curiositech 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.