Files
agents/plugins/web-scripting/agents/php-pro.md
Seth Hobson c7ad381360 feat: implement three-tier model strategy with Opus 4.5 (#139)
* feat: implement three-tier model strategy with Opus 4.5

This implements a strategic model selection approach based on agent
complexity and use case, addressing Issue #136.

Three-Tier Strategy:
- Tier 1 (opus): 17 critical agents for architecture, security, code review
- Tier 2 (inherit): 21 complex agents where users choose their model
- Tier 3 (sonnet): 63 routine development agents (unchanged)
- Tier 4 (haiku): 47 fast operational agents (unchanged)

Why Opus 4.5 for Tier 1:
- 80.9% on SWE-bench (industry-leading for code)
- 65% fewer tokens for long-horizon tasks
- Superior reasoning for architectural decisions

Changes:
- Update architect-review, cloud-architect, kubernetes-architect,
  database-architect, security-auditor, code-reviewer to opus
- Update backend-architect, performance-engineer, ai-engineer,
  prompt-engineer, ml-engineer, mlops-engineer, data-scientist,
  blockchain-developer, quant-analyst, risk-manager, sql-pro,
  database-optimizer to inherit
- Update README with three-tier model documentation

Relates to #136

* feat: comprehensive model tier redistribution for Opus 4.5

This commit implements a strategic rebalancing of agent model assignments,
significantly increasing the use of Opus 4.5 for critical coding tasks while
ensuring Sonnet is used more than Haiku for support tasks.

Final Distribution (153 total agent files):
- Tier 1 Opus: 42 agents (27.5%) - All production coding + critical architecture
- Tier 2 Inherit: 42 agents (27.5%) - Complex tasks, user-choosable
- Tier 3 Sonnet: 38 agents (24.8%) - Support tasks needing intelligence
- Tier 4 Haiku: 31 agents (20.3%) - Simple operational tasks

Key Changes:

Tier 1 (Opus) - Production Coding + Critical Review:
- ALL code-reviewers (6 total): Ensures highest quality code review across
  all contexts (comprehensive, git PR, code docs, codebase cleanup, refactoring, TDD)
- All major language pros (7): python, golang, rust, typescript, cpp, java, c
- Framework specialists (6): django (2), fastapi (2), graphql-architect (2)
- Complex specialists (6): terraform-specialist (3), tdd-orchestrator (2), data-engineer
- Blockchain: blockchain-developer (smart contracts are critical)
- Game dev (2): unity-developer, minecraft-bukkit-pro
- Architecture (existing): architect-review, cloud-architect, kubernetes-architect,
  hybrid-cloud-architect, database-architect, security-auditor

Tier 2 (Inherit) - User Flexibility:
- Secondary languages (6): javascript, scala, csharp, ruby, php, elixir
- All frontend/mobile (8): frontend-developer (4), mobile-developer (2),
  flutter-expert, ios-developer
- Specialized (6): observability-engineer (2), temporal-python-pro,
  arm-cortex-expert, context-manager (2), database-optimizer (2)
- AI/ML, backend-architect, performance-engineer, quant/risk (existing)

Tier 3 (Sonnet) - Intelligent Support:
- Documentation (4): docs-architect (2), tutorial-engineer (2)
- Testing (2): test-automator (2)
- Developer experience (3): dx-optimizer (2), business-analyst
- Modernization (4): legacy-modernizer (3), database-admin
- Other support agents (existing)

Tier 4 (Haiku) - Simple Operations:
- SEO/Marketing (10): All SEO agents, content, search
- Deployment (4): deployment-engineer (4 instances)
- Debugging (5): debugger (2), error-detective (3)
- DevOps (3): devops-troubleshooter (3)
- Other simple operational tasks

Rationale:
- Opus 4.5 achieves 80.9% on SWE-bench with 65% fewer tokens on complex tasks
- Production code deserves the best model: all language pros now on Opus
- All code review uses Opus for maximum quality and security
- Sonnet > Haiku (38 vs 31) ensures better intelligence for support tasks
- Inherit tier gives users cost control for frontend, mobile, and specialized tasks

Related: #136, #132

* feat: upgrade final 13 agents from Haiku to Sonnet

Based on research into Haiku 4.5 vs Sonnet 4.5 capabilities, upgraded
agents requiring deep analytical intelligence from Haiku to Sonnet.

Research Findings:
- Haiku 4.5: 73.3% SWE-bench, 3-5x faster, 1/3 cost, sub-200ms responses
- Best for Haiku: Real-time apps, data extraction, templates, high-volume ops
- Best for Sonnet: Complex reasoning, root cause analysis, strategic planning

Agents Upgraded (13 total):
- Debugging (5): debugger (2), error-detective (3) - Complex root cause analysis
- DevOps (3): devops-troubleshooter (3) - System diagnostics & troubleshooting
- Network (2): network-engineer (2) - Complex network analysis & optimization
- API Documentation (2): api-documenter (2) - Deep API understanding required
- Payments (1): payment-integration - Critical financial integration

Final Distribution (153 total):
- Tier 1 Opus: 42 agents (27.5%) - Production coding + critical architecture
- Tier 2 Inherit: 42 agents (27.5%) - Complex tasks, user-choosable
- Tier 3 Sonnet: 51 agents (33.3%) - Support tasks needing intelligence
- Tier 4 Haiku: 18 agents (11.8%) - Fast operational tasks only

Haiku Now Reserved For:
- SEO/Marketing (8): Pattern matching, data extraction, content templates
- Deployment (4): Operational execution tasks
- Simple Docs (3): reference-builder, mermaid-expert, c4-code
- Sales/Support (2): High-volume, template-based interactions
- Search (1): Knowledge retrieval

Sonnet > Haiku as requested (51 vs 18)

Sources:
- https://www.creolestudios.com/claude-haiku-4-5-vs-sonnet-4-5-comparison/
- https://www.anthropic.com/news/claude-haiku-4-5
- https://caylent.com/blog/claude-haiku-4-5-deep-dive-cost-capabilities-and-the-multi-agent-opportunity

Related: #136

* docs: add cost considerations and clarify inherit behavior

Addresses PR feedback:
- Added comprehensive cost comparison for all model tiers
- Documented how 'inherit' model works (uses session default, falls back to Sonnet)
- Explained cost optimization strategies
- Clarified when Opus token efficiency offsets higher rate

This helps users make informed decisions about model selection and cost control.
2025-12-10 15:52:06 -05:00

2.0 KiB

name, description, model
name description model
php-pro Write idiomatic PHP code with generators, iterators, SPL data structures, and modern OOP features. Use PROACTIVELY for high-performance PHP applications. inherit

You are a PHP expert specializing in modern PHP development with focus on performance and idiomatic patterns.

Focus Areas

  • Generators and iterators for memory-efficient data processing
  • SPL data structures (SplQueue, SplStack, SplHeap, ArrayObject)
  • Modern PHP 8+ features (match expressions, enums, attributes, constructor property promotion)
  • Type system mastery (union types, intersection types, never type, mixed type)
  • Advanced OOP patterns (traits, late static binding, magic methods, reflection)
  • Memory management and reference handling
  • Stream contexts and filters for I/O operations
  • Performance profiling and optimization techniques

Approach

  1. Start with built-in PHP functions before writing custom implementations
  2. Use generators for large datasets to minimize memory footprint
  3. Apply strict typing and leverage type inference
  4. Use SPL data structures when they provide clear performance benefits
  5. Profile performance bottlenecks before optimizing
  6. Handle errors with exceptions and proper error levels
  7. Write self-documenting code with meaningful names
  8. Test edge cases and error conditions thoroughly

Output

  • Memory-efficient code using generators and iterators appropriately
  • Type-safe implementations with full type coverage
  • Performance-optimized solutions with measured improvements
  • Clean architecture following SOLID principles
  • Secure code preventing injection and validation vulnerabilities
  • Well-structured namespaces and autoloading setup
  • PSR-compliant code following community standards
  • Comprehensive error handling with custom exceptions
  • Production-ready code with proper logging and monitoring hooks

Prefer PHP standard library and built-in functions over third-party packages. Use external dependencies sparingly and only when necessary. Focus on working code over explanations.