Consider

The command will analyze, gather required information, then apply the right model(s).

Full model templates: See references/ directory for complete execution frameworks.

When information gathering is needed, use Task tool with structured prompts for token efficiency.

@type: AnalyzeAction
about: "[specific question about codebase/docs]"

@return Answer:
- text: string (direct answer, max 200 chars)
- evidence: string[] (file:line references, max 5)
- confidence: string (high|medium|low)

@constraints:
  maxTokens: 2000
  format: JSON object

Return ONLY the specified structure. No preamble or explanations.

Use subagent_type: Explore with thoroughness based on scope:

• Single file/function: quick
• Module/feature: medium
• Cross-cutting concern: thorough

@type: AnalyzeAction
query: "[specific research query]"

@return ItemList (max 5 items):
- position: integer
- name: string (source name)
- url: string (if available)
- summary: string (max 150 chars, key finding)
- relevance: string (high|medium|low)

@constraints:
  maxTokens: 3000
  format: markdown table

Return ONLY the specified structure. No commentary.

Use WebSearch or WebFetch for:

• Current market conditions
• Competitor analysis
• Industry benchmarks
• Recent trends or news

Invoke multiple Task calls in a single message:

• Codebase analysis (Task/Explore)
• Web research (Task with WebSearch)
• These run in parallel, reducing latency

Example parallel invocation:

Task 1: Explore codebase for error handling patterns
Task 2: WebSearch for "industry error handling best practices 2024"

Both return focused, structured responses within token budgets.

Diagnostic Chain: 5-Whys → First Principles → Inversion (find root → verify assumptions → prevent recurrence)

Decision Chain: Opportunity Cost → Second-Order → 10-10-10 (what you give up → consequences → time horizons)

Priority Chain: Pareto → One Thing → Via Negativa (vital few → single leverage → remove rest)

Strategic Chain: SWOT → Inversion → Second-Order (position → failure modes → consequences)

High-stakes decision: 10-10-10 + Inversion + Second-Order Strategic pivot: SWOT + First Principles + Opportunity Cost Simplification: Via Negativa + Pareto + One Thing

At analysis start, if MCP memory tools are available:

Use mcp__memory__search_nodes to find relevant prior analyses:

search_nodes("{key problem terms}")

Look for:

• Similar Problem entities (entityType: "Problem")
• Related RootCause entities (entityType: "RootCause")
• Applicable Insight entities (entityType: "Insight")

If matches found, use mcp__memory__open_nodes to get details:

open_nodes(["problem-similar-issue", "insight-relevant-finding"])

Present to user:

## Prior Context (from memory)

**Similar problems analyzed:**
- [problem name]: [key observations]

**Relevant insights:**
- [insight]: [content, outcome]

**Recurring root causes in this area:**
- [root cause]: [occurrence count]

Use prior context to:

• Suggest models that worked well before
• Highlight root causes that recur
• Avoid repeating failed approaches
• Build on validated insights

Skip memory recall if:

• MCP memory tools not available
• User requests fresh analysis
• No relevant matches found

1. Available locally?

   • Conversation history
   • Codebase/project files
   • User-provided documents

2. Requires web research?

   • Market/competitor data
   • Industry benchmarks
   • Current trends

3. Must ask user?

   • Personal values/priorities
   • Constraints not documented
   • Success criteria

Execute information gathering based on assessment:

• Local: Use Read or Task(Explore) with token constraints
• Web: Use Task with WebSearch, structured return format
• User: Use AskUserQuestion with specific, focused questions

Parallel execution: If needs are independent, invoke multiple Task calls in single message.

Token budget guidance:

• Simple lookup: 1000-2000 tokens
• Moderate analysis: 2000-3000 tokens
• Complex research: 3000-5000 tokens

With gathered context:

1. Load full model template from references/[model-name].md
2. Apply model systematically using template structure
3. For serial chains: complete each model before starting next
4. For parallel triangulation: apply all models, then compare

Deliver:

• Key Insight: Single most important finding (1-2 sentences)
• Recommended Action: Specific next step
• Confidence Level: High/Medium/Low with reasoning
• Information Gaps: What couldn't be determined (if any)

Before proceeding to execution, verify:

• Problem type confirmed with user
• Model selection appropriate for type + focus
• Information needs classified (local/web/user)
• Required information gathered with structured responses
• Token budgets respected in subagent calls
• No open-ended research (all queries focused)

Red flags requiring user clarification:

• Problem fits multiple types equally
• Critical information unavailable
• High emotional loading detected
• Conflicting constraints identified

Analysis is successful when:

• Problem correctly classified and confirmed
• Required information gathered efficiently (minimal tokens)
• Model(s) applied with full rigor using templates
• Insight is specific and actionable
• Confidence level justified
• User can take immediate action on recommendation

Classification Output Format

For the problem classification section (step 1), use TOON structured format:

@type: AnalyzeAction
name: problem-classification
object: [problem statement text]
actionStatus: CompletedActionStatus

classification:
primaryType: [DIAGNOSIS|DECISION|PRIORITIZATION|INNOVATION|RISK|FOCUS|OPTIMIZATION|STRATEGY]
temporalFocus: [PAST|PRESENT|FUTURE]
complexity: [SIMPLE|COMPLICATED|COMPLEX]
emotionalLoading: [HIGH|LOW]

signals[N]: [key,signal,words]

Note: Keep all reasoning, framework selection, model execution, and synthesis as markdown prose. Only use TOON for the structured classification output at the beginning of the analysis.

Model execution templates (read when applying specific model):

• references/5-whys.md - Root cause drilling
• references/10-10-10.md - Time horizon analysis
• references/eisenhower.md - Urgency/importance matrix
• references/first-principles.md - Assumption challenging
• references/inversion.md - Failure mode analysis
• references/occams-razor.md - Simplest explanation
• references/one-thing.md - Leverage identification
• references/opportunity-cost.md - Tradeoff analysis
• references/pareto.md - 80/20 analysis
• references/second-order.md - Consequence chains
• references/swot.md - Strategic position
• references/via-negativa.md - Improvement by subtraction
• references/six-hats.md - Parallel perspective exploration
• references/toc.md - Theory of Constraints logical thinking

For memory schema details, see mcp/memory-schema.md.