dimensional-analysis
Skill2 starsChanged 6 months agoApache-2.0View on GitHub
Campaign for mapping design spaces — discover dimensions/axes, enumerate combinations, identify gaps and novel opportunities. Produces dimensional maps in the wiki vault.
3 other projects use a version of this.
Adopted by
Most recently taken up by
- Walsworth-Group/physkittheir own version, first seen Nov 8, 2026
- davidanthonyn/claude-skillstheir own version, first seen Oct 20, 2026
- BenSchZA/skillstheir own version, first seen Oct 13, 2026
Other versions3 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 3
- Walsworth-Group/physkit★ 7same name
- BenSchZA/skills★ 1same name
- davidanthonyn/claude-skills★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository70 other files it gives its agents.
- alias-resolutionSkill
- argument-mappingSkill
- argument-synthesisSkill
- argument-visualizationSkill
- axis-extractionSkill
- axis-validationSkill
- causal-chain-querySkill
- causal-modelingSkill
- claim-decompositionSkill
- claim-extractionSkill
- claim-page-creationSkill
- combination-enumerationSkill
- combination-mappingSkill
- concept-decompositionSkill
- concept-extractionSkill
- concept-page-creationSkill
- confidence-scoringSkill
- confidence-updateSkill
- consistency-checkingSkill
- contradiction-flaggingSkill
- counterfactual-reasoningSkill
- dimension-discoverySkill
- dimension-page-creationSkill
- domain-scopingSkill
- edge-batch-creationSkill
- evidence-attachmentSkill
- evidence-collectionSkill
- evidence-linking-argSkill
- evidence-linkingSkill
- evidence-weighingSkill
- feedback-loop-detectionSkill
- gap-detectionSkill
- gap-prioritizationSkill
- hierarchy-constructionSkill
- hierarchy-visualizationSkill
- intervention-analysisSkill
- intervention-page-creationSkill
- knowledge-compilationSkill
- loop-documentationSkill
- matrix-exportSkill
- matrix-generationSkill
- mechanism-edge-creationSkill
- mechanism-mappingSkill
- merge-candidatesSkill
- model-gap-detectionSkill
- model-validationSkill
- novelty-scoringSkill
- ontology-buildingSkill
- ontology-exportSkill
- ontology-refinementSkill
- question-generationSkill
- rebuttal-documentationSkill
- relation-typingSkill
- seed-concept-searchSkill
- source-gatheringSkill
- strength-assessmentSkill
- strength-scoringSkill
- synthesis-reportSkill
- taxonomy-validationSkill
- validation-reportSkill
- The first 60 shown.
The file
We split this skill into the rules it gives an agent
5 rules. 0 are common, 2 are distinctive, and 3 are very specific to this project.
0
Common
Most of the 3 edited versions we read keep these.
2
Distinctive
This author's own choices. Worth a look.
3
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Very specific: line 46
Call context-init at campaign start
- Very specific: line 47
Call context-checkpoint after each strategy completes
- Very specific: line 48
Call knowledge-compilation after each strategy </HARD-GATE>
- Distinctive: line 53
Dimensions are independent: If two axes always co-vary, they're likely one dimension.
- Distinctive: line 56
Feasibility constrains: Not all combinations are realizable.
“Common” is measured: kept by at least 6 in 10 of the 3 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Dimensional Analysis
Map the design space of a research area by discovering its fundamental dimensions (axes of variation), enumerating meaningful combinations, and identifying unexplored regions that represent novel opportunities.
Manifest
| Level | Count | Skills |
| Strategy | 3 | dimension-discovery, combination-mapping, gap-prioritization |
| Tactic | 2 | axis-extraction, matrix-generation |
| SOP | 6 | dimension-page-creation, axis-validation, combination-enumeration, novelty-scoring, question-generation, matrix-export |
Budget Table
| Metric | Small | Medium | Large |
| Dimensions identified | 3 | 6 | 10 |
| Axes per dimension | 3 | 5 | 8 |
| Combinations explored | 10 | 30 | 60 |
| Novel gaps identified | 3 | 8 | 15 |
| Questions generated | 5 | 12 | 25 |
Strategy Sequence (Reference, Not Prescription)
- dimension-discovery — identify fundamental axes of variation in the design space
- combination-mapping — enumerate interesting combinations, mark existing work
- gap-prioritization — rank unexplored combinations by novelty and feasibility
MCP Tools Used
vault_search— find existing coverage of dimension valuesvault_add_edge— connect dimensions to conceptsvault_query_graph— explore existing dimension neighborhoodsvault_graph_stats— assess coverage completeness
Context-Management
<HARD-GATE>
- Call `context-init` at campaign start
- Call `context-checkpoint` after each strategy completes
- Call `knowledge-compilation` after each strategy
</HARD-GATE>Guiding Principles
- Dimensions are independent. If two axes always co-vary, they're likely one dimension.
- Completeness over depth. Map the full space first, then drill into promising regions.
- Empty cells are opportunities. An unexplored combination in a well-populated matrix is a research opportunity.
- Feasibility constrains. Not all combinations are realizable. Mark impossible combinations explicitly.
- Cross-domain dimensions. The most interesting dimensions often come from outside the domain.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.