools, and persona as that server's Discord bot and Linux desk client. This repo has no import dependency on the server repo; it's a standalone HTTP client configured via its own .env.

Pipeline: mic → openWakeWord ("thunderbolt", on-device) / push-to-talk / click → record utterance → Deepgram STT → + active-window + screen-layout context → POST /desk/converse → [server may relay a shell command to run on this machine, or a petctl pseudo-command that moves/emotes the pet, jumps it to another monitor, reads a screen's text back, or plays a multi-voice scene instead] → reply (optionally tagged with a voice the server picked for it) → ElevenLabs streaming TTS (or offline pyttsx3 fallback) → speakers, with the pet sprite/speech bubble reflecting state throughout, and playback interruptible by talking over it (barge-in).

Because a relayed command's output goes back up the tool-result relay before the final reply, a petctl read mid-turn means Bolt can look at a monitor and then talk about what's on it in the same answer.

Side channels that let the pet act between turns: the heartbeat (proactive announcements), the desktop notification bridge, and autonomous wandering — all suppressed while it's napping (quiet hours / fullscreen DND).

Commands

# Setup + run (creates .venv and installs requirements.txt on first run)
./run.sh          # macOS/Linux
run.bat           # Windows

# Preflight: is this install actually going to work? (config, mic, keys, sprites…)
python -m bolt_pet --doctor          # shallow: no network, no mic
python -m bolt_pet --doctor --deep   # contacts the server and opens the microphone

# Run tests (no pytest config file — tests self-insert repo root via sys.path).
# QT_QPA_PLATFORM=offscreen avoids a QApplication segfault on headless/no-display hosts.
QT_QPA_PLATFORM=offscreen .venv/bin/pytest tests/
.venv/bin/pytest tests/test_state.py::test_happy_path_transitions  # single test

# Redraw the pet's sprite frames (the committed PNGs are this script's output)
python scripts/generate_bolt_sprites.py             # --out /tmp/x to preview first

# Convert a grid sprite sheet into the per-frame-PNG convention sprite.py expects
python scripts/slice_spritesheet.py path/to/sheet.png assets/sprites/idle --cols 6 --rows 1

There is no lint/build step configured beyond pytest. cp .env.example .env and fill in BOLT_SERVER_URL / DESK_API_KEY (+ DEEPGRAM_API_KEY, ELEVENLABS_API_KEY) before running — without server config the controller logs a missing-config message and exits its thread instead of starting.

Architecture

• config.py — loads .env from the project root (not via python-dotenv; a small hand-rolled parser matching the server repo's desk_client/bolt_desk.py convention) into module-level constants. Everything else reads config from here, never os.environ directly.
• state.py — PetStateMachine, pure logic with no Qt/audio imports (kept that way deliberately for cheap unit testing). Enforces a transition table; notably IDLE -> TALKING is legal directly (no LISTENING/THINKING leg) because the heartbeat can make the pet speak proactively/unprompted.
• controller.py — PetController(QObject), the pipeline orchestrator. Runs on a background QThread (wired in ui/app.py) so audio I/O/network never blocks the Qt event loop; communicates with the UI only through Qt signals (state_changed, said, log, action, napping), never touches a QWidget directly. Also drives the periodic heartbeat (_maybe_heartbeat, gated by HEARTBEAT_INTERVAL_SECONDS) which lets the server push proactive spoken announcements between user turns, and on the same tick re-evaluates nap state, checks for delivered files, and drains queued desktop notifications. It owns the live wake-word threshold (wake_threshold() is passed to listen_for_wake_word as a callable so the tray slider takes effect mid-listen) and the conversation history.
• server_client.py — HTTP client for the desk API, dependency-free beyond requests so it's easy to mock in tests. converse() loops relaying server-issued shell commands (run_local_command, executed via subprocess.run(shell=True) as the desktop user, 30s default timeout) via /desk/tool_result until the server sends a final reply (capped at _MAX_RELAY_HOPS). This is the same "full desktop control" trust model as the server repo's other desk clients — commands only ever originate from the user's own voice/click requests in their own session. A final reply is returned as a Reply(text, voice_id, voice_name) rather than a bare string, because the server can tag it with a voice — see "Voices" below. list_outbox_files / download_outbox_file hit the same /desk/files and /desk/files/<id> endpoints the server's deliver_files tool queues onto — see file_delivery.py.
• file_delivery.py — the filesystem half of receiving files the server queues via its deliver_files tool (ai/desk_api.py in the main tmn-api repo — "send me that report" during a conversation spools the matched workspace files, zipping multiple into one, onto the session's outbox). controller._check_deliveries lists /desk/files and downloads anything queued — right after a conversation/notification turn (the common case) and once per heartbeat tick for anything queued out-of-band — saving each under DELIVERED_FILES_DIR (default ~/Downloads/Bolt). Downloading a file dequeues it server-side, so it's only ever handed out once; save() never overwrites an existing download, suffixing " (1)", " (2)", ... on a name collision. sanitize_filename() reduces a server-supplied name to its bare filename (Path(...).name), which is defense-in-depth against a delivered name that's secretly a path, since a per-user desk API key means the name isn't always coming from someone as trusted as the owner. Toggle off entirely with RECEIVE_FILES=false.
• audio/ — mic.py (energy-based VAD utterance capture, ported from the server repo's bolt_desk.py), wake_word.py (openWakeWord thunderbolt.onnx detection + NearMissLog for threshold tuning — see below), stt.py (Deepgram), tts.py (ElevenLabs, streaming by default — stream_pcm() + play_stream() start playback on the first chunk; chunks_to_int16() carries odd bytes across HTTP chunk boundaries, without which everything after the first split sample plays as static — falling back to whole-clip PCM then offline pyttsx3; every entry point takes an optional voice_id overriding ELEVENLABS_VOICE_ID, and model_for() picks the multilingual model whenever there's an override or non-ASCII text, since the default eleven_flash_v2 is English-only and would read either as garbled phonetic English rather than failing), barge_in.py (two detectors behind one reset()/check() shape, chosen by BARGE_IN_MODE via make_detector: wake (default) scores every frame with the same openWakeWord model the idle listener uses, so only the wake phrase cuts playback; energy is the original N-consecutive-loud-frames rule, threshold ~4x the VAD one because the mic hears the pet's own voice. Wake mode shares _default_model with the idle listener — the two never run concurrently — and reset()s it on detection so the tail of one reply can't count toward the next). Each accepts an injectable stream/model/protocol so tests don't need real audio hardware or a display.
• pet_actions.py — petctl pseudo-commands (petctl move top-left, petctl emote wave, say/wander/nap, the screen verbs jump/monitors/read, and voice reset). The desk API has no "move the pet" payload type and this repo can't change the server, so these ride the existing shell-command relay: controller._handle_command parses them and they never reach subprocess; anything else is a real shell command exactly as before. Pure parsing; the UI half is PetWindow.apply_action. Note jump's target is not validated here — which monitors exist is a runtime fact this pure module doesn't have, so the spec passes through to monitors.resolve(). Query verbs (monitors, read) are answered in _handle_command rather than by pet_actions.describe(), because their output is the point: it goes back up the tool-result relay for Bolt to use in his reply — as is voice reset, which reports what it dropped since the server can't see which voice is in use. voice only ever resets: picking one is the server's job (speak_as, which it already knows how to use), so a petctl voice <name> attempt is an error pointing back at that marker.
• self_restart.py — petctl self_restart, the pet restarting itself so Bolt can see a code change he just made instead of waiting for a human to restart it. Three problems shape it, and all three are the interesting part. (1) The restart can't happen inline: killing the process mid-turn would drop the HTTP tool relay before the result was posted, leaving the server to wait out its timeout on a turn that can never finish — so the command only arms it (controller._arm_self_restart) and controller._maybe_self_restart fires it after the reply is spoken, the same "only between turns" rule the updater follows. (2) A broken edit must not be fatal, so preflight() imports the package in a subprocess before arming — this process holds the old modules, so an in-process import would pass on a file that no longer parses — and a SyntaxError comes back as the command's output, in the same turn, with the pet still running. (3) The reason has to outlive the process, so it's written to ~/.cache/bolt-pet/restart_context.json (never inside the repo Bolt is editing) and read on the way back up by controller._report_self_restart, which posts it to the server as an ordinary turn — that's what makes "restart and check the sprites load" finish as a spoken sentence rather than a silence. check_loop_guard refuses after SELF_RESTART_MAX restarts in SELF_RESTART_WINDOW_SECONDS, so an edit-restart-crash cycle stops itself. Off switch: SELF_RESTART=false.
• dialogue.py — dialoguectl pseudo-commands: a multi-voice scene through ElevenLabs' Text to Dialogue endpoint (audio/tts. synthesize_dialogue), checked in _handle_command between petctl and filectl. Same single-line-JSON wire format as filectl and for the same reason (the server's command marker captures only up to the next newline), and it accepts the ElevenLabs field names (inputs/voice_id) as well as its own (lines/voice) because the model has read that API and copying its shape is the obvious thing to try. Voices are named (DIALOGUE_VOICES maps names to ids) rather than pasted as raw ids, and self resolves to whatever voice the pet is speaking with right now — including a speak_as pick — so Bolt sounds like himself in his own scenes. The API's limits (10 distinct voices, ~2000 characters) are enforced before the request so a mistake comes back up the tool-result relay as a sentence Bolt can act on rather than an HTTP 422 he can't see. Unlike the normal reply path there is no streaming variant, so a scene is whole-clip: controller._play_dialogue plays it with the same bubble, transcript and barge-in handling a spoken reply gets, and returns to THINKING afterwards (not IDLE) because the server is still waiting on the tool result — that leg is why state.py allows TALKING -> THINKING.
• file_ops.py — filectl pseudo-commands, checked in _handle_command right after petctl and before falling through to a real shell command. Executing arbitrary commands already worked via the shell relay (run_local_command — see server_client.py below); what filectl adds is a reliable way to do the read/write/edit/list slice of that, since getting the model to hand-roll a shell heredoc for multi-line content full of quotes/$/backticks is failure-prone — and list exists as its own op (rather than relying on the model shelling out to ls/dir) because this project is cross-platform and the model shouldn't have to guess which listing command applies on Windows vs. Linux vs. macOS; one glob-based op (pattern, default *; recursive for rglob instead of glob) covers all three. Wire format is filectl <json> where <json> is a single-line compact JSON object — {"op": "list"|"read"|"write"|"edit", "path": ..., ...} — not a multi-line marker block (an earlier design): the server relays this as the argument to the ordinary command tool marker, and that marker's extractor (ai/agents/default.py in the main repo) only captures up to the next newline, so anything genuinely multi-line silently got truncated no matter how the prompt worded it. JSON sidesteps that for free — json.dumps already encodes embedded newlines as the two characters \n, not a real line break, so multi-line file content still fits on the one physical line the extractor sees. edit requires the old text to match exactly once — same discipline as this project's own code-editing tool — and raises rather than guessing if it's missing or ambiguous. This doesn't expand what the server can do to this machine (a relayed shell command could already overwrite anything the desktop user can write — see the security notes below); it's a safer path to the same capability. Pure parsing (parse) is separated from the filesystem I/O (execute), matching pet_actions.py's parse/describe split.
• screen_context.py — active-window title (xprop/xdotool, Win32, osascript) appended to each utterance via context_for(), plus is_fullscreen_active() for do-not-disturb. Text only — the desk API takes no images. Every probe is best-effort and returns None/False rather than raising; the parsing is split into pure functions that are tested without a display server.
• monitors.py — the screen layout, and resolving petctl jump targets (a 1-based number, a name, next/prev/primary/other, or a direction like left/up worked out from the actual geometry). Pure — no Qt, no subprocess. The monitor list is published by the UI (PetWindow.publish_monitors builds it from QGuiApplication.screens() and emits it over a queued signal to controller.set_monitors), because the controller and the window must agree on what "monitor 2" means: enumerating with xrandr on one side and Qt's screen list on the other gives different orderings on the same machine, and Bolt would announce one screen and land on another. Qt is the single source of truth; Monitor.index is 0-based and .number is the 1-based value used in every string a human or the model sees. The controller resolves a jump to a concrete index before emitting it, so the window can't re-resolve against a different list.
• screen_text.py — OCR, so Bolt can read what's on a monitor (petctl read [n|here|all]). Pull, not push: nothing captures on its own — the server has to ask, and the text goes back as that command's output. That's deliberate; OCR of a 4K screen costs a second or two that would otherwise be added to every utterance, and screen contents leaving the machine should be a visible decision rather than a constant. Capture needs mss (X11/Win32/macOS, not Wayland), recognition needs Tesseract or RapidOCR; both are optional and soft-fail with a reason the way hotkey.py does, and read_monitor() never raises because its return value is command output. Engine selection takes injected probes so it's testable wherever.
• quiet.py — quiet-hours spec parsing (23:00-08:00, wraps midnight, comma-separated). Napping suppresses proactive noise and wandering only; wake word / click / push-to-talk still work.
• notifications.py — Linux/D-Bus notification bridge. Two latency/loss bugs fixed 2026-08-02, both in controller._maybe_heartbeat: draining was wired to the 60s heartbeat interval rather than the ~1.2s wake tick, and a heartbeat that landed mid-conversation stamped its own clock before checking — burning the slot and waiting another full interval, repeatedly, which is how a notification could go unspoken for five or ten minutes. Draining now runs on every tick while IDLE, and the heartbeat clock only advances when the heartbeat actually runs. Separately, the rate limit used to drop notifications inside its window (a second text a minute later was silently lost); the filter still gates at queue time but the limit is gone — a burst is batched into one turn instead, same single round trip, no lost messages, capped by _MAX_PENDING_NOTIFICATIONS.
• notifications.py internals — tails dbus-monitor, parses Notify calls (pure iter_notifications()), filters and rate-limits them (NotificationGate), and the controller forwards survivors through converse(). Off by default — each one is a round trip.
• sudo_askpass.py — makes server-relayed sudo usable from a process with no terminal, by pointing sudo's SUDO_ASKPASS at a GUI helper and rewriting bare sudo to sudo -A (add_askpass_flag, a conservative regex that skips anything already carrying a flag and anything inside quotes). Prefers a real askpass binary and falls back to generating a zenity/kdialog wrapper in ~/.cache/bolt-pet/askpass.sh. Resolution order is injectable (is_executable/which) so it's testable on a machine with a different set installed. See the security notes — the dialog is the boundary.
• updater.py — self-update from the Gitea releases API. Polls <UPDATE_REPO_API>/releases/latest for a tag newer than bolt_pet.__version__ and moves the checkout to it with git fetch --tags + git checkout tags/<tag>, so "downloading an update" is just git and rolling back is one command. Three safety rules: a dirty working tree is skipped, never stashed (silently discarding your work-in-progress beats running an old version); everything after the checkout — dependency install, then an import smoke test in a subprocess (this process still has the old modules loaded, so importing in-process would prove nothing) — is guarded, and any failure rolls back to the exact ref that was live before, branch name or SHA; and the restart only happens once the new code imports, so a broken release costs a log line rather than a pet that won't start. Git goes through an injectable run(args) -> (code, output) callable so apply/rollback is unit-tested against a fake git; version comparison and release parsing are pure. controller._maybe_update drives it from the wake-listener tick (so the pet is IDLE and between turns by construction) and the actual os.execv happens in ui/app.py after app.exec() returns — that ordering is what guarantees the mic is released before the new process opens it.
• history.py — rolling transcript (HISTORY_LIMIT turns) behind the tray's History window and click-to-copy on the bubble.
• hotkey.py — global push-to-talk via pynput; soft-fails with a logged reason (Wayland, missing package, macOS permissions) since the wake word is the primary trigger.
• audio/stt_stream.py — streaming speech-to-text. The one-shot path waits for the utterance to end, uploads the whole WAV, then waits again; that second wait is dead time that grows with how long you spoke. Deepgram's live websocket removes it: record_utterance(on_frame=...) hands each captured frame to a StreamingTranscriber, so by the time the VAD decides you stopped the transcript is essentially already there. Three deliberate limits: open() returning None is an ordinary outcome (no websocket-client, no network, no key) because the full audio is still buffered and controller._transcribe just falls back; the local VAD still decides when you stopped rather than Deepgram's endpointing, since barge-in, follow-up listening and the grace period are all built on it and coupling them to the network is not a first-pass change; and the socket is per-utterance, because holding one open across an idle pet bills for silence and dies on the first blip. Off: STT_STREAMING=false.
• Streamed replies — server_client.converse_stream() reads NDJSON from the desk API's /desk/converse_stream and speaks each sentence as it arrives (controller._speak_stream_chunk), so the wait is time-to-first-sentence instead of the whole model call. Reply.spoken marks a reply whose sentences were already said: the text is still carried, because the follow-up rule needs to see whether it ended on a question, but it must not be read out again. A stream that fails before anything was spoken falls back to converse() invisibly; one that fails after ends the turn quietly rather than repeating the first half. Off: STREAMING_REPLIES=false.
• speech.py — everything the pet says, and the policy differences between kinds of saying. There were four near-copies of this in controller.py (a reply, a holding line, a streamed sentence, a dialogue scene), each repeating the same dance — transition state, show the bubble, maybe record history, reset barge-in, call TTS, reset barge-in again, resume state, decide whether to keep the mic open — and they had already drifted apart: one forgot to arm barge-in, another skipped the follow-up rule. Now the dance is Speaker.say() and the differences are data on a frozen Utterance (record, resume, hold_talking, follow_up, interruptible), built by the four classmethods reply/holding/stream_chunk/scene. Notable policies: a holding line is not recorded (it's filler; the transcript should keep the answer) and not interruptible (cutting off "give me a sec" strands the tool already running), and it resumes the state it interrupted rather than dropping to IDLE, because the turn isn't over. A streamed chunk stays TALKING so the sprite doesn't flicker between sentences. Collaborators are injected, so all of this is tested without Qt, audio or a real state machine. Two subtleties that are bugs waiting to happen: the barge-in detector is read through a callable, not held (it's built after the Speaker — it needs the mic stream — and swapped when the mode changes; two copies drifting apart is invisible until the wake model starts hearing the pet), and last_detail is captured before the post-playback reset, since reading it after means every interruption reports zeroed counters. follow_up_decision() is the mic-open rule as a pure function.
• Lip-sync — the mouth is driven by the audio, not a timer. audio/tts.level_of(frame) reduces a PCM frame to 0..1 loudness on a sqrt curve (speech sits well below peak most of the time, so a linear map leaves the mouth barely open during normal talking), envelope() does the same for a whole clip, and playback calls the on_level hook per chunk. That travels Speaker → controller.mouth (a Signal) → PetWindow.set_mouth, and _mouth_frame() indexes the talking frames directly — which works because generate_bolt_sprites.py draws them as an openness ramp (closed first, widest last) rather than an arbitrary loop. Levels going stale (_MOUTH_STALE_SECONDS) hands control back to the ordinary animation, so offline TTS — which has no envelope — degrades to the timed loop instead of freezing the mouth mid-syllable.
• window_state.py — where the pet was left, so it starts there. ~/.cache/bolt-pet/window.json, atomic write on drag-end, every function swallows its own errors (a corrupt state file must mean the default corner, never a pet that won't start). is_visible_on() re-validates against the current screen layout on load, because the common case for a stale position is exactly the dangerous one: the pet was last on a monitor that is now unplugged, and restoring it faithfully puts it somewhere unreachable. Takes plain rectangles rather than importing Qt. Off: PET_REMEMBER_POSITION=false.
• doctor.py — python -m bolt_pet --doctor, a preflight, written after a week of debugging things one command would have shown: a venv whose python was a zero-byte file, an OCR engine never installed, a wrong proxy header. Twelve independent checks, each reporting ok / warn (degraded but working) / fail, and each saying what to do about it — screen reading: warn is useless alone, apt install tesseract-ocr is the whole point. Nothing raises: a doctor that crashes on a broken install is diagnosing the wrong patient, so run() catches per-check and a failed check becomes a FAIL row rather than a traceback. Shallow by default (no network, no mic) since it's the first thing you reach for when the network is what's broken; --deep actually contacts the server and opens the microphone.
• speech_text.py — sanitizes server replies before they're heard/shown. for_speech() (called inside tts.speak(), so every path to the speakers is covered) strips markdown, emoji, URLs and stray symbols the voice would read literally ("asterisk asterisk"), turns bullet lists into full sentences, and words a few symbols (& → "and"). for_display() is the looser version for the speech bubble — markdown syntax gone, emoji kept. is_question() decides whether a reply leaves the pet waiting on an answer: it tests the spoken form (so a '?' inside a stripped code block or URL doesn't count) and only a trailing one counts, since a question asked in passing isn't awaiting a reply. speech.follow_up_decision uses it to keep listening without the wake word, capped by FOLLOW_UP_MAX_TURNS so a server that ends every reply with a question can't loop forever off mic noise. Pure string logic, no Qt/audio imports.
• ui/ — app.py wires QApplication + PetWindow + PetTray + the history/tuner windows + the push-to-talk hotkey + the controller thread together; pet_window.py is the frameless/translucent/always-on-top sprite window + speech bubble (non-square frames are centered in the square PET_SIZE window, see paintEvent), and also owns:
  • wandering — a ~30fps timer walks the window toward a random on-screen target every PET_WANDER_INTERVAL_SECONDS (randomized), suppressed whenever the pet is non-IDLE, napping, dragged, or has a bubble up. A commanded petctl move overrides all of that except the drag.
  • the walk cycle — while actually travelling, _animation_key() swaps the state animation for the side-view walk/ frames (not a PetState — see the sprites README). It is stepped by distance travelled (_WALK_PIXELS_PER_FRAME), never by the animation timer, so the planted paw tracks backwards at exactly the speed the window moves forwards; _advance_frame deliberately no-ops while walking so the two can't double-step it. The art is drawn facing right and _oriented() mirrors it (cached per frame) when heading left. No walk/ art → falls back to the old coded bob rather than a placeholder blob.
  • emotes — emote_transform() is pure maths (dx, dy, rotation, scale from a 0..1 progress) kept out of paintEvent so the curves are unit tested; every emote must return to the identity transform at progress 1.0 or the pet ends up permanently askew.
  • shaped input / click-through — PET_SHAPED_INPUT masks the window to the sprite's opaque pixels so the square window's transparent corners stop eating clicks (mask rebuilt only when the frame changes, and pinned to the resting position so a bob/spin doesn't thrash it); PET_CLICK_THROUGH makes the pet ignore the mouse entirely.
  • edge snapping (PET_EDGE_SNAP) after a drag or a stroll, and nap dimming (set_napping). sprite.py loads assets/sprites/<state>/*.png (filename-sorted, looping — the art is generated by scripts/generate_bolt_sprites.py, a Pillow drawing of Bolt as a shepherd pup; edit the script and re-run it rather than the committed PNGs, see assets/sprites/README.md) and falls back to a procedurally-drawn placeholder blob per state if a folder has no frames. It also loads EXTRA_ANIMATIONS — currently just walk/ — keyed by name rather than by PetState, with has() reporting whether a key is backed by real art so callers can decline a placeholder instead of trotting a blob across the desktop; tray.py is the system tray menu (talk now / mute / nap / wander / click-through / history / wake-word tuning / use-default-voice / quit) — the pet window has no title bar or taskbar entry; history_window.py and wake_tuner.py are the two dialogs it opens.

Voices (the server's speak_as)

Ask Bolt to talk like someone else, or in another language, and the server does the picking: its desk-only voice_search marker browses the ElevenLabs voice library, and speak_as: <voice_id> on the final reply tags that reply with the chosen voice (adding a Voice Library pick to the ElevenLabs account first, so the id is usable by the time it reaches us). Nothing about that is this repo's to decide — all the client owes it is actually speaking in the voice it was handed: converse() returns it on Reply, _apply_voice() records it, and _speak() passes it to tts.speak(voice_id=...).

Two things are decided here, though, because the server can't:

• The voice sticks (VOICE_STICKY, default on). The server tags one reply and strips the marker before storing the turn, so it never sees the id again — "keep talking like that" would send it searching for a voice all over again, and it'd likely land on a different one. Holding the id client-side is what makes the rest of the conversation stay in that voice. An untagged reply therefore never changes the voice; only a new speak_as, VOICE_STICKY=false, or a reset does.
• There's a way back. Since the server was never told Bolt's own voice id, it can't ask for it back with speak_as — so reverting is local: the tray's Use default voice entry (enabled only while a picked voice is in use, kept in sync by the voice_changed signal), a restart, or petctl voice reset, which is what lets Bolt honour "go back to your normal voice" out loud. That last one needs the server's pet prompt block (ai/desk_api.py, pet_tools) to mention the verb, or the model never emits it — the desk API's prompt is where petctl is advertised.

Wake-word detection

audio/wake_word.py uses a custom-trained openWakeWord model, thunderbolt.onnx (ships in the project root), the same way the server repo's desk_client/bolt_desk.py uses bolt.onnx for "hey bolt" — same runtime (openWakeWord, ONNX inference framework), same per-frame predict()/reset() loop. Every mic frame is scored; any class score at or above WAKE_WORD_THRESHOLD (default 0.5, in .env) counts as a detection. Swap WAKE_MODEL_FILE to point at a differently-trained .onnx model to change the wake phrase — everything downstream (STT, server call, TTS) is unaffected.

openwakeword's Model.reset() is not enough to forget a detection. It clears the prediction buffer only; the rolling audio window the classifier actually scores lives in model.preprocessor (raw_data_buffer — 10s of raw audio — plus melspectrogram_buffer and a ~120-frame feature_buffer) and AudioFeatures has no reset method at all. So after a detection the wake phrase is still in the window, and the next frame fed to the model re-fires on it. Symptom when this bites: the pet cuts itself off a word into every reply, because wake-mode barge-in resumes feeding the model and instantly matches the "thunderbolt" that started the turn. wake_word.hard_reset(model) restores the preprocessor to its as-constructed (silence) state and is what both listen_for_wake_word and WakeWordBargeIn.reset() call — use it, not reset(), anywhere a detection needs to be genuinely forgotten. The blank state is cached on the preprocessor object (not in an id()-keyed dict — CPython reuses ids after GC), since rebuilding it costs an ONNX pass over 10s of silence.

The threshold is tunable at runtime: the tray's Wake word tuning… window (ui/wake_tuner.py) shows the peak score seen and a rolling list of near misses (frames within WAKE_NEAR_MISS_MARGIN below the threshold — i.e. the times it nearly heard you), and its slider is read per frame because listen_for_wake_word accepts a callable threshold. Set the threshold just under the peak you can hit reliably, then persist it in .env.

Testing conventions

tests/ covers pure logic only (state machine, wake-word scoring loop, mic VAD, HTTP client against mocks) — nothing there needs real audio hardware or a display. Modules under test are written to accept fake streams/models/ on_command callables specifically to keep tests hardware-free; follow that pattern (inject a Protocol-typed collaborator) rather than mocking at the sounddevice/openwakeword import boundary when adding new testable logic. test_controller.py, test_controller_features.py, test_wander.py and test_pet_window_features.py need a QApplication, which segfaults without a display unless run with QT_QPA_PLATFORM=offscreen.

Newer subsystems follow the same rule — the testable part is separated from the part that needs hardware: dbus-monitor output is parsed by a pure iter_notifications(lines), xprop output by pure parse_xprop_* functions, HTTP chunk reassembly by chunks_to_int16, emote motion by emote_transform. Tests that touch the controller monkeypatch screen_context.context_for / is_fullscreen_active, otherwise they shell out to xprop on a headless box.

test_pipeline_smoke.py is the exception, deliberately. Unit tests inject a fake at the seam they care about, and a run of production bugs — notifications sitting unspoken for minutes, the pet saying things twice, [laughing] read aloud, a device command arriving as prose — got through with every one of them green, because each was an interaction between two individually-correct units. So that file stands up a real threaded HTTP server on a loopback port, speaks the desk protocol at it, and drives whole turns through the real server_client (including NDJSON streaming), the real controller and the real state machine, faking only the mic stream and the speakers. When a bug crosses a module boundary, add the case there; when it lives inside one module, the pure-function pattern above is still the cheaper test. It is also worth mutation-checking a new case — break the source line it is meant to catch and confirm it actually goes red.

Security notes

The server can relay a shell command back to this machine to execute as the desktop user (see server_client.run_local_command). This is intentional ("full desktop control" for things like "open firefox" or disk checks) and matches the trust model of the server repo's other desk clients. Keep DESK_API_KEY private and don't expose the desk API port to the open internet.

file_ops.py's filectl read/write/edit pseudo-commands ride that same relay and are bound by the same trust model — no path is off-limits beyond normal filesystem permissions for the desktop user, exactly like a relayed cat/sed/rm already isn't. They don't grant the server anything a shell command couldn't already do; they just make the read/write/edit path reliable instead of relying on the model getting shell quoting right.

sudo_askpass.py widens that further, by design: with SUDO_ASKPASS_PROMPT on (the default), a relayed bare sudo is rewritten to sudo -A and the password is collected in a desktop dialog, so commands can escalate to root instead of hanging on a tty the pet doesn't have. The dialog is the security boundary — it's the only thing between the server deciding to run sudo and it running, so the prompt is deliberately not suppressible per-command and those commands get their own longer timeout (SUDO_COMMAND_TIMEOUT_SECONDS) rather than being made non-interactive. Set SUDO_ASKPASS_PROMPT=false to take the capability away entirely; sudo commands then fail. Note that sudo -n / sudo -A / sudo -u … in a relayed command are never rewritten, so an explicit non-interactive sudo stays non-interactive.

Don't run the pet as root. It needs no privileges of its own, PortAudio can't reach the user's PipeWire socket from a root session (raw ALSA devices reject the 16 kHz capture rate — paInvalidSampleRate), and every relayed command would run unconstrained.

Several features widen what leaves this machine, all switchable in .env: SCREEN_CONTEXT appends the focused window's title to each utterance (titles often contain file paths, document names, or subject lines), MONITOR_CONTEXT appends the screen layout (sizes and names only — no contents), and NOTIFICATION_BRIDGE (off by default) forwards matching desktop notifications to the server. None of those send screenshots or notification contents you haven't matched with NOTIFICATION_FILTER.

SCREEN_TEXT is the biggest of them: petctl read OCRs a whole monitor and sends the recognised text to the server — everything visible, not just the focused window. Two things keep it honest. It's pull-only: no capture happens unless the server explicitly asks, so it can't leak in the background the way a per-turn annotation would, and each read is logged. And it is strictly not a new capability — the shell relay could already run a screenshot tool and pipe it through OCR — it just makes a thing the trust model already allowed reliable, bounded (SCREEN_TEXT_MAX_CHARS) and visible. It is nonetheless far easier to reach for than the shell route, so if that trade isn't one you want, SCREEN_TEXT=false removes it and petctl read starts reporting that it's disabled. Capture is mss-based and therefore silently unavailable on Wayland.