Seth Hobson
c7ad381360
feat: implement three-tier model strategy with Opus 4.5 ( #139 )
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* 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
Seth Hobson
65e5cb093a
feat: add Agent Skills and restructure documentation
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- Add 47 Agent Skills across 14 plugins following Anthropic's specification
- Python (5): async patterns, testing, packaging, performance, UV package manager
- JavaScript/TypeScript (4): advanced types, Node.js patterns, testing, modern JS
- Kubernetes (4): manifests, Helm charts, GitOps, security policies
- Cloud Infrastructure (4): Terraform, multi-cloud, hybrid networking, cost optimization
- CI/CD (4): pipeline design, GitHub Actions, GitLab CI, secrets management
- Backend (3): API design, architecture patterns, microservices
- LLM Applications (4): LangChain, prompt engineering, RAG, evaluation
- Blockchain/Web3 (4): DeFi protocols, NFT standards, Solidity security, Web3 testing
- Framework Migration (4): React, Angular, database, dependency upgrades
- Observability (4): Prometheus, Grafana, distributed tracing, SLO
- Payment Processing (4): Stripe, PayPal, PCI compliance, billing
- API Scaffolding (1): FastAPI templates
- ML Operations (1): ML pipeline workflow
- Security (1): SAST configuration
- Restructure documentation into /docs directory
- agent-skills.md: Complete guide to all 47 skills
- agents.md: All 85 agents with model configuration
- plugins.md: Complete catalog of 63 plugins
- usage.md: Commands, workflows, and best practices
- architecture.md: Design principles and patterns
- Update README.md
- Add Agent Skills banner announcement
- Reduce length by ~75% with links to detailed docs
- Add What's New section showcasing Agent Skills
- Add Popular Use Cases with real examples
- Improve navigation with Core Guides and Quick Links
- Update marketplace.json with skills arrays for 14 plugins
All 47 skills follow Agent Skills Specification:
- Required YAML frontmatter (name, description)
- Use when activation clauses
- Progressive disclosure architecture
- Under 1024 character descriptions
2025-10-16 20:33:27 -04:00
