[plan] Formalize proven patterns from high-scoring research scenarios

[github-actions]

Objective

Extract and formalize proven patterns from high-scoring research scenarios into agent knowledge base, ensuring future workflows benefit from successful patterns.

Context

Research identified 5 high-impact patterns that consistently scored 5.0/5.0. These should be explicitly documented and emphasized in agent instructions.

High-Value Patterns to Formalize

1. repo-memory for baselines (Used in 4/11 scenarios)

   • Pattern: Store baseline metrics, compare new runs, detect regressions
   • Example: API performance monitoring (BE-2) - 30-day historical tracking
   • When to use: Performance tracking, visual regression, flaky test analysis

2. noop for healthy states (Prevents issue spam)

   • Pattern: Only create issues when problems detected, use noop otherwise
   • Example: Deployment monitoring (DO-1) - Only alert on failures
   • When to use: Monitoring workflows that run frequently

3. Rate limiting for automated PRs (Team-friendly automation)

   • Pattern: Limit automated PR creation to N per day with CVSS prioritization
   • Example: Security scanner (DO-2) - Max 3 PRs/day
   • When to use: Any workflow that creates PRs automatically

4. Multi-phase analysis pipelines (Complex statistical analysis)

   • Pattern: Data collection → Analysis → Historical tracking → Reporting
   • Example: Flaky test analyzer (QA-2) - 6-phase pipeline with scoring algorithm
   • When to use: Workflows requiring sophisticated analysis

5. Smart duplicate prevention (30-day cache window)

   • Pattern: Hash issues/incidents, check cache before creating
   • Example: Deployment monitoring (DO-1), Security scanner (DO-2)
   • When to use: Monitoring that detects recurring problems

Approach

1. Create patterns reference section in agent instructions
2. Document each pattern with:
   • Description and benefits
   • Code example (10-15 lines)
   • When to use / when not to use
   • Link to source scenario from research
3. Add decision tree for pattern selection
4. Update agent to proactively suggest relevant patterns based on scenario type

Files to Modify

• Update: .github/instructions/developer.instructions.md (add patterns reference section)
• Create: .github/instructions/patterns/proven-patterns.md (detailed pattern catalog)

Acceptance Criteria

• All 5 patterns documented with working code examples
• Each pattern includes "When to use" and "When NOT to use" guidance
• Decision tree helps agent select appropriate patterns
• Examples are copy-paste-ready (under 20 lines each)
• Agent instructions reference patterns section
• Tested by generating 3 workflows that leverage these patterns

Expected Impact

Ensures future workflows benefit from proven high-scoring patterns, improving average quality score and reducing reinvention of successful approaches.
Related to #10285

AI generated by Plan Command for discussion #10248

[github-actions]

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