SKILL.md: Incident Command Decision Intelligence

Decision Points

Task Behavioral Level Routing

Novelty Assessment:
├─ High Pattern Match Confidence + Well-Precedented
│  └─ Route to SB-Level: Compiled response, no deliberation
├─ Medium Match + Parametric Variation Needed
│  └─ Route to RB-Level: Rule lookup + situational adjustment
└─ Low Match OR High Stakes + Novel Elements
   └─ Route to KB-Level: Analytical reasoning + uncertainty tracking

Time Pressure vs Accuracy Trade-off:
├─ High Time Pressure + Moderate Stakes
│  └─ RPD Mode: Pattern match → simulate first option → act
├─ Moderate Time + High Stakes
│  └─ Modified RPD: Pattern match → simulate 2-3 options
└─ Low Time Pressure + Critical Decision
   └─ Full Analysis: Generate options → compare → decide

Information Uncertainty Triage

Information Gap Assessment:
├─ Missing Info Would Change Action
│  ├─ Acquisition Cost < Decision Delay Cost → Acquire first
│  └─ Acquisition Cost > Decision Delay Cost → Act on current estimate
├─ Missing Info Would Only Increase Confidence
│  └─ Skip acquisition → Act immediately
└─ Uncertainty is Aleatory (Random)
   └─ Build robust response → Don't wait for clarity

Multi-Agent Coordination Strategy

Agent Count & Complexity:
├─ Single Agent Task
│  └─ Individual optimization sufficient
├─ 2-5 Agent Coordination
│  └─ Explicit handoff protocols + shared state
├─ 5+ Agents OR Cross-Domain
│  ├─ Centralized: Single coordinator + hierarchical reporting
│  └─ Distributed: Mesh communication + conflict resolution
└─ Mission-Critical Multi-Agent
   └─ Redundant coordination + failure detection + graceful degradation

Escalation Trigger Points

IF: Pattern match confidence < 60% AND stakes = high
   → Escalate to KB-level analysis

IF: Multiple simulation failures on first option
   → Generate second option (don't enumerate all)

IF: Information gaps block action AND delay cost < error cost
   → Pause for targeted information acquisition

IF: Inter-agent coordination failure detected
   → Switch to centralized coordination mode

IF: Time pressure + no clear pattern match
   → Apply "least regret" heuristic + act

Failure Modes

1. Behavioral Level Mismatch

• Symptoms: KB-level deliberation on routine tasks (over-engineering) OR SB automation on novel situations
• Detection: Processing time >> expected for task type OR critical errors on "simple" tasks
• Fix: Recalibrate novelty assessment → route to appropriate behavioral level

2. Analysis Paralysis Under Pressure

• Symptoms: Option enumeration when pattern matching would suffice, infinite information gathering
• Detection: Time spent on analysis > time available for action implementation
• Fix: Force RPD mode → pattern match → simulate first viable option → act

3. Epistemic Cowardice

• Symptoms: Refusing to act until certainty achieved, treating all uncertainty as requiring resolution
• Detection: Action delayed while seeking information that won't change the decision
• Fix: Classify uncertainty as epistemic vs aleatory → act on best current estimate

4. Coordination Centralization Cascade

• Symptoms: All decisions routing through single bottleneck agent under load
• Detection: Single agent queue depth growing while other agents idle
• Fix: Distribute authority → push decisions to edge agents → central coordination only for conflicts

5. Retrospective Training Contamination

• Symptoms: Agent behavior optimized for post-hoc rationalization rather than real-time effectiveness
• Detection: Perfect textbook responses that fail in messy real conditions
• Fix: Weight observational training data over self-reported case studies

Worked Examples

Example 1: Emergency System Triage

Scenario: Multi-agent system monitoring industrial facility. Sensor agent detects anomalous temperature spike in reactor core. Need to route to appropriate response level.

Novice Approach: Generate full option tree, analyze each branch, compute expected values.

Expert Decision Process:

1. Situation Assessment: "Temperature spike + reactor = potential emergency pattern"
2. Behavioral Level: High stakes + some precedent = RB-level (rule-based)
3. Pattern Match: Apply "reactor anomaly response protocol"
4. First Option: Immediate cooling system activation + operator alert
5. Mental Simulation: "Does this fail in next 3 steps?" → Cooling adequate for this temperature level
6. Action: Execute cooling protocol, monitor for 2 minutes
7. Checkpoint: If temperature drops → continue monitoring; if rises → escalate to KB analysis

Key Decision Points Navigated:

• Recognized emergency pattern (avoiding over-analysis delay)
• Chose RB over KB (precedented situation, established protocol exists)
• Used RPD simulation rather than option comparison (time pressure)
• Built in explicit checkpoint (uncertainty management)

What Novice Misses: Time cost of full analysis, adequacy of rule-based response, value of rapid initial action with planned escalation.

Example 2: Inter-Agent Coordination Breakdown

Scenario: Customer service system with specialized agents (billing, technical, account management). Customer issue spans multiple domains. Initial routing to billing agent, but issue requires technical knowledge.

Failure Pattern: Billing agent attempts to handle technical aspects (wrong behavioral level), customer transferred multiple times, no agent has complete context.

Expert Coordination Process:

1. Handoff Trigger: Billing agent recognizes technical component beyond expertise
2. Context Transfer: Full state package (not just customer ID) to technical agent
3. Coordination Mode: Technical agent takes lead, billing agent remains in loop
4. Shared State: Both agents update common context store
5. Quality Gate: Before customer contact, verify both agents agree on solution approach

Decision Points:

• When to escalate vs attempt domain stretch (expertise boundary detection)
• How to transfer context without information loss (handoff protocol)
• Whether to maintain multi-agent involvement vs single owner (coordination cost/benefit)

Failure Prevention: Clear expertise boundaries, mandatory context transfer protocols, shared state visibility.

Example 3: Novel Situation Under Time Pressure

Scenario: AI safety monitoring system encounters new failure mode not in training data. System showing anomalous behavior, potential risk to users, 5-minute window before automatic failsafe triggers.

Expert Response Pattern:

1. Novelty Recognition: "No clear pattern match, high stakes = KB-level required"
2. Uncertainty Classification: Epistemic (missing understanding) not aleatory (random)
3. Information Triage: What data would most change response? → Focus on user impact indicators
4. Bounded Analysis: 2-minute analysis window, then act on best estimate
5. Conservative Bias: When uncertain + high stakes → err toward safety
6. Action: Trigger controlled shutdown, alert human operators
7. Learning Loop: Log decision process for future pattern development

Critical Trade-offs:

• Analysis time vs action time (bounded deliberation)
• False alarm cost vs miss cost (conservative bias justification)
• Immediate response vs information gathering (epistemic uncertainty handling)

Quality Gates

Task completion checklist - verify each before considering decision process complete:

• Behavioral level correctly matched to task novelty and stakes
• If time-pressured, RPD process used (pattern → simulate → act) rather than option enumeration
• Uncertainty classified as epistemic vs aleatory with appropriate response strategy
• Information gaps identified and triaged by action-relevance not confidence-improvement
• Multi-agent handoffs include complete context transfer and explicit coordination protocol
• Decision process includes explicit checkpoints for estimate updates
• Failure modes pre-identified with detection triggers and remediation plans
• Individual agent capability verified as necessary but not sufficient for system success
• Training/learning signals weighted by data source quality (observational > self-reported)
• Conservative bias applied appropriately when uncertainty + high stakes combine

NOT-FOR Boundaries

Do NOT use this skill for:

• Well-specified single-agent problems with complete information
• Routine CRUD operations or standard API integrations
• Code generation or syntax-level programming tasks
• Mathematical optimization with known objective functions
• Creative content generation or brainstorming sessions

Delegate instead to:

• Standard workflows: Use task-decomposition for routine multi-step processes
• Technical implementation: Use system-architecture for non-crisis system design
• Creative synthesis: Use creative-problem-solving for open-ended ideation
• Mathematical analysis: Use quantitative-reasoning for optimization problems
• Individual skill gaps: Use capability-development for single-agent improvement

Use this skill specifically when:

• Time pressure + uncertainty + coordination complexity combine
• Multiple expert agents must work together under constraints
• System failure modes could cascade or compound
• Decision quality degrades under operational stress
• Learning from crisis case studies or building crisis-resilient systems