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Query the Ars Contexta research methodology. Answers 'why does my system do X?' with research-backed explanations from 249 interconnected claims.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
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Query the Ars Contexta research methodology. Answers 'why does my system do X?' with research-backed explanations from 249 interconnected claims.
用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Add a new knowledge domain to an existing vault. Creates domain-specific MOCs, templates, and vocabulary mappings.
Research-backed vault evolution guidance. Toggle features (self-space, semantic search), rebalance MOCs, adjust dimensions with full rationale.
Analyze the vault's knowledge graph. Subcommands: orphans, density, bridges, synthesis, traverse, clusters, stats.
Run vault health diagnostics. 3 modes: quick (schema+orphans+links), full (all 8 categories), three-space (boundary violations).
Contextual guidance and command discovery. Shows available skills, agents, and intelligent suggestions based on vault state.
End-to-end source processing: reduce → reflect → reweave → verify. Fresh context per phase for optimal LLM attention.
| name | ask |
| description | Query the Ars Contexta research methodology. Answers 'why does my system do X?' with research-backed explanations from 249 interconnected claims. |
| tags | ["methodology","research","learning"] |
Delegates to: @ars-contexta:knowledge-guide
Invoke this skill when the user asks a "why" question about the system's design, wants to understand the research backing for a feature, or wants to explore the methodology behind a vault convention.
Question Reception Accept the user's question verbatim. Examples of well-suited questions:
Methodology Search Search the methodology knowledge base in this order:
ops/methodology/ — vault-local methodology notes, if presentops/derivation.md — for vault-specific rationale tied to this user's derivationTrace the answer back to specific named claims or research traditions. Do not give generic knowledge-management advice — always anchor to the specific research the system is built on.
Answer Structure Structure every answer in three parts:
The direct answer — one or two sentences stating what the system does and why.
The research backing — cite the specific cognitive science finding, network theory principle, or knowledge management tradition that justifies this design. Name the claim, researcher, or framework (e.g., "Claim 47: retrieval practice strengthens memory consolidation", "Luhmann's slip-box principle of forced articulation", "small-world network theory applied to knowledge graphs").
The practical implication — explain what breaks if this convention is ignored, so the user understands the cost of deviation rather than just being told to comply.
Uncertainty Handling If the question cannot be traced to a specific research claim, say so explicitly. Do not fabricate citations. Instead, explain what is known and suggest where the user might find a more authoritative answer.
Follow-up After answering, ask: "Would you like to see related methodology claims, or does this answer your question?"