| name | dd - Dimension Discovery |
| description | Identify the key dimensions that define a problem space, enabling comprehensive enumeration. Use before space_enumeration for novel domains. |
| output | {"format":"table"} |
Dimension Discovery
Input: $ARGUMENTS
Interpretations
Before executing, identify which interpretation matches the user's input:
Interpretation 1 — Map a problem space: The user has a domain or topic and wants to discover the independent axes of variation that define it, enabling systematic exploration.
Interpretation 2 — Analyze a dataset's structure: The user has data (a spreadsheet, survey, product catalog) and wants to identify the key dimensions or variables that characterize it.
Interpretation 3 — Find all ways something varies: The user is asking "what are all the ways this could differ?" about a concept, product, or situation — they want to see the full shape of the variation.
If ambiguous, ask: "I can help with mapping a conceptual problem space, analyzing dimensions in a dataset, or discovering all the ways something varies — which fits?"
If clear from context, proceed with the matching interpretation.
Purpose
Before generating a comprehensive list, you need to know the dimensions that define the space. This skill identifies those dimensions for any domain.
Dimensions are independent axes of variation. For "types of vehicles":
- Dimension 1: Power source (gas, electric, human, hybrid)
- Dimension 2: Medium (land, water, air, space)
- Dimension 3: Purpose (personal, commercial, military, recreational)
With dimensions identified, /se can systematically cover the space.
When to Use
| Situation | Use This Skill |
|---|
| Novel domain with no known structure | YES |
| Generating comprehensive list | YES (before /se) |
| Domain structure already known | SKIP (use known dimensions directly) |
| Simple enumeration | SKIP (overkill) |
Depth Scaling
Default: 2x. Parse depth from $ARGUMENTS if specified (e.g., "/dd 4x [input]").
| Depth | Min Dimensions Found | Min Sources/Methods | Min Hidden Dimensions | Min Validation Tests |
|---|
| 1x | 4 | 2 | 1 | 1 |
| 2x | 6 | 3 | 2 | 2 |
| 4x | 9 | 5 | 3 | 3 |
| 8x | 12 | 7 | 5 | 4 |
| 16x | 16 | 10 | 8 | 6 |
These are floors. Go deeper where insight is dense. Compress where it's not.
The Process
Step 1: Seed with Examples
Start with 5-10 concrete examples of what you're trying to enumerate.
EXAMPLES FOR: [domain]
1. [example 1]
2. [example 2]
3. [example 3]
...
Why examples first? Dimensions emerge from comparing examples.
Step 2: Compare for Differences
For each pair of examples, ask: "What makes these different?"
COMPARING: [example A] vs [example B]
Differences:
- [difference 1] -> Potential dimension: [name]
- [difference 2] -> Potential dimension: [name]
Step 3: Apply Universal Dimensions
Check these universal dimensions (apply to almost any domain):
| Dimension | Question | Example Values |
|---|
| WHO | Who is involved? | Individual, team, organization, society |
| WHAT | What type/category? | Domain-specific types |
| WHEN | What time frame? | Immediate, short-term, long-term, recurring |
| WHERE | What location/context? | Local, regional, global, virtual |
| WHY | What purpose/goal? | Primary, secondary, indirect |
| HOW | What method/approach? | Manual, automated, hybrid |
| HOW MUCH | What scale/degree? | Small, medium, large, extreme |
For each: Does this dimension create meaningful variation in [domain]?
Step 4: Identify Domain-Specific Dimensions
Beyond universal dimensions, what's unique to this domain?
DOMAIN-SPECIFIC DIMENSIONS FOR: [domain]
1. [Dimension name]
- What it captures: [explanation]
- Possible values: [value1, value2, value3, ...]
- Why it matters: [importance]
2. [Dimension name]
...
Step 5: Validate Independence
Dimensions should be independent (not redundant):
INDEPENDENCE CHECK:
- Can [Dimension A] vary while [Dimension B] stays constant?
- If NO -> Dimensions may be correlated, consider merging
- If YES -> Dimensions are independent, keep both
Step 6: Validate Completeness
Do the dimensions cover the examples?
COMPLETENESS CHECK:
For each example:
- [Example 1]: Dim1=[value], Dim2=[value], Dim3=[value] [x]
- [Example 2]: Dim1=[value], Dim2=[value], Dim3=[value] [x]
Any example that doesn't fit? -> Missing dimension
Step 7: Output Dimensions
DIMENSIONS DISCOVERED FOR: [domain]
| # | Dimension | Values | Validation |
|---|-----------|--------|------------|
| 1 | [name] | [v1, v2, v3, ...] | Covers examples [x] |
| 2 | [name] | [v1, v2, v3, ...] | Independent [x] |
| 3 | [name] | [v1, v2, v3, ...] | Meaningful variation [x] |
TOTAL SPACE SIZE: [D1 values] × [D2 values] × [D3 values] = [N] combinations
NOTES:
- [Any caveats about dimension interactions]
- [Any "other" category needed for edge cases]
Example: Dimensions for "Software Testing Types"
Step 1: Examples
- Unit tests
- Integration tests
- Load tests
- Security penetration tests
- User acceptance tests
- Regression tests
Step 2: Compare
- Unit vs Integration -> Scope (single unit vs multiple units)
- Load vs Security -> Focus (performance vs security)
- User acceptance vs Regression -> Purpose (validation vs verification)
Step 3: Universal Dimensions
- WHO: Developer, QA, User, Automated
- WHEN: Development, Pre-release, Post-release, Continuous
- HOW: Manual, Automated, Hybrid
Step 4: Domain-Specific
- Scope: Unit, Component, Integration, System, End-to-end
- Focus: Functional, Performance, Security, Usability, Reliability
- Automation level: Manual, Semi-automated, Fully automated
Step 5-6: Validate
All examples fit. Dimensions are independent.
Step 7: Output
| # | Dimension | Values |
|---|
| 1 | Scope | Unit, Component, Integration, System, E2E |
| 2 | Focus | Functional, Performance, Security, Usability, Reliability |
| 3 | Timing | Development, Pre-release, Post-release, Continuous |
| 4 | Automation | Manual, Semi-automated, Fully automated |
TOTAL SPACE: 5 × 5 × 4 × 3 = 300 combinations
Quality Checklist
Before completing:
Next Steps
After dimension discovery:
- Use
/se to generate comprehensive list
- Use
/mv to verify coverage
