Device Profile Generation Design

Goal

Build a self-contained device identity generation pipeline for producing many stable, self-consistent Android device profiles without depending on live APP runtime state.

The first deliverable is local identity generation and persistence. Online DFP bootstrap is intentionally separated into a later step because egid and cloud did are server-issued values and require a coherent deviceInfo payload.

Current Findings

Known runtime relationships:

oDid = "ANDROID_" + android_id
rdid = "ANDROID_" + md5(gRdi2)[16:32]
local fallback did can be generated independently
cloud did and cdid_tag override local fallback values after unifiedId refresh
egid is returned by DFP report, not derived by a local hash

Observed sample:

android_id=46a032e0a2af8184
oDid=ANDROID_46a032e0a2af8184
gRdi2=799999139::8641|899999556::8641|999999345::4741|899999995::8641|999999515::4741
md5(gRdi2)=be49e5841412c571741de4351c44850d
rdid=ANDROID_741de4351c44850d
did=ANDROID_e8dfd2f16b618053
cdid_tag=2

Scope

Included in phase 1

Generate local Android identity fields.
Generate a stable gRdi2 string and matching rdid.
Generate a local fallback did.
Persist and reload profiles without changing identities.
Apply server identity values later through an explicit update method.
Export profiles as JSON and .env snippets for other scripts.

Excluded from phase 1

Calling DFP/unifiedId services.
Producing a guaranteed valid egid.
Replacing main.py task runner behavior.
Reusing HAR files as runtime templates.

Architecture

`core/device_profile.py`

Owns device identity data and local generation rules.

Main objects:

DeviceProfile: serializable profile model.
DeviceProfileGenerator: creates new profiles from a random source.
load_device_profile(path): loads a persisted profile.
save_device_profile(profile, path): writes profile JSON.

`tools/new_device.py`

Small CLI wrapper around the core generator.

Responsibilities:

create one or more profiles
save JSON files
optionally print .env format
avoid depending on APP, Frida, HAR, or out/

Tests

tests/test_device_profile.py verifies:

android_id is 16 lowercase hex characters
oDid matches android_id
rdid matches md5(gRdi2)[16:32]
persisted profile reloads identically
cloud identity update changes did, cdid_tag, and egid only when explicit

Data Flow

new_device.py
  -> DeviceProfileGenerator.new_profile()
  -> DeviceProfile.to_dict()
  -> save JSON / print env

existing JSON
  -> load_device_profile()
  -> use stable identity in request builders

server response
  -> profile.apply_cloud_identity(did, cdid_tag, egid)
  -> save JSON

Error Handling

Invalid android_id, did, oDid, rdid, or egid values raise ValueError.
Loading malformed JSON raises ValueError with the file path.
Existing output files are not overwritten unless the CLI receives --force.
Batch generation creates separate files and fails fast on duplicate filenames.

Testing Strategy

Use TDD:

Write failing tests for local generation and persistence.
Implement minimal core code to pass tests.
Add CLI tests or smoke checks.
Run focused tests and compile checks before claiming completion.

Future Phase

After phase 1, add an online bootstrap layer:

DeviceProfile
  -> build DFP fetch/check/repair/report forms
  -> call unifiedId / gdfp report
  -> apply cloud did / cdid_tag / egid

That layer should migrate useful code out of out/build_dfp_*.py into core/ without making main.py depend on HAR templates.

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