construct-argument-map
SkillFiles & storageLets your agent break claims into premises and counterclaims, attach evidence, and build an inspectable argument map.
Use construct-argument-map in Claude, ChatGPT or Ahel Desktop
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Also: Claude Code · Cursor · Codex
Then ask your AI: use the construct-argument-map skill
Details
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.
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Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
About this skill
Atomize claims, expose premises and counterclaims, attach evidence/defeaters, score claim strength, and construct an inspectable argument graph independent of storage format.
What this skill tells your AI
The instructions your AI receives, as published by yogsoth-ai/de-anthropocentric-research-engine in skills/construct-argument-map/SKILL.md and read by ahel’s review.
Purpose
Atomize claims, expose premises and counterclaims, attach evidence/defeaters, score claim strength, and construct an inspectable argument graph independent of storage format.
Input contract
required: [claim_records, premise_records, evidence_records]
optional: [assumptions, prior_findings, evidence_updates]
constraints: [consume named scientific objects; preserve provenance; keep unresolved uncertainty visible]
Execution protocol
Do not perform called SOP operations inline; each loaded SOP owns its contract and thresholds.
- You MUST load skill
atomize-claimto split compound claims into atomic propositions. - You MUST load skill
surface-assumptionsto register explicit and implicit load-bearing assumptions. - You MUST load skill
attach-evidence-to-relationto link source records to typed relations with directness, independence, consistency, and alternative interpretations. - You MUST load skill
document-counterclaimto add credible counterclaims with their basis, scope, implications, and possible rebuttals. - You MUST load skill
score-objectto score each typed object against the supplied rubric without inventing missing evidence. - You MUST load skill
construct-critiqueto attack the assembled argument and rank its strongest weakness. - You MUST load skill
detect-contradictionto compare opposing claims under shared scope and record the evidence needed to adjudicate them. If the argument requires a balanced attack-defense exchange, consideradversarial-deliberation. If its apparent simplicity may hide unsupported relabeling, consideraudit-explanatory-compression.
Deviation: reorder only when a dependency is already satisfied or unavailable; record the reason and confidence effect.
Output contract
produces: [argument_graph, evidence_links, counterclaims, strength_assessment]
delta_fields: [evidence_updates]
Thresholds and quality gates
- Each output is traceable to an input object, operation, and evidence reference.
- Scope, assumptions, and unresolved alternatives remain explicit.
- Retain $\alpha$ 0.05 and power 0.8 wherever the predeclared statistical design requires them.
Failure and counterexamples
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.
Provenance map
- intermediate: knowledge-structuring/argument-mapping [campaign]
- resolved: claim-extraction
- intermediate: premise-identification [strategy]
- intermediate: counterargument-mapping [strategy]
- resolved: evidence-linking-arg
- resolved: argument-synthesis
- intermediate: claim-decomposition [tactic]
- resolved: strength-assessment
Preserved source criteria ledger
| 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 |
Context checkpoint / Delta notes
Return the node-specific research-state delta and preserve findings, evidence updates, uncertainties, decisions, open questions, and recommended jumps as applicable.
Signals
- GitHub stars
- 503
- Forks
- 42
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
construct-argument-map- Source
- github.com/yogsoth-ai/de-anthropocentric-research-engine
github.com/yogsoth-ai/de-anthropocentric-research-engine