abstract-structure
Remove domain surface details to expose transferable relational/mechanistic structure at a chosen abstraction level.
Atomize claims, expose premises and counterclaims, attach evidence/defeaters, score claim strength, and construct an inspectable argument graph independent of storage format.
Claude Code
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill construct-argument-map --agent claude-code
Codex
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill construct-argument-map -a codex -y
Cursor
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill construct-argument-map -a cursor -y
Gemini CLI
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill construct-argument-map -a gemini-cli -y
GitHub Copilot
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill construct-argument-map -a github-copilot -y
OpenCode
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill construct-argument-map -a opencode -y
Windsurf
$ npx skills add yogsoth-ai/de-anthropocentric-research-engine --skill construct-argument-map -a windsurf -y
name: construct-argument-map description: "Atomize claims, expose premises and counterclaims, attach evidence/defeaters, score claim strength, and construct an inspectable argument graph independent of storage format."
Atomize claims, expose premises and counterclaims, attach evidence/defeaters, score claim strength, and construct an inspectable argument graph independent of storage format.
required: [claim_records, premise_records, evidence_records] optional: [assumptions, prior_findings, evidence_updates] constraints: [consume named scientific objects; preserve provenance; keep unresolved uncertainty visible]
Do not perform called SOP operations inline; each loaded SOP owns its contract and thresholds.
1. You MUST load skill `atomize-claim` to split compound claims into atomic propositions. 2. You MUST load skill `surface-assumptions` to register explicit and implicit load-bearing assumptions. 3. You MUST load skill `attach-evidence-to-relation` to link source records to typed relations with directness, independence, consistency, and alternative interpretations. 4. You MUST load skill `document-counterclaim` to add credible counterclaims with their basis, scope, implications, and possible rebuttals. 5. You MUST load skill `score-object` to score each typed object against the supplied rubric without inventing missing evidence. 6. You MUST load skill `construct-critique` to attack the assembled argument and rank its strongest weakness. 7. You MUST load skill `detect-contradiction` to compare opposing claims under shared scope and record the evidence needed to adjudicate them. If the argument requires a balanced attack-defense exchange, consider `adversarial-deliberation`. If its apparent simplicity may hide unsupported relabeling, consider `audit-explanatory-compression`.
Deviation: reorder only when a dependency is already satisfied or unavailable; record the reason and confidence effect.
produces: [argument_graph, evidence_links, counterclaims, strength_assessment] delta_fields: [evidence_updates]
Stop synthesis when a required object is absent, a precondition is violated, or a counterexample invalidates the proposed conclusion; return the partial delta with the failure recorded.
| source | criterion | treatment | |---|---|---| | resolved v3 entries above | node-specific criteria | retained and specialized to the v4 object contract | | experiment-execution/statistical-testing | $\alpha$ = 0.05 | fixed value retained where applicable | | experiment-execution/sample-size-estimation | power = 0.8 | fixed value retained where applicable |
Return the node-specific research-state delta and preserve findings, evidence updates, uncertainties, decisions, open questions, and recommended jumps as applicable.
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.
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