Research-Driven Planning (Loop 1)
Purpose
Comprehensive planning with research-backed solutions and iterative risk mitigation that prevents 85-95% of problems before coding begins.
Specialist Agent Coordination
I coordinate multi-agent research and planning swarms using explicit agent SOPs from Claude-Flow's 86-agent ecosystem.
Methodology (SOP: Specification → Research → Planning → Execution → Knowledge):
- Specification Phase: Requirements capture with structured SPEC.md
- Research Phase: 6-agent parallel research with self-consistency validation
- Planning Phase: MECE task decomposition with research integration
- Execution Phase: 8-agent Byzantine consensus pre-mortem (5 iterations)
- Knowledge Phase: Planning package generation for Loop 2 integration
Integration: Loop 1 of 3. Feeds → parallel-swarm-implementation (Loop 2), Receives ← cicd-intelligent-recovery (Loop 3) failure patterns.
When to Use This Skill
Activate this skill when:
- Starting a new feature or project requiring comprehensive planning
- Need to prevent problems before coding begins (85-95% failure prevention)
- Want research-backed solutions instead of assumptions (30-60% time savings)
- Require risk analysis with <3% failure confidence
- Building something complex with multiple failure modes
- Need evidence-based planning that feeds into implementation
DO NOT use this skill for:
- Quick fixes or trivial changes (use direct implementation)
- Well-understood repetitive tasks (use existing patterns)
- Emergency hotfixes (skip to Loop 2)
Input Contract
input:
project_description: string (required)
# High-level description of what needs to be built
requirements:
functional: array[string] (required)
# Core features and capabilities
non_functional: object (optional)
performance: string
security: string
scalability: string
constraints:
technical: array[string] (stack, framework, dependencies)
timeline: string (deadlines, milestones)
resources: object (team, budget, infrastructure)
options:
research_depth: enum[quick, standard, comprehensive] (default: standard)
premortem_iterations: number (default: 5, range: 3-10)
failure_threshold: number (default: 3, target: <3%)
Output Contract
output:
specification:
spec_file: path # SPEC.md location
requirements_complete: boolean
success_criteria: array[string]
research:
evidence_sources: number # Total research sources
recommendations: array[object]
solution: string
confidence: number (0-100)
evidence: array[url]
risk_landscape: array[object]
risk: string
severity: enum[low, medium, high, critical]
mitigation: string
planning:
enhanced_plan: path # plan-enhanced.json location
total_tasks: number
task_dependencies: object
estimated_complexity: string
risk_analysis:
premortem_iterations: number
final_failure_confidence: number # Target: <3%
critical_risks_mitigated: number
defense_strategies: array[string]
integration:
planning_package: path # loop1-planning-package.json
memory_namespace: string # integration/loop1-to-loop2
ready_for_loop2: boolean
SOP Phase 1: Specification
Objective: Define initial requirements with clarity and structure.
Create SPEC.md
Generate a comprehensive specification document in the project root:
# Project Specification
## Overview
[High-level description of what needs to be built]
## Requirements
### Functional Requirements
1. [Core feature 1]
2. [Core feature 2]
...
### Non-Functional Requirements
- Performance: [metrics]
- Security: [requirements]
- Scalability: [targets]
- Compliance: [standards]
## Constraints
- Technical: [language, framework, dependencies]
- Timeline: [deadlines, milestones]
- Resources: [team size, budget, infrastructure]
## Success Criteria
1. [Measurable outcome 1]
2. [Measurable outcome 2]
...
## Out of Scope
- [Explicitly excluded features]
Store Initial Context
npx claude-flow@alpha memory store \
"project_spec" \
"$(cat SPEC.md)" \
--namespace "loop1/specification"
Output: Structured SPEC.md file and memory-stored specification
SOP Phase 2: Research (Multi-Agent Evidence Collection)
Objective: Comprehensive solution discovery using evidence-based research with self-consistency validation.
Execute 6-Agent Parallel Research SOP
Agent Coordination Pattern (Claude Code Task tool - Single Message):
// RESEARCH PHASE: 6-Agent Parallel Evidence Collection
// Self-Consistency: Multiple research perspectives + cross-validation
[Single Message - All 6 Research Agents]:
// Web Research Agents (3 perspectives for self-consistency)
Task("Web Research Specialist 1",
"Research [primary_technology] best practices 2024. Focus on: security patterns, industry standards, implementation approaches. Provide evidence with source URLs. Store findings in .claude/.artifacts/web-research-1.json. Use hooks: npx claude-flow@alpha hooks pre-task --description 'web research 1' && npx claude-flow@alpha hooks post-task --task-id 'web-research-1'",
"researcher")
Task("Web Research Specialist 2",
"Research [technology] libraries comparison. Focus on: developer experience, community support, production reliability, security track record. Cross-validate findings from Specialist 1. Store in .claude/.artifacts/web-research-2.json. Use hooks for coordination.",
"researcher")
Task("Academic Research Agent",
"Research [domain] security research papers and compliance requirements. Focus on: recent vulnerabilities, mitigation strategies, industry standards, regulatory requirements. Store in .claude/.artifacts/academic-research.json.",
"researcher")
// GitHub Analysis Agents (code quality perspective)
Task("GitHub Quality Analyst",
"Analyze top [technology] libraries on GitHub. Focus on: code quality metrics (test coverage, cyclomatic complexity), issue resolution time, commit frequency, maintainer responsiveness. Generate quality rankings. Store in .claude/.artifacts/github-quality.json.",
"code-analyzer")
Task("GitHub Security Auditor",
"Audit [technology] library security. Focus on: vulnerability history, security advisories, patch response time, dependency security. Flag high-risk libraries. Store in .claude/.artifacts/github-security.json.",
"security-review")
// Synthesis Coordinator (Plan-and-Solve pattern)
Task("Research Synthesis Coordinator",
"Wait for all 5 research agents to complete. Synthesize findings using self-consistency validation: 1) Aggregate all evidence, 2) Cross-validate conflicting recommendations, 3) Calculate confidence scores based on source agreement, 4) Flag any contradictory evidence, 5) Generate ranked recommendations with evidence. Use Byzantine consensus for critical technology decisions (require 3/5 agent agreement). Store final synthesis in .claude/.artifacts/research-synthesis.json. Memory store: npx claude-flow@alpha memory store 'research_findings' \"$(cat .claude/.artifacts/research-synthesis.json)\" --namespace 'loop1/research'",
"analyst")
Evidence-Based Techniques Applied:
- Self-Consistency: 3 web research agents + cross-validation
- Plan-and-Solve: Synthesis coordinator waits, then validates systematically
- Program-of-Thought: Explicit step-by-step synthesis workflow
- Byzantine Consensus: 3/5 agreement required for critical decisions
Research Output
This produces:
- Solution Rankings: Best approaches with evidence and confidence scores
- Pattern Library: Proven implementation patterns from real codebases
- Risk Identification: Known pitfalls from real implementations
- Technology Recommendations: Evidence-based stack selection with justifications
**Validatio