# Ask

Query the bundled research knowledge graph for methodology guidance.

- Repository: https://github.com/agenticnotetaking/arscontexta (3518 stars)
- Category: Writing
- License: MIT
- Last updated: 2026-02-24
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add agenticnotetaking/arscontexta --skill ask -g -a claude-code -y`
Codex: `npx skills add agenticnotetaking/arscontexta --skill ask -g -a codex -y`
Cursor: `npx skills add agenticnotetaking/arscontexta --skill ask -g -a cursor -y`
Gemini CLI: `npx skills add agenticnotetaking/arscontexta --skill ask -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add agenticnotetaking/arscontexta`

## Description

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...".

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

## EXECUTE NOW

**Question: $ARGUMENTS**

If no question provided, ask the user what they want to know.

**Execute these steps:**

1. **Classify the question**, determine which knowledge base tier(s) to consult (see Query Classification below)
2. **Search the knowledge base**, route to appropriate tiers based on classification
3. **Read relevant claims and docs**, load 3-7 most relevant sources fully (use `mcp__qmd__multi_get` when reading multiple IDs)
4. **Check user context**, read `ops/derivation.md` if the question involves their specific system
5. **Synthesize an answer**, weave claims into a coherent, opinionated argument
6. **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.

The rest: https://github.com/agenticnotetaking/arscontexta/blob/HEAD/skills/ask/SKILL.md