towards-a-taxonomy-of-cognitive-task-ana

name	towards-a-taxonomy-of-cognitive-task-ana
description	Use for cognitive task analysis, CTA method selection, knowledge capture, tacit expertise routing, and representation design by matching elicitation methods to knowledge type. NOT for generic ontology naming, benchmark-only model evaluation, or ML architecture tuning.
license	Apache-2.0
allowed-tools	Read,Grep,Glob
metadata	{"category":"Knowledge Engineering","tags":["cta","knowledge-capture","routing","taxonomy","procedural-knowledge"],"provenance":{"kind":"first-party","owners":["some-claude-skills"],"source":{"title":"Towards a Taxonomy of Cognitive Task Analysis Methods","authors":"[Truncated]"}},"authorship":{"authors":["some-claude-skills"],"maintainers":["some-claude-skills"]}}

Towards a Taxonomy of Cognitive Task Analysis Methods

Source basis: Kenneth Anthony Yates on how elicitation methods bias what kinds of expert knowledge get captured and how that affects system design.

When to Use

• An agent underperforms experts and the missing capability feels tacit or hard to verbalize.
• A knowledge base or prompt was built mainly from expert interviews or self-report.
• You need to decide how to capture, represent, or route expertise across a skill library.
• A taxonomy keeps growing without an organizing theory or any reduction pressure.
• You suspect the chosen representation format is driving the capture method instead of the other way around.

NOT for

• Generic label taxonomies or ontology cleanup with no link to expert-performance capture.
• Benchmark-focused model evaluation that does not involve knowledge elicitation or capability routing.
• Pure machine learning architecture selection divorced from the problem of expert knowledge capture.

Decision Points

1. Classify the target knowledge: declarative, procedural-classify, or procedural-change.
2. Estimate automation-gap risk. If experts are fast and reliable but poor at explanation, self-report alone is insufficient.
3. Choose capture methods based on the knowledge type, not on the output format you hope to build.
4. Decide whether the library is a typology or a real taxonomy by asking what theory would let categories consolidate over time.

Decision Flow

flowchart TD
  A[Knowledge capture request] --> B{Knowledge type}
  B -->|Declarative| C[Use interviews, document analysis, structured schemas]
  B -->|Procedural classify| D[Use observation, examples, contrastive cases]
  B -->|Procedural change| E[Use process tracing, simulation, replay, intervention review]
  C --> F{Automation gap high?}
  D --> F
  E --> F
  F -->|Yes| G[Do not rely on self-report alone]
  F -->|No| H[Proceed with mixed methods]
  G --> I{Representation driving capture?}
  H --> I
  I -->|Yes| J[Reset around knowledge type first]
  I -->|No| K[Design routing and taxonomy]
  J --> K

Working Model

• Expertise has an automation gap. The knowledge that makes experts fast and reliable is often the part they can least report directly.
• Knowledge has architecture. Declarative facts, procedural classification, and procedural change skills are different targets and need different capture strategies.
• Methods are not neutral. Interviews, concept maps, protocol analysis, and observation open access to different layers of cognition.
• Representation bias is circular. If rules, templates, or embeddings dictate capture method, you will overfit the knowledge to the format.
• Taxonomies should reduce, not just proliferate. Growth without consolidation signals missing theory.

Failure Modes

• Interviewing experts and mistaking articulate explanations for complete knowledge capture.
• Choosing capture methods because they map neatly to a preferred output format.
• Using one expert or one method and assuming the blind spots will average out.
• Routing skills by keyword or name when the real difference is knowledge type.
• Growing a capability library by accretion instead of revising the underlying organizing theory.

Anti-Patterns and Shibboleths

• Anti-pattern: collecting articulate interview answers and calling the tacit layer captured.
• Anti-pattern: designing the embedding schema or template first and then forcing the elicitation method to fit it.
• Shibboleth: if routing logic could be replaced by keyword matching with no loss, the CTA taxonomy is still too shallow.

Worked Examples

• A dispatcher-support agent fails on edge cases even though its prompt contains expert-written rules. The likely issue is procedural knowledge captured declaratively; add observation and process tracing before rewriting the prompt.
• A large skill library keeps spawning near-duplicate skills for planning, diagnosis, and review. The likely issue is typological growth; reorganize by knowledge type produced and consumed, then consolidate.

Fork Guidance

• Stay in-process when you are classifying one task and choosing one capture strategy.
• Fork separate subagents only when you need independent audits of knowledge type, capture method, and routing theory for the same system before merging findings.

Quality Gates

• The target task is decomposed by knowledge type before method selection starts.
• Capture methods are justified by what knowledge they can reach, not by what output artifact they produce.
• Procedural blind spots are named explicitly when self-report is used.
• The resulting taxonomy has a path to consolidation, not just more categories.
• Routing logic uses theory about knowledge type rather than surface naming alone.

Reference Routing

• references/expert-knowledge-automation-gap.md: load when experts outperform the system in ways they struggle to explain.
• references/declarative-vs-procedural-knowledge-in-agent-systems.md: load when representation is mismatched to the kind of expertise required.
• references/method-selection-drives-knowledge-outcomes.md: load when choosing among capture methods.
• references/representation-bias-and-knowledge-fidelity.md: load when format is starting to dictate what knowledge gets captured.
• references/skill-selection-as-cognitive-task-analysis-problem.md: load when routing or orchestration fails on ambiguous cases.
• references/building-theory-driven-agent-capability-taxonomies.md: load when the library needs an organizing theory instead of more names.
• references/taxonomy-theory-and-the-proliferation-trap.md: load when category growth outpaces explanatory power.