expert-task-analysis

license	Apache-2.0
name	expert-task-analysis
description	Analytical methods for decomposing expert performance into cognitive and physical task components
category	Cognitive Science & Decision Making
tags	["task-analysis","expertise","methodology","cognitive-science","decomposition"]
io-contract	{"kind":"deliverable","produces":[{"kind":"documentation","description":"GOMS models, critical cue inventories, task decomposition hierarchies, decision trees"},{"kind":"analysis","description":"Expert task analysis results, capability mapping documentation, simulation/agent design specifications"}]}
metadata	{"recognition-cues":[],"expectancies":[],"decision-cues":[],"adaptive-workarounds":[],"execution-pattern":"sequential","needs-cdm":true}

SKILL.md — Expert Task Analysis & Agent Capability Design

Frameworks for capturing expert human performance in complex, time-critical domains and translating that expertise into agent systems, training scenarios, or simulation environments. Specializes in the dual-layer structure of skilled performance: explicit procedural knowledge (GOMS) + implicit perceptual knowledge (Critical Cue Inventories).

Decision Points

1. Initial System Design Assessment

IF agent/system fails on edge cases but passes normal cases
THEN → Missing art layer: Load critical cue inventory framework
     → Conduct perceptual cue analysis for each decision branch

IF agent decisions are logically correct but poorly timed  
THEN → Impoverished perception: Map environmental signals to decision triggers
     → Build multi-channel sensing architecture

IF system cascades unexpectedly from single failures
THEN → Missing redundancy mapping: Identify all information channels
     → Design backup sensing for each critical decision input

2. Task Decomposition Strategy Selection

IF task has <3 decision branches total
THEN → Use simple GOMS decomposition: Goals→Methods→Operators→Selection rules

IF task has 3-10 decision branches with environmental dependencies  
THEN → Use hierarchical GOMS with Critical Cue Inventories
     → Map each selection rule to specific perceptual cues

IF task has >10 decision branches or continuous adaptation required
THEN → Use situation awareness architecture with predict-compare-adjust loops
     → Build dynamic environmental state model

3. Expert Interview Protocol Selection

IF expert says "I just know" or "it's obvious" at decision points
THEN → Use Critical Decision Method (CDM) probing
     → Probe: "What do you see/hear/feel right before that decision?"

IF expert provides clear procedural explanations
THEN → Assume 50% completeness: Science layer captured, art layer missing
     → Validate with second expert to reveal systematic omissions

IF multiple experts disagree on procedure  
THEN → Document both methods: Disagreement IS the selection rule
     → Map environmental conditions that trigger each expert's method

4. Simulation Fidelity Requirements

IF simulation training transfers poorly to real deployment
THEN → Wrong fidelity focus: Rendering procedures instead of cues
     → Include only environmental details that appear in Critical Cue Inventories

IF simulation handles textbook scenarios but not edge cases
THEN → Missing failure mode scenarios: Add degraded conditions
     → Stress perceptual cues, not just logical procedures

IF simulation feedback is delayed or abstracted
THEN → Real-time cue-action pairing broken: Redesign immediate feedback loops

5. Agent Architecture Selection

IF agent needs predictive capabilities (anticipate, not just react)
THEN → Implement continuous monitoring goals, not just sequential tasks
     → Build command-predict-observe-compare-adjust loops

IF agent operates in degraded conditions (noise, partial information)  
THEN → Design multi-channel perception with graceful degradation
     → No single points of perceptual failure

IF agent must handle novel situations within domain expertise
THEN → Capture pattern recognition, not just rule following
     → Build situational cue libraries for pattern matching

Failure Modes

1. Science-Only Implementation

Detection: System works on textbook cases, fails when reality diverges from script Diagnosis: Captured explicit procedures but missed implicit perceptual knowledge Fix: Conduct Critical Cue Inventory for each decision branch; map environmental signals to selection rules

2. Single Expert Validation Gap

Detection: First expert's model seems complete but validation reveals systematic omissions Diagnosis: Expert automaticity conceals habitual tools/context-setup behaviors Fix: Multi-expert validation protocol; specifically probe for "obvious" setup steps and tool usage

3. Sequential-Only Orchestration

Detection: Agent can only react to events, never anticipates; no situational awareness Diagnosis: Modeled all goals as sequential nodes; missing continuous monitoring loops
Fix: Identify recurring goals (assessment, monitoring); implement loop architecture for ongoing state awareness

4. Procedure-Centric Simulation

Detection: Training in simulation doesn't transfer to real deployment performance Diagnosis: Simulated logical steps without perceptual decision triggers Fix: Map Critical Cue Inventory to simulation elements; render cues that trigger decisions, not just visual realism

5. Post-Incident Failure Classification

Detection: Cascading failures from unclassified edge cases; reactive rather than proactive failure handling Diagnosis: No pre-classification of foreseeable failure modes and response options Fix: Pre-classify failure signatures; implement "Restricted Maneuvering" doctrine with pre-warmed responses

Worked Example: Ship-Handling Agent Design

Expert Interview Phase

SME Statement: "When approaching the pier, I adjust speed based on conditions" CDM Probe: "What specific signals tell you to adjust speed?" Expert Response: "Wind noise around superstructure changes pitch, wake turbulence pattern around pier pilings, how quickly fixed reference points move relative to ship"

Task Decomposition

GOAL: Approach pier safely
├── METHOD A: Standard approach [IF wind <15 knots AND clear sight lines]
│   ├── Monitor relative motion cues (visual)
│   ├── Monitor wind sound signature (auditory)  
│   └── Adjust throttle based on convergence rate
└── METHOD B: Controlled approach [IF wind >15 knots OR restricted visibility]
    ├── Use redundant position sensing (radar + visual + pilot input)
    ├── Monitor line tension (tactile feedback through deck vibration)
    └── Engage tugboat assistance [IF available]

Critical Cue Inventory Extract

Decision Point	Perceptual Cue	Signal Type	What It Indicates
Speed adjustment	Wind pitch change around superstructure	Auditory	Relative wind speed increasing
Distance assessment	Wake turbulence pattern at pier pilings	Visual	Proximity to pier structure
Final approach timing	Fixed reference point motion rate	Visual	Ground speed and angle

Agent Implementation

Perception Layer: Multi-channel sensors for visual (camera array), auditory (directional microphones), tactile (vibration sensors) Decision Layer: If relative-motion-rate > threshold AND wind-pitch > baseline → engage Method B Action Layer: Throttle control with tugboat coordination protocol

Validation Gap Discovery

Initial Model: No tugboat usage (expert unconsciously assumed tugboat unavailability)
Validation Finding: All validation experts used tugboats as standard practice Fix: Added tugboat coordination as default method; tugboat-unavailable as exception case

Quality Gates

• Each decision branch in task decomposition has explicit selection rules with environmental triggers
• Critical Cue Inventory completed for all major decision points (what expert sees/hears/feels)
• Multi-expert validation conducted with systematic probe for "obvious" omitted steps
• Recurring goals identified and separated from sequential goals in architecture
• Failure modes pre-classified with detection signatures and response protocols
• Agent perception layer has redundant sensing for all critical decision inputs
• Simulation environment includes only CCI-validated cues (not decorative realism)
• Selection rules tested against degraded conditions (noise, partial information, time pressure)
• Expert can recognize their own decision process in final agent/system design
• System performs comparably to expert on novel scenarios within domain boundaries

NOT-FOR Boundaries

Do NOT use this skill for:

• Simple rule-following tasks with <3 decision branches → Use standard process documentation instead
• Tasks where all decision inputs are discrete/digital → Use decision tree or expert system approaches
• Creative or open-ended problem solving → Use brainstorming/ideation skills instead
• Tasks where expert performance varies widely across practitioners → Use consensus-building skills first
• Real-time performance optimization → Use system tuning/performance analysis skills instead

Delegate to other skills when:

• Building user interfaces → Use UX design skills
• Optimizing computational performance → Use algorithm optimization skills
• Managing expert interview logistics → Use project management skills
• Statistical validation of agent performance → Use experimental design skills
• Legal/regulatory compliance → Use domain-specific compliance skills

This skill operates specifically in the knowledge elicitation and task modeling phase of intelligent system design, not in implementation, optimization, or deployment phases.