name	consider
description	Selects and applies mental models for structured problem analysis. Triggers when user asks "why", "what if", "how should we", needs systematic problem-solving, or mentions analyzing a situation. MUST BE USED when comparing options, making decisions, or evaluating trade-offs.

Analyze problems, decisions, and questions using proven mental frameworks. This skill: 1. Recalls relevant past analyses from memory (if MCP memory available) 2. Classifies problems by type and characteristics 3. Assesses information requirements (local, web, user) 4. Gathers context efficiently using subagent coordination 5. Selects and executes optimal mental model(s) 6. Synthesizes actionable insights 7. Stores analysis to memory for future recall ``` /consider [problem statement] ```

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

| Type | Signals | Description | |------|---------|-------------| | DIAGNOSIS | "why", "cause", "root" | Understanding why something happened | | DECISION | "should I", "decide", "choose" | Choosing between options | | PRIORITIZATION | "overwhelmed", "too many", "first" | Determining what matters most | | INNOVATION | "stuck", "nothing works", "assume" | Breaking through barriers | | RISK | "fail", "risk", "wrong" | Assessing potential failures | | FOCUS | "focus", "leverage", "important" | Finding highest-impact actions | | OPTIMIZATION | "simplify", "remove", "reduce" | Improving by subtraction | | STRATEGY | "strategy", "position", "compete" | Assessing competitive position | | DELIBERATION | "perspectives", "group", "meeting", "angles" | Exploring from multiple viewpoints | | SYSTEMIC | "symptoms", "causes", "constraint", "bottleneck" | Complex system diagnosis (TOC) | **Temporal Focus**: PAST | PRESENT | FUTURE **Complexity**: SIMPLE | COMPLICATED | COMPLEX **Emotional Loading**: HIGH | LOW **Information State**: OVERLOAD | SPARSE | CONFLICTING | Problem Type | Focus Area | Primary Model | Supporting Model | |--------------|------------|---------------|------------------| | DIAGNOSIS | Root cause | 5-Whys | First Principles | | DIAGNOSIS | Assumptions | First Principles | Occam's Razor | | DIAGNOSIS | Simplest explanation | Occam's Razor | 5-Whys | | DECISION | Time horizons | 10-10-10 | Second-Order | | DECISION | Tradeoffs | Opportunity Cost | 10-10-10 | | DECISION | Failure prevention | Inversion | Second-Order | | PRIORITIZATION | Urgency/importance | Eisenhower Matrix | Pareto | | PRIORITIZATION | Impact ranking | Pareto | One Thing | | PRIORITIZATION | Single leverage | One Thing | Pareto | | INNOVATION | Challenge assumptions | First Principles | Inversion | | INNOVATION | Flip perspective | Inversion | First Principles | | INNOVATION | Subtract complexity | Via Negativa | One Thing | | RISK | Failure modes | Inversion | Second-Order | | RISK | Consequence chains | Second-Order | Inversion | | FOCUS | Highest leverage | One Thing | Pareto | | FOCUS | Vital few | Pareto | One Thing | | FOCUS | What to eliminate | Via Negativa | Pareto | | OPTIMIZATION | Remove bloat | Via Negativa | Pareto | | OPTIMIZATION | Efficiency | Pareto | Via Negativa | | STRATEGY | Position | SWOT | Second-Order | | STRATEGY | Competition | SWOT | Inversion | | STRATEGY | Long-term | Second-Order | SWOT | | DELIBERATION | Perspectives | Six Hats | SWOT | | DELIBERATION | Emotions vs logic | Six Hats | 10-10-10 | | SYSTEMIC | Constraint | TOC | 5-Whys | | SYSTEMIC | Conflict resolution | TOC | Six Hats |

Model	Best For	Core Question
5-Whys	Root cause analysis	"Why did this happen?" (iterate 5x)
10-10-10	Decisions with emotional bias	"How will I feel in 10 min/months/years?"
Eisenhower	Task prioritization	"Is this urgent AND important?"
First Principles	Challenging assumptions	"What is fundamentally true?"
Inversion	Risk prevention	"What would guarantee failure?"
Occam's Razor	Competing explanations	"Which requires fewest assumptions?"
One Thing	Finding leverage	"What makes everything else easier?"
Opportunity Cost	Tradeoff analysis	"What am I giving up?"
Pareto	Impact prioritization	"Which 20% drives 80% of results?"
Second-Order	Consequence analysis	"And then what happens?"
SWOT	Strategic position	"Strengths/Weaknesses/Opportunities/Threats?"
Via Negativa	Simplification	"What should I remove?"
Six Hats	Parallel perspectives	"What are all the angles?"
TOC	Systemic root cause + conflict resolution	"What constraint is blocking the system?"

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

| Model | Local Sources | Web Research | User Clarification | |-------|--------------|--------------|-------------------| | 5-Whys | Logs, history, docs | Rarely needed | Root symptoms, timeline | | 10-10-10 | Past decisions | Rarely needed | Values, priorities | | Eisenhower | Task lists, deadlines | Rarely needed | Urgency criteria | | First Principles | Technical docs | Industry fundamentals | Core assumptions | | Inversion | Failure history | Industry failure cases | Success definition | | Occam's Razor | Available evidence | Rarely needed | Competing hypotheses | | One Thing | Goals, metrics | Rarely needed | Primary objective | | Opportunity Cost | Project docs, budgets | Market rates, benchmarks | Budget constraints | | Pareto | Metrics, analytics | Industry benchmarks | Success metrics | | Second-Order | Codebase, history | Industry trends, precedents | Time horizon | | SWOT | Internal docs, capabilities | Market/competitor data | Strategic goals | | Via Negativa | Current state docs | Best practices | What to preserve | **Before executing any model, classify each information need:**

Need Type	Source	Tool/Method
Historical context	Local	Read (logs, docs, git history)
Codebase patterns	Local	Task(Explore) with constraints
Current metrics	Local	Read analytics, logs
Market data	Web	Task + WebSearch
Competitor info	Web	Task + WebSearch
Industry benchmarks	Web	Task + WebSearch
User preferences	User	AskUserQuestion
Success criteria	User	AskUserQuestion
Constraints/limits	User	AskUserQuestion
Technical specs	Local/User	Read docs OR AskUserQuestion

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

**For codebase/local file analysis:**

@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

**For market/competitor/industry research:**

@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

**When multiple independent information needs exist:**

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.

Use when output of one model feeds the next:

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)

Use multiple lenses simultaneously for validation:

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:

**Recall Past Context**

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

**Analyze Problem** - Read problem statement - Detect signal words (see problem_types table) - Classify: type, temporal focus, complexity, emotional loading, information state **Confirm Classification** - Use AskUserQuestion to verify: - Problem type classification - Focus area within type - Any constraints or preferences - Refine classification based on response **Assess Information Needs** For selected model(s), determine:

Available locally?
- Conversation history
- Codebase/project files
- User-provided documents
Requires web research?
- Market/competitor data
- Industry benchmarks
- Current trends
Must ask user?
- Personal values/priorities
- Constraints not documented
- Success criteria

**Gather Information**

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

**Execute Model(s)**

With gathered context:

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

**Synthesize Insights**

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.

consider

Install Skill

SKILL.md

Classification Output Format