Purpose: Fast lookup for the most commonly referenced Tier A/B sources
For complete source database: See SOURCES.md (1,278 lines, comprehensive)
Role: Engineering Manager at Anthropic, Claude Code creator Source: Paddo.dev Interview (Jan 2026) Key Patterns: Parallel sessions (5 terminal + 5-10 web), plan mode first, natural language git, verification = 2-3x quality Referenced in: 8+ patterns
Source: https://code.claude.com/docs/en/best-practices Key Guidance: CLAUDE.md ~60 lines, "Would removing this cause mistakes? If not, cut it.", avoid long slash command lists Referenced in: FOUNDATIONAL-PRINCIPLES, 12+ patterns
Source: Anthropic Engineering Blog (Nov 2025) Key Insights: External artifacts as memory, one feature at a time, structured task lists Referenced in: long-running-agent.md, documentation-maintenance.md
Source: Anthropic Engineering Blog (Sep 2025) Key Insights: Context engineering > prompt engineering, context rot, 54% benchmark gains from scratchpad Referenced in: context-engineering.md, FOUNDATIONAL-PRINCIPLES
Source: Anthropic Dev Blog (Nov 24, 2025) Key Features: Tool search (85% token reduction), programmatic calling (37% token reduction), input examples Referenced in: advanced-tool-use.md
Source: Demystifying Evals for AI Agents Key Insights: Task-based evals, LLM-as-judge, infrastructure noise quantification Referenced in: agent-evaluation.md
Source: Anthropic Engineering Blog Key Insights: OS-level sandboxing, permission prompts vs isolation trade-offs Referenced in: safety-and-sandboxing.md
Source: https://owasp.org/www-project-mcp-top-10/ Key Insights: 10 critical MCP security risks, mitigation strategies Referenced in: mcp-patterns.md, safety-and-sandboxing.md
Source: Production AI Engineering Best Practices Key Principles: 6 principles for production reliability, lifecycle-aware context model Referenced in: agent-principles.md, memory-architecture.md
Source: YouTube: How I Actually Use Claude Code (Dec 2025) Key Insights: "Great planning is great prompting", The Big Three framework, principles over tools Referenced in: FOUNDATIONAL-PRINCIPLES, spec-driven-development.md
Source: https://github.com/github/spec-kit Key Methodology: Spec-driven development workflow (Specify → Plan → Tasks → Implement) Referenced in: spec-driven-development.md
Source: Test Time Diversity for Reliability (Google DeepMind) Key Algorithm: Multi-candidate generation, judge loop, crossover for self-improvement Referenced in: recursive-evolution.md
Source: https://github.com/glittercowboy/get-shit-done Key Pattern: Fresh context per subagent, state externalization, orchestrator never does heavy lifting Referenced in: gsd-orchestration.md, framework-selection-guide.md
Source: https://github.com/skribblez2718/caii (Kristoffer Sketch) Key Pattern: 7 fixed cognitive agents vs domain-specific proliferation, Johari Window for ambiguity Referenced in: cognitive-agent-infrastructure.md, johari-window-ambiguity.md
Source: Tenzir: "We Did MCP Wrong" (Jan 2026) Key Data: Skills 50% cheaper than MCP, production cost comparison Referenced in: mcp-vs-skills-economics.md
Source: LlamaIndex Blog Key Insight: Dynamic navigation vs pre-computed embeddings, context fragmentation solutions Referenced in: agentic-retrieval.md
Source: arXiv:2512.24601 (MIT CSAIL - Zhang, Kraska, Khattab) Key Concept: Programmatic self-examination vs single forward pass, "paradigm of 2026" Referenced in: recursive-context-management.md
Sources: Claude Diary (Lance Martin), Generative Agents paper, Yohei Nakajima (BabyAGI) Key Pattern: Capture corrections from sessions, propose updates to persistent config Referenced in: session-learning.md
Source: Production validation (this repository, 73% token savings measured) Key Pattern: 3-tier architecture (main skill + workflow modules + templates), show less reference more Referenced in: progressive-disclosure.md, 10 example skills
Source: Production validation (this repository, adapted from research methodology) Dual System: A-D for source quality (primary/secondary/tertiary/opinion) + 1-5 for claim strength Referenced in: evidence-tiers.md, writing/research presets
- Spec-Driven Development: GitHub Spec Kit (Tier A)
- Framework Selection: Synthesis of GSD, CAII, Native patterns (Tier B)
- Context Engineering: Anthropic Engineering (Tier A), Nate B. Jones (Tier A)
- Memory Architecture: Nate B. Jones lifecycle model (Tier A)
- Johari Window: CAII methodology (Tier B)
- Project Infrastructure: Anthropic docs + Boris Cherny (Tier A)
- Safety & Sandboxing: Anthropic + OWASP (Tier A)
- Agent Evaluation: Anthropic evals blog series (Tier A)
- Agent Principles: Nate B. Jones 6 principles (Tier A)
- MCP Patterns: OWASP MCP Top 10 + Nate B. Jones (Tier A)
- GSD Orchestration: glittercowboy/get-shit-done (Tier B)
- CAII: skribblez2718/caii (Tier B)
- Recursive Context: MIT CSAIL RLM paper (Tier B)
- MCP vs Skills: Tenzir production data (Tier B)
- Progressive Disclosure: Production validation (Tier B)
- Advanced Tool Use: Anthropic Dev Blog (Tier A)
| I Need... | Top Source | Tier |
|---|---|---|
| Core principles | Anthropic Official Docs | A |
| Planning workflow | GitHub Spec Kit | A |
| Context management | Anthropic Context Engineering Blog | A |
| Security guidance | OWASP MCP Top 10 + Anthropic Security | A |
| Orchestration | GSD (glittercowboy) or CAII (Sketch) | B |
| Cost optimization | Tenzir MCP vs Skills | B |
| Self-improvement | Google TTD-DR | A |
| Production reliability | Nate B. Jones 6 Principles | A |
| Parallel workflows | Boris Cherny Interview | A |
| Evaluation patterns | Anthropic Evals Blog | A |
For detailed citations, methodology, and complete source database: See SOURCES.md
Last Updated: February 2026