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## HANDOFF CONTEXT
GOAL
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Continue implementation of IRT-Powered Adaptive Question Bank System after user configures GLM-5 model mapping for specific subagent categories.
WORK COMPLETED
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- Created comprehensive PRD (v1.1) from project-brief.md
- Resolved 10 critical clarification questions with client:
  1. Excel Import: Standardized across ALL tryouts
  2. AI Generation: 1 request = 1 question, admin playground for testing, no approval workflow
  3. Normalization: Optional manual/automatic control (system handles auto when sufficient data)
  4. Rollback: Preserve IRT history, apply CTT to new sessions only
  5. Admin Permissions: Not needed (WordPress handles per-site admins)
  6. Dashboards: FastAPI Admin only
  7. Rate Limiting: User-level reuse check + AI generation toggle
  8. Student UX: Admin sees internal metrics, students only see primary score
  9. Data Retention: Keep all data
  10. Reporting: All 4 report types required
- Created detailed technical implementation plan with 10 parallel subagents:
  - Deep Agent 1: Core API + CTT Scoring
  - Deep Agent 2: IRT Calibration Engine (recommended for GLM-5)
  - Deep Agent 3: CAT Selection Logic (recommended for GLM-5)
  - Deep Agent 4: AI Generation + OpenRouter (recommended for GLM-5)
  - Deep Agent 5: WordPress Integration
  - Deep Agent 6: Reporting System (recommended for GLM-5)
  - Unspecified-High Agents: Database Schema, Excel Import/Export, Admin Panel, Normalization
CURRENT STATE
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- PRD.md file created (746 lines, v1.1)
- project-brief.md exists (reference document)
- No code implementation started yet
- No git repository initialized
- Working directory: /Users/dwindown/Applications/tryout-system
- Session ID: ses_2f1bf9e3cffes96exBxyheOiYT
PENDING TASKS
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1. User configures GLM-5 model mapping for `deep` category (GLM-5 for algorithmic complexity)
2. User configures GLM-4.7 model mapping for `unspecified-high` category (general implementation)
3. Initialize git repository
4. Create project structure (app/, models/, routers/, services/, tests/)
5. Launch Unspecified-High Agent 1: Database Schema + ORM (BLOCKS all other agents)
6. After schema complete: Launch Deep Agents 1-3 in parallel (Core API, IRT Calibration, CAT Selection)
7. Launch Deep Agents 4-6 + Unspecified-High Agents 2-4 in parallel (AI Generation, WordPress, Reporting, Excel, Admin, Normalization)
8. Integration testing and validation
KEY FILES
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- PRD.md - Complete product requirements document (v1.1, 746 lines)
- project-brief.md - Original technical specification reference
IMPORTANT DECISIONS
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- 1 request = 1 question for AI generation (no batch)
- Admin playground for AI testing (no approval workflow for student tests)
- Normalization: Admin chooses manual/automatic; system handles auto when data sufficient
- Rollback: Keep IRT historical scores, apply CTT only to new sessions
- No admin permissions system (WordPress handles per-site admin access)
- FastAPI Admin only (no custom dashboards)
- Global AI generation toggle for cost control
- User-level question reuse check (prevent duplicate difficulty exposure)
- Admin sees internal metrics, students only see primary score
- Keep all data indefinitely
- All 4 report types required (Student, Item, Calibration, Tryout comparison)
EXPLICIT CONSTRAINTS
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- Excel format is standardized across ALL tryouts (strict parser)
- CTT formulas must match client Excel 100% (p = Σ Benar / Total Peserta)
- IRT 1PL Rasch model only (b parameter, no a/c initially)
- θ and b ∈ [-3, +3], NM and NN ∈ [0, 1000]
- Normalization target: Mean=500±5, SD=100±5
- Tech stack: FastAPI, PostgreSQL, SQLAlchemy, FastAPI Admin, OpenRouter (Qwen3 Coder 480B / Llama 3.3 70B)
- Deployment: aaPanel VPS with Python Manager
- No type error suppression (no `as any`, `@ts-ignore`)
- Zero disruption to existing operations (non-destructive, additive)
GLM-5 MODEL ALLOCATION RECOMMENDATION
-----------------------------------
Use GLM-5 for:
- Deep Agent 2: IRT Calibration Engine (mathematical algorithms, sparse data handling)
- Deep Agent 3: CAT Selection Logic (adaptive algorithms, termination conditions)
- Deep Agent 4: AI Generation + OpenRouter (prompt engineering, robust parsing)
- Deep Agent 6: Reporting System (complex aggregation, multi-dimensional analysis)
Use GLM-4.7 for:
- Deep Agent 1: Core API + CTT Scoring (straightforward formulas)
- Deep Agent 5: WordPress Integration (standard REST API)
- Unspecified-High Agents: Database Schema, Excel Import/Export, Admin Panel, Normalization (well-defined tasks)
NOTE: Model mapping is controlled by category configuration in system, not by direct model specification in task() function.
CONTEXT FOR CONTINUATION
------------------------
- User is currently configuring GLM-5 model mapping for specific categories
- After model mapping is configured, implementation should start with Database Schema (Unspecified-High Agent 1) as it blocks all other work
- Parallel execution strategy: Never run sequential when parallel is possible - all independent work units run simultaneously
- Use `task(category="...", load_skills=[], run_in_background=true)` pattern for parallel delegation
- All delegated work must include: TASK, EXPECTED OUTCOME, REQUIRED TOOLS, MUST DO, MUST NOT DO, CONTEXT (6-section prompt structure)
- Verify results after delegation: DOES IT WORK? DOES IT FOLLOW PATTERNS? EXPECTED RESULT ACHIEVED?
- Run `lsp_diagnostics` on changed files before marking tasks complete
- This is NOT a git repository yet - will need to initialize before any version control operations
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