enumerate-dimension-values
SkillDev toolsLets your agent generate a list of test values for a variable, including normal, boundary, and extreme cases.
Available today. Use it from your connected AI after setup.
No other account needed.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the enumerate-dimension-values skill
About this skill
Enumerate meaningful values/levels for a dimension or factor. Mode controls representative, experimental, boundary, pathological, or adversarial coverage.
What this skill tells your AI
The instructions your AI receives, as published by yogsoth-ai/de-anthropocentric-research-engine in skills/enumerate-dimension-values/SKILL.md and read by ahel’s review.
Purpose
Enumerate meaningful values or levels for a dimension or factor, including representative, experimental, boundary, pathological, or adversarial coverage.
Input contract
required: [dimensions, value_schema, mode]
optional: [ranges, spacing_strategy, domain_constraints, baseline_values]
constraints: [values are typed to the dimension; mode determines representative, experimental, boundary, pathological, or adversarial coverage]
Procedure
- Validate each dimension's type, range, units, and constraints.
- Generate representative values and caller-requested boundary or extreme values.
- For experimental mode, apply the supplied spacing strategy and preserve factor comparability.
- Deduplicate and annotate rationale, provenance, and expected coverage.
- Return the value set per dimension.
If the dimension values are ready for controlled combination, consider construct-design-matrix as the next tactic.
If the values define plausible perturbations around a conclusion, consider assess-sensitivity as the next tactic.
Output contract
produces: [dimension_value_sets, boundary_cases, spacing_annotations, coverage_rationale]
delta_fields: [findings, evidence_updates, decisions, uncertainties]
Quality gates
- Preserve the source representative enumeration of 3-5 values per parameter when that mode is selected.
- Preserve experimental factor levels of 2-5 levels with an explicit spacing strategy.
- Boundary/extreme mode must include the caller-defined boundary, pathological, distribution-shift, rare-combination, or scale-extreme cases; do not invent a fixed case count.
Parameterization
The caller must provide dimensions and types, valid ranges and units, enumeration mode, spacing strategy, baseline values, and domain constraints. If experimental mode is used, provide factor roles and the intended comparison design.
Failure and counterexamples
Reject values outside declared domains, levels without units or semantics, or pathological cases presented as representative defaults.
Provenance map
- resolved: creative-ideation/value-enumeration
- resolved: stress-test/extreme-value-generation
- resolved: deep-insight/edge-case-generation
- resolved: experiment-execution/level-specification
- intermediate: Pass3/enumerate-values
- intermediate: Pass3/generate-extreme-values
- intermediate: Pass3/specify-factor-levels
Verbatim source criteria excerpts
value-enumerationline 14: Enumerate 3-5 meaningful values per parameter, ensuring coverage of boundary and extreme values.
Preserved source criteria ledger
| source | physical line | kind | source criterion |
|---|---|---|---|
| creative-ideation/value-enumeration | 11 | numeric | Enumerate 3-5 values per parameter, including extremes. |
| experiment-execution/level-specification | 12 | numeric | Experimental factor levels use 2-5 levels and an explicit spacing strategy. |
Signals
- GitHub stars
- 501
- Forks
- 41
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
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enumerate-dimension-values- Source
- github.com/yogsoth-ai/de-anthropocentric-research-engine