Deploy SAT & ACT Learning Lab 1.3.0

DEPLOYMENT_NOTES.md

@@ -1,4 +1,4 @@
-# Deployment notes — SAT & ACT Learning Lab 1.2.2
+# Deployment notes — SAT & ACT Learning Lab 1.3.0
 
 Target private Space: `DearmonAnalytics/SAT_ACT_Learning_Lab`
 
@@ -6,7 +6,7 @@ Turso database: `dearmon-sat-act-learning-lab`
 
 Schema namespace: `sat_act_learning_lab_v1`
 
-Encrypted-source secret: `SAT_ACT_APP_FERNET_V1_2_2_F48B2CACB56D_C9A0A3E35DD0`
+Encrypted-source secret: `SAT_ACT_APP_FERNET_V1_3_0_530ED11365EB_0A5895DF3462`
 
 ## Runtime contract
 
@@ -61,7 +61,7 @@ Encrypted-source secret: `SAT_ACT_APP_FERNET_V1_2_2_F48B2CACB56D_C9A0A3E35DD0`
 
 Core secrets are `OPENAI_API_KEY`, `TURSO_DATABASE_URL`, `TURSO_AUTH_TOKEN`,
 `LEARNER_ID_HMAC_SECRET`, `INSTRUCTOR_EXPORT_SECRET`, and
-`SAT_ACT_APP_FERNET_V1_2_2_F48B2CACB56D_C9A0A3E35DD0`. Live avatar video additionally uses optional
+`SAT_ACT_APP_FERNET_V1_3_0_530ED11365EB_0A5895DF3462`. Live avatar video additionally uses optional
 `SIMLI_API_KEY`.
 
 Email recovery uses `AUTH_RECOVERY_EMAIL_ENABLED`, `AUTH_SMTP_HOST`,
@@ -78,17 +78,17 @@ Release runtime defaults include `ACCOUNT_AUTH_ENABLED=1`,
 `ASK_PROFESSOR_SIMLI_DEFAULT=0`. Face IDs and the pinned Simli client module are
 listed in `environment.example` used to prepare this deployment.
 
-## v1.2.2 upgrade gate
+## v1.3.0 upgrade gate
 
 Finish or explicitly abandon every active timed simulation from an earlier
-release before installing v1.2.2. Checkpoints are intentionally bound to
+release before installing v1.3.0. Checkpoints are intentionally bound to
 app/source/form versions and cannot be resumed across this release. Back up
 Turso, preserve `LEARNER_ID_HMAC_SECRET`, and retain prior Fernet secrets and
 rollback commits needed through acceptance testing.
 
 ## Key lifecycle and rollback
 
-The current loader reads only `SAT_ACT_APP_FERNET_V1_2_2_F48B2CACB56D_C9A0A3E35DD0`. A later release must generate a
+The current loader reads only `SAT_ACT_APP_FERNET_V1_3_0_530ED11365EB_0A5895DF3462`. A later release must generate a
 new versioned, fingerprinted secret name. The deployer adds that secret before
 the repository commit and retains earlier keys. Roll back by reverting the
 Space repository to the prior commit recorded in `deployment_receipt.json`.

README.md

@@ -14,7 +14,7 @@ short_description: Dynamic SAT/ACT practice, mastery, and reasoning graphs
 
 # Dearmon Analytics SAT & ACT Learning Lab
 
-Release `1.2.2` provides original dynamic practice for current SAT and
+Release `1.3.0` provides original dynamic practice for current SAT and
 ACT skill blueprints, a four-level topic graph, individual mastery tracking,
 targeted practice, and test-like diagnostic sessions. It adds username/password
 accounts, secure recovery, an isolated one-click demo, and the embedded teaching
@@ -41,7 +41,7 @@ attribute-level mastery or retention records.
 This private Space uses the separate Turso database
 `dearmon-sat-act-learning-lab` and schema namespace
 `sat_act_learning_lab_v1`. Its encrypted application uses the versioned Space
-secret `SAT_ACT_APP_FERNET_V1_2_2_F48B2CACB56D_C9A0A3E35DD0`.
+secret `SAT_ACT_APP_FERNET_V1_3_0_530ED11365EB_0A5895DF3462`.
 
 Required Space secrets are `OPENAI_API_KEY`, `TURSO_DATABASE_URL`,
 `TURSO_AUTH_TOKEN`, `LEARNER_ID_HMAC_SECRET`, `INSTRUCTOR_EXPORT_SECRET`, and
@@ -83,9 +83,9 @@ controls, and retention policies.
 
 ## Upgrade note
 
-Before replacing any earlier release with v1.2.2, every active timed simulation
+Before replacing any earlier release with v1.3.0, every active timed simulation
 must be finished or explicitly abandoned. Saved forms are bound to the app
-version and encrypted-source fingerprint, so v1.2.2 intentionally rejects older
+version and encrypted-source fingerprint, so v1.3.0 intentionally rejects older
 checkpoints. Preserve `LEARNER_ID_HMAC_SECRET` and retain every prior versioned
 Fernet secret and rollback commit needed through acceptance testing.
 

release_manifest.json

@@ -1,18 +1,18 @@
 {
-  "built_utc": "2026-08-29T21:32:04+00:00",
+  "built_utc": "2026-08-29T22:32:47+00:00",
   "database_name": "dearmon-sat-act-learning-lab",
-  "encrypted_payload_sha256": "624ee6c73861cb1664781dea08df4acf727f0d93e4e04186947f777520d5fab1",
+  "encrypted_payload_sha256": "98e56749f08fcce8fb612d85b8c11ae9c01e626f50fb26dab52528d124127bca",
   "entry_module": "sat_act_lab.app_main",
-  "fernet_secret_name": "SAT_ACT_APP_FERNET_V1_2_2_F48B2CACB56D_C9A0A3E35DD0",
-  "key_fingerprint_sha256_prefix": "C9A0A3E35DD0",
-  "plaintext_archive_sha256": "f48b2cacb56d10f6d361cfdf5e01a4a4c4637f1b6b1ec01aab32bdbede584904",
-  "release_version": "1.2.2",
+  "fernet_secret_name": "SAT_ACT_APP_FERNET_V1_3_0_530ED11365EB_0A5895DF3462",
+  "key_fingerprint_sha256_prefix": "0A5895DF3462",
+  "plaintext_archive_sha256": "530ed11365eb79e0f0fdfb306594a7a316c95ac5e2ada567c5bbd87e8c0ba5ad",
+  "release_version": "1.3.0",
   "schema_namespace": "sat_act_learning_lab_v1",
   "schema_version": "dearmon-sat-act-release-v1",
   "source_members": {
     "sat_act_lab/__init__.py": {
       "bytes": 71,
-      "sha256": "31ffa487949f215cc8112867e1a4e0d3f371a63865950dd364a6b5f0fa999877"
+      "sha256": "ef21e2c2be0c32fa808b92839e04419e7ccaf2292f74c260631ffc779a69b511"
     },
     "sat_act_lab/act_english_bank.py": {
       "bytes": 79622,
@@ -27,12 +27,12 @@
       "sha256": "5959db1e6a007f9384978c2a65af82e9bfd81216db939a1d852a91852badef8e"
     },
     "sat_act_lab/app_main.py": {
-      "bytes": 240007,
-      "sha256": "ae4c6090cfded3affe6830fe04869d6a85868eaa6f8e54f763aa737134ad8929"
+      "bytes": 265227,
+      "sha256": "415448d1c144174d3c44b03c0755106c60a26f4a1e9c64dafd869027543418ae"
     },
     "sat_act_lab/attribute_graph.py": {
-      "bytes": 119752,
-      "sha256": "be633962117561ae8ee2f02cf43c1b1612d7faf79916d2af52c5596db1ec8b0a"
+      "bytes": 131004,
+      "sha256": "8188d65e7894a4c69aed1fdf9f8f4a0c7e90ca6055be44776fac8a39a7bb4dea"
     },
     "sat_act_lab/auth.py": {
       "bytes": 52719,
@@ -58,6 +58,10 @@
       "bytes": 100384,
       "sha256": "2abb720cd5a0a5a459cbcdc7ec1a8919ea50d7203cc809adccdefed4317e3a7f"
     },
+    "sat_act_lab/learning_concepts.py": {
+      "bytes": 51335,
+      "sha256": "202212791dc80fcc20b2dcc5d662c98e43f53db1e39b2b9992c8289183263346"
+    },
     "sat_act_lab/question_engine.py": {
       "bytes": 221287,
       "sha256": "929d8e424bbb845e69cab76f6e591f9bfb1f825c00733cd90efce089ea1a89a2"
@@ -74,15 +78,15 @@
     },
     "DEPLOYMENT_NOTES.md": {
       "bytes": 5663,
-      "sha256": "75c5b9f2879c7d8c3edf341a9193651d35fc803480cb03082ab94e09bd495057"
+      "sha256": "a7230ac72d71b5a18b9f4edfb119bdd0b0d00ac12b28124c271a88867abd8385"
     },
     "README.md": {
       "bytes": 5113,
-      "sha256": "834bcec62692addb45ba46e4936455adf94b202cc5ff9fce0c046b5474684e4e"
+      "sha256": "65a80c1d285d961b284528d8cd2e001737887ae1bcfff7c3fad24a3835da34fe"
     },
     "app.py": {
-      "bytes": 556664,
-      "sha256": "22acd56b8e17e47ab11c2eeea2ba2fc5f20e6a24db71f1d069f7fdc54f23e8f8"
+      "bytes": 595623,
+      "sha256": "c1b7e44eda4e80344fa7171883ac66a69cdb125be8026e80e15f03cb7afda0b1"
     },
     "requirements.txt": {
       "bytes": 287,