abstract-structure
Remove domain surface details to expose transferable relational/mechanistic structure at a chosen abstraction level.
Construct a balanced/randomized/orthogonal experimental design matrix appropriate to the chosen design mode.
Claude Code
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill construct-design-matrix --agent claude-code
Codex
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill construct-design-matrix -a codex -y
Cursor
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill construct-design-matrix -a cursor -y
Gemini CLI
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill construct-design-matrix -a gemini-cli -y
GitHub Copilot
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill construct-design-matrix -a github-copilot -y
OpenCode
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill construct-design-matrix -a opencode -y
Windsurf
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill construct-design-matrix -a windsurf -y
name: construct-design-matrix description: "Construct a balanced/randomized/orthogonal experimental design matrix appropriate to the chosen design mode."
Construct a balanced/randomized/orthogonal experimental design matrix appropriate to the chosen design mode.
required: [factor_schema, design_mode, run_budget, randomization_constraints] optional: [evidence, assumptions, prior_results] constraints: [use named scientific objects; retain provenance and missingness; $\alpha$ = 0.05 and power = 0.8 where applicable]
1. Validate the typed inputs and state the decision this operation must support. 2. Apply the declared operation to the named object; record intermediate values that affect interpretation. 3. Check boundary conditions and counterexamples, then emit the result with uncertainty and source links.
If the design cells are fixed but their outcomes and estimands are not, consider `specify-metrics` as the next tactic.
produces: [construct_design_matrix_result, evidence_trace, uncertainties] delta_fields: [evidence_updates, uncertainties]
Return a failed operation with the violated precondition when inputs are incomplete, assumptions are unsupported, or a counterexample defeats the result.
A research skill graph for AI-native science. 271 markdown files. No runtime, no dependencies, no build step.
Remove domain surface details to expose transferable relational/mechanistic structure at a chosen abstraction level.
Evaluate competing arguments against stated criteria and produce a reasoned verdict with uncertainty.
Move a scientific object up/down in abstraction or narrow/broaden selected scope dimensions (population, mechanism, context,…
Run structured attack/defense/adjudication over a claim, candidate, criterion set, or current winner. Perspective, target,…
Aggregate criterion or comparison results into an ordered recommendation under an explicit rule.
Abstract relational structure from source domains, map it to the target, validate depth, and instantiate transferable mechanisms.
Direct browser control via CDP for web interaction: automation, scraping, testing, screenshots, and site/app work. Also covers…
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling…