feat(gfql): GFQL Programs Spec - PRD Development by lmeyerov · Pull Request #695 · graphistry/pygraphistry

lmeyerov · 2025-07-10T23:00:35Z

Summary

Developing comprehensive product specification for GFQL Programs feature
4-phase iterative PRD process with technical analysis, user research, and validation
Tracking work for extending GFQL from single chains to DAG composition

Context

This PR tracks the development of a comprehensive product specification for the GFQL Programs feature, which extends PyGraphistry's GFQL from single chain queries to DAG-based composition with support for:

Multiple named graph bindings
Remote graph loading
Graph combinators (union, intersection, etc.)
Call operations for PyGraphistry methods

Development Phases

Phase 1: Technical Analysis ✏️

Deep dive into current GFQL implementation
Create knowledge base with code references
Analyze each proposed feature
Initial spec refinement (sketch1X.md)

Phase 2: User Research 📝

Define user personas (data scientists, security analysts, etc.)
Create scenarios for each persona
Dynamic generation based on Phase 1 findings

Phase 3: Role-Play Validation 🎭

100+ LOC role play files with 3-20 turns each
Wire protocol and Python API usage examples
Prioritized improvement analysis (P0-P5)

Phase 4: Final Synthesis 🎯

Review all role plays and feedback
Create final specification (sketch3X.md)
Ready for implementation

Files

AI_PROGRESS/gfql-programs-spec/PLAN.md - Development plan following strict protocol
AI_PROGRESS/gfql-programs-spec/sketch.md - Initial RFC with proposed features

Test Plan

Complete Phase 1 technical analysis
Generate and validate user personas/scenarios
Execute all role play scenarios
Final spec review and approval

🤖 Generated with Claude Code

- Create comprehensive 4-phase PRD development plan - Phase 1: Technical analysis and initial spec refinement - Phase 2: User persona and scenario development - Phase 3: Role-play validation (100+ LOC files, 3-20 turns) - Phase 4: Final synthesis into sketch3X.md - Dynamic step generation based on previous findings - Includes sketch.md RFC for GFQL DAG composition features 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>

…tion - Created comprehensive GFQL knowledge base with code references - Analyzed PyGraphistry API integration points and wire protocol - Enumerated 5 major features from sketch.md - Generated detailed feature analysis steps (1.3.1-1.3.5) Progress on PR #695 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>

- Analyzed all 5 features: DAG composition, dotted refs, remote graphs, combinators, call ops - Created individual feature analysis documents with implementation details - Completed combined critical review identifying cross-cutting concerns - Established 4-phase implementation roadmap based on risk/value assessment Next: Create sketch1X.md synthesizing all findings Progress on PR #695 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>

- Created comprehensive sketch1X.md incorporating all analysis insights - Added security model, error handling, and implementation roadmap - Compared with original sketch.md - no features lost, many enhancements - Production-ready specification with examples and migration guides Phase 1 complete. Next: Phase 2 user personas and scenarios. Progress on PR #695 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>

- Created 6 diverse user personas with different skill levels - Defined 22 scenarios covering all major GFQL features - Completed gap analysis showing 85% coverage - Ready for Phase 3 role plays Progress on PR #695 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>