Ask
Query the bundled research knowledge graph for methodology guidance.
Install
npx skills add agenticnotetaking/arscontexta --skill ask -g -a claude-code -y- Repository
- agenticnotetaking/arscontexta3.5k
- Category
- Writing
- Updated
- 8mo ago
- License
- MIT
- Context cost
- Mediumabout 3,000 tokens
- Runs scripts
- No, instructions only
What it does
Query the bundled research knowledge graph for methodology guidance. Routes questions through a 3-tier knowledge base, WHY (research claims), HOW (guidance docs), WHAT IT LOOKS LIKE (domain examples), plus structured reference documents. Returns research-backed answers grounded in specific claims with practical application to the user's system. Triggers on "/ask", "/ask [question]", "why does my system...", "how should I...".
EXECUTE NOW
Question: $ARGUMENTS
If no question provided, ask the user what they want to know.
Execute these steps:
- Classify the question, determine which knowledge base tier(s) to consult (see Query Classification below)
- Search the knowledge base, route to appropriate tiers based on classification
- Read relevant claims and docs, load 3-7 most relevant sources fully (use
mcp__qmd__multi_getwhen reading multiple IDs) - Check user context, read
ops/derivation.mdif the question involves their specific system - Synthesize an answer, weave claims into a coherent, opinionated argument
- Cite sources, reference specific claims and documents so the user can explore further
START NOW. Reference below explains routing and synthesis methodology.
The Three-Tier Knowledge Base
The plugin's knowledge base has three distinct parts, each serving a different function. Effective answers often draw from multiple tiers.
Tier 1: Research Graph (WHY)
Location: ${CLAUDE_PLUGIN_ROOT}/methodology/, filter by kind: research
Content: 213 interconnected research claims grounded in cognitive science, knowledge system theory, and agent cognition research.
Use for: Questions about principles, trade-offs, why things work, theoretical foundations.
What it contains:
- Claims about how knowledge systems work (human and agent)
- Cognitive science foundations (working memory, attention, retrieval)
- Methodology comparisons (Zettelkasten vs PARA, atomic vs compound)
- Design dimensions (trade-off spectrums with poles and decision factors)
- Failure modes and anti-patterns
- Agent-specific constraints (context windows, session boundaries)
Search strategy: Use mcp__qmd__deep_search (highest quality, LLM-reranked) for conceptual questions. Use mcp__qmd__vector_search for semantic exploration. Use mcp__qmd__search for known terminology. All searches use the methodology collection.
