abstract-structure
Remove domain surface details to expose transferable relational/mechanistic structure at a chosen abstraction level.
Build multi-level cause -> failure mode -> effect chains and identify cascades/shared roots.
Claude Code
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill build-failure-chain --agent claude-code
Codex
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill build-failure-chain -a codex -y
Cursor
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill build-failure-chain -a cursor -y
Gemini CLI
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill build-failure-chain -a gemini-cli -y
GitHub Copilot
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill build-failure-chain -a github-copilot -y
OpenCode
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill build-failure-chain -a opencode -y
Windsurf
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill build-failure-chain -a windsurf -y
name: build-failure-chain description: "Build multi-level cause -> failure mode -> effect chains and identify cascades/shared roots."
Build cause -> failure mode -> effect chains and identify cascades and shared roots.
required: [functions_or_process, failure_modes, observed_effects] optional: [causal_evidence, dependency_graph] constraints: [each edge needs a mechanism or evidence note]
1. Place causes, failure modes, and effects in typed levels. 2. Connect edges and record mechanism/evidence. 3. Mark cascades, shared roots, and unresolved links.
produces: [failure_chain, cascade_map, shared_root_report] delta_fields: [findings, evidence_updates, uncertainties, decisions]
Do not collapse correlation into a causal chain without an evidence or mechanism note.
A research skill graph for AI-native science. 271 markdown files. No runtime, no dependencies, no build step.
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