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AgentProbe Development Tasks

Task Management Style Guide

Priority Levels

  • 🔴 P0 (Critical): Must be done immediately, blocks other work
  • 🟠 P1 (High): Important features, should be next in queue
  • 🟡 P2 (Medium): Nice to have, implement when P0/P1 done
  • 🟢 P3 (Low): Future enhancements, not urgent

Task Format

- [ ] [P#] Task description (Assignee: @name) [Status: Not Started/In Progress/Done]
  - [ ] Subtask 1
  - [ ] Subtask 2

Status Tracking

  • Not Started: Task hasn't begun
  • In Progress: Currently being worked on
  • Blocked: Waiting on dependencies
  • Review: Implementation complete, needs review
  • Done: Fully complete and tested

Completion Tracking

  • Mark tasks with [x] when complete
  • Add completion date: [Done: 2025-01-20]
  • Strike through entire line when archived: ~~- [x] [P1] Completed task~~

Phase 1: Results Storage System

🟠 P1: Implement Result Persistence

  • [P1] Add SQLite database for storing test results [Status: Not Started]
    • Create database schema (results, traces, metadata)
    • Add storage.py module with ResultStore class
    • Update runner.py to save results after each test
    • Add result retrieval methods
    • Add database migrations support

🟠 P1: Result History Management

  • [P1] Track results over time for trend analysis [Status: Not Started]
    • Add timestamp and version tracking
    • Implement result comparison methods
    • Create result aggregation queries
    • Add cleanup for old results (configurable retention)
    • Add result tagging/labeling system

🟡 P2: Export Functionality

  • [P2] Add CSV/JSON export for results [Status: Not Started]
    • Export single test results
    • Export benchmark results
    • Export aggregated statistics
    • Add filter options for exports
    • Support bulk export operations

Phase 2: Enhanced Analysis Engine

🟠 P1: Command Pattern Analysis

  • [P1] Parse actual CLI commands from traces [Status: Not Started]
    • Extract command patterns from message content
    • Build command frequency analysis
    • Identify successful vs failed command patterns
    • Track command sequence analysis
    • Detect command correction attempts

🟠 P1: Error Pattern Detection

  • [P1] Implement smart error categorization [Status: Not Started]
    • Parse error messages from traces
    • Categorize errors by type (auth, missing deps, syntax, etc)
    • Map errors to recommendations
    • Add per-tool error patterns
    • Create error frequency reports

🟡 P2: Learning Curve Metrics

  • [P2] Measure AI learning efficiency [Status: Not Started]
    • Track turns to first success
    • Measure help usage patterns
    • Identify exploration vs execution phases
    • Calculate efficiency scores
    • Compare learning curves across tools

🟡 P2: Tool Usage Analytics

  • [P2] Deep analysis of tool interactions [Status: Not Started]
    • Track which CLI subcommands are used
    • Measure flag/option usage patterns
    • Identify common parameter combinations
    • Detect workflow patterns

Phase 3: Advanced Reporting

🟠 P1: Complete Report Command

  • [P1] Implement report generation functionality [Status: Not Started]
    • Load results from storage
    • Generate markdown reports
    • Generate HTML reports with charts
    • Generate JSON reports for programmatic use
    • Add report templates system

🟠 P1: CLI Usability Scoring

  • [P1] Create scoring algorithm for CLI friendliness [Status: Not Started]
    • Define scoring criteria (help quality, error clarity, etc)
    • Implement scoring calculations
    • Add comparative scoring across tools
    • Generate improvement recommendations
    • Create scoring rubric documentation

🟡 P2: Trend Analysis Reports

  • [P2] Show performance over time [Status: Not Started]
    • Success rate trends
    • Cost trends
    • Duration trends
    • Compare versions/releases
    • Add anomaly detection

🟡 P2: Interactive Dashboard

  • [P2] Create web-based results viewer [Status: Not Started]
    • Simple Flask/FastAPI server
    • Real-time result updates
    • Interactive charts and filters
    • Export capabilities from UI

Additional Features

🟢 P3: Scenario Management

  • [P3] Enhanced scenario system [Status: Not Started]
    • Scenario metadata (difficulty, category, etc)
    • Scenario dependencies
    • Conditional scenarios
    • Scenario validation

🟢 P3: Plugin System

  • [P3] Extensibility for custom analyzers [Status: Not Started]
    • Plugin architecture design
    • Custom analyzer API
    • Plugin discovery mechanism
    • Documentation for plugin development

Implementation Timeline

Week 1 (Current)

  • Set up development environment
  • Create storage.py foundation
  • Implement basic SQLite storage
  • Update runner to save results

Week 2

  • Complete result persistence
  • Start command pattern analysis
  • Implement error categorization

Week 3

  • Complete analysis engine
  • Implement report generation
  • Add CLI usability scoring

Week 4+

  • Polish and optimize
  • Add trend analysis
  • Documentation updates
  • Performance improvements

Technical Decisions

Database Choice: SQLite

  • Embedded, no server needed
  • Perfect for local development
  • Easy to backup/share
  • Can migrate to PostgreSQL later if needed

Report Formats Priority

  1. Markdown (easiest, git-friendly)
  2. JSON (programmatic use)
  3. HTML (visual reports)
  4. CSV (data analysis)

Analysis Focus

  • Start with simple pattern matching
  • Add ML-based analysis later
  • Keep analysis pluggable

Notes

  • Each feature should include unit tests
  • Update README.md as features are added
  • Maintain backwards compatibility
  • Consider performance for large result sets
  • Keep storage format versioned for migrations