Common issues, Developer Mode, version compatibility, state machine diagnosis
npx skills add https://github.com/facebook/meta-wearables-dat-ios --skill debugging
Diagnose common setup, registration, and streaming issues in DAT SDK integrations.
If local DAT Inspector MCP tools are available, use the live-debugging-mcp
skill before changing app code. Prefer get_dat_readiness,
get_companion_boundary_diagnosis, and get_device_path to separate app bugs from
Meta AI app/device boundary issues using read-only DAT evidence.
Device not connecting?
│
├── Is Developer Mode enabled? → Enable in Meta AI app settings
│
├── Is device registered? → Check registration state
│
├── Is device in range? → Bluetooth on, glasses powered on
│
├── Is the app registered? → Check registrationStateStream()
│
└── Stream stuck in waitingForDevice? → Check device availability
Developer Mode must be enabled for 3P apps to access device features.
waitingForDevicestopped → waitingForDevice → starting → streaming → stopped
Ensure compatible versions of SDK, Meta AI app, and glasses firmware. See version dependencies for the current compatibility matrix.
| Issue | Workaround |
|-------|-----------|
| No internet → registration fails | Internet required for registration |
| Streams started with glasses doffed pause when donned | Unpause by tapping side of glasses |
| [iOS] Meta Ray-Ban Display: no audio feedback on pause/resume | Will be fixed in future release |
import os
private let logger = Logger(subsystem: "com.yourapp", category: "Wearables")
// In your streaming code:
logger.debug("Stream state changed to: \(state)")
logger.error("Stream error: \(error)")
When the developer has a local DAT debug server connected to the agent:
get_sdk_state and get_dat_readinessget_companion_boundary_diagnosis for permission, app identity, devicecapability, device-selection, deeplink-return, and DAM/DWA-visible blockers
get_device_path to trace app -> DAT registration -> Meta AI app permissions ->device selection/link -> session -> stream
wait_for_events with thenarrowest category or source that matches the flow
export_diagnostic_bundle for redacted support handoffDo not treat this as companion app introspection. It is diagnosis from
app-visible DAT debug events.
Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
Use when implementation is complete, all tests pass, and you need to decide how to integrate the work - guides completion of development work by presenting structured options for merge, PR, or cleanup
Use when implementing any feature or bugfix, before writing implementation code
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Use when about to claim work is complete, fixed, or passing, before committing or creating PRs - requires running verification commands and confirming output before making any success claims; evidence before assertions always
Expert guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading development.
Cloud laboratory platform for automated protein testing and validation. Use when designing proteins and needing experimental validation including binding assays, expression testing, thermostability measurements, enzyme activity assays, or protein sequence optimization. Also use for submitting experiments via API, tracking experiment status, downloading results, optimizing protein sequences for better expression using computational tools (NetSolP, SoluProt, SolubleMPNN, ESM), or managing protein design workflows with wet-lab validation.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Take facebook/debugging from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.