/argument-visualization
SOP for generating argument structure visualization — query graph for
$ npx -y skills add yogsoth-ai/de-anthropocentric-research-engine --skill argument-visualization --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/argument-visualization
Context preview
The summary Claude sees to decide when to auto-load this skill.
SOP for generating argument structure visualization — query graph for
SKILL.md
argument-visualization.SKILL.mdname: argument-visualization description: SOP for generating argument structure visualization — query graph for argument chains, format as mermaid diagram or indented tree, write to vault. execution: sop
Argument Visualization
Generate a visual representation of the argument structure for a topic.
Tool
`vault_query_graph` + CC file write
Protocol
1. Query graph starting from the topic node, traversing supported_by, contradicts, and derived_from edges 2. Collect all claims, evidence, and their relationships 3. Format as a mermaid diagram in the wiki page:
- Green nodes: strong claims (strength ≥ 7)
- Yellow nodes: moderate claims (strength 4-6)
- Red nodes: weak claims (strength ≤ 3)
- Solid edges: supported_by
- Dashed edges: contradicts
4. Write/update `wiki/topics/<topic-slug>.md` with the argument map section 5. Include a summary: total claims, strongest/weakest, key contradictions
HARD-GATE
<HARD-GATE> Visualization must include at least 3 claims and their relationships. A single-node diagram is not useful. </HARD-GATE>
Yield
Returns: `{ topic: string, claims_shown: number, edges_shown: number, format: "mermaid" }`
The complete research orchestration system for AI-native science. What It Does Design Philosophy Architecture (v3.2.2) Quick Start Configuration Roadmap License DARE is not a tool that helps you do research. It is the researcher.
Repo: yogsoth-ai/de-anthropocentric-research-engine
Other skills on de-anthropocentric-research-engine.
- /formated-results
Closing skill for the research-executor, loaded as the last step of formated-specs. Summarize the design just produced into one research-result JSON fenced block in your reply. Do not execute the research.
Open skill - /formated-specs
Spec-slot skill for the research-executor. Emit the 4-layer DARE orchestration of the assigned topic as one research-graph JSON fenced block in your reply. Replaces the generic spec-writing step.
Open skill - /injection-fidelity
Loss-1 judge (codex role). Given one sample's de-identified dialogue and its PolicyCard, decide axis-by-axis whether the user-simulator enacted the card's per-axis pressure. Judge enactment of the card, never whether the research is good.
Open skill - /ladder-quality-order
Loss-2 judge (codex role). Over one topic's 6 shuffled research-design samples, pairwise-rank by quality using the D1–D5 standard. Emit the pairwise log; the harness computes the order and the ladder verdicts. Judge quality difference, never against academic standards.
Open skill - /optimization-loop
The optimizer brain for the ladder-foundry pretraining loop. Runs the two-level nested batch loop, delegates gating to gate_eval, attributes a failing batch to one weight (attribute-first), and recovers from disk after compaction. Control flow is fully scripted; only the
Open skill - /acu-nugget-recall
Tactic: Extract atomic units from one paper and score how much of a caller-supplied summary covers. Use for ACU-style binary or Nugget-style ternary recall checks; cannot run without a target summary.
Open skill