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...".
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...".
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_get when reading multiple IDs)
Check user context — read ops/derivation.md if 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:${CLAUDE_PLUGIN_ROOT}/methodology/ — filter by kind: researchContent: 213 interconnected research claims grounded in cognitive science, knowledge system theory, and agent cognition research.
Use for: Questions about principles, trade-offs, why things work, theoretical foundations.
What it contains:
Claims about how knowledge systems work (human and agent)
Search strategy: Use mcp__qmd__deep_search (highest quality, LLM-reranked) for conceptual questions. Use mcp__qmd__vector_search for semantic exploration. Use mcp__qmd__search for known terminology. All searches use the methodology collection.
Tier 2: Guidance Docs (HOW)
Location:${CLAUDE_PLUGIN_ROOT}/methodology/ — filter by
9 operational documents covering procedures, workflows, and implementation rationale.
Questions about how to do things, operational best practices, workflow mechanics.
kind: guidance
Content:
Use for:
Documents include:
Schema enforcement rationale and procedures
Pipeline philosophy and processing workflow
MOC methodology and navigation patterns
Maintenance patterns and condition-based triggers
Memory architecture and session management
Vocabulary transformation procedures
Failure mode prevention patterns
Multi-domain composition rules
Onboarding and evolution decisions
Search strategy:mcp__qmd__search with keywords from the question using the methodology collection. To narrow to guidance docs, add kind:guidance to your grep filter on results.
Tier 3: Domain Examples (WHAT IT LOOKS LIKE)
Location:${CLAUDE_PLUGIN_ROOT}/methodology/ — filter by kind: exampleContent: 12 domain-specific compositions showing what generated vaults look like in practice.
Use for: Questions about how to apply methodology to specific domains, inspiration for novel domain mapping.
Examples include domains like:
Research vaults (academic literature reviews, claim extraction)
Personal assistant vaults (life management, therapy, health wellness)
Search strategy: Use mcp__qmd__vector_search across the methodology collection for semantic domain matching. To list all examples: rg '^kind: example' ${CLAUDE_PLUGIN_ROOT}/methodology/.
Location:${CLAUDE_PLUGIN_ROOT}/reference/Content: Structured reference documents supporting derivation and system architecture.
Use for: Deep dives into specific architectural topics, cross-referencing dimension positions, understanding interaction constraints.
Core Architecture:
methodology.md — universal principles and processing pipeline
components.md — component blueprints and feature blocks
kernel.yaml — the 12 non-negotiable primitives
three-spaces.md — self/notes/ops architecture and boundary rules
Configuration & Derivation:
dimension-claim-map.md — which research claims inform which dimensions
interaction-constraints.md — how dimension choices create pressure on others
tradition-presets.md — named points in configuration space
vocabulary-transforms.md — universal-to-domain term mapping
derivation-validation.md — validation tests for derived systems
Behavioral & Quality:
personality-layer.md — personality derivation and encoding
conversation-patterns.md — worked examples of full derivation paths
failure-modes.md — how knowledge systems die and prevention patterns
Lifecycle & Operations:
use-case-presets.md — preset configurations for common domains
open-questions.md — unresolved research questions and deferred items
Also read:claim-map.md — the routing index showing which claims map to which topics. Start here when you need to find relevant claims quickly.
Query Classification
Before searching, classify the user's question to determine which tier(s) to consult.
Classification Rules
Question Type
Signals
Primary Tier
Secondary Tier
WHY
"why does...", "what's the reasoning...", "what's the theory behind...", "why not just..."
Research Graph
Guidance Docs
HOW
"how do I...", "what's the workflow for...", "how should I...", "what's the process..."
Guidance Docs
Research Graph
WHAT
"what does X look like...", "show me an example...", "how would this work for...", "what would a Y vault..."
Domain Examples
Guidance Docs
COMPARE
"X vs Y", "what's the difference between...", "should I use X or Y...", "trade-offs between..."
Research Graph
Examples
DIAGNOSE
"something feels wrong...", "why isn't this working...", "my system is doing X when it should..."
Guidance Docs + Reference (failure-modes.md)
Research Graph
CONFIGURE
"what dimension...", "how should I set...", "what configuration for...", "which preset..."
Reference (dimensions, constraints)
Research Graph
EVOLVE
"should I change...", "my system has grown...", "this doesn't fit anymore..."
Reference (evolution-lifecycle.md)
Guidance Docs
Multi-Tier Questions
Many questions require consulting multiple tiers. The classification above shows primary and secondary tiers. Always check: would the answer be stronger with evidence from another tier?
Example multi-tier routing:
Question: "Why does my system use atomic notes instead of longer documents?"
WHY tier — search research graph for claims about atomicity, granularity, composability
Reference — check dimension-claim-map.md for the granularity dimension's informing claims
User context — check ops/derivation.md for their specific granularity position and reasoning
WHAT tier — optionally pull an example showing what atomic notes look like in a similar domain
Question: "How should I handle therapy session notes that are very long?"
HOW tier — search guidance for processing workflow, chunking strategies
WHAT tier — check examples for therapy or personal domains with similar challenges
WHY tier — search for claims about context degradation, chunking benefits, large source handling
Search Strategy
Step 1: Route to Claim-Map
Read ${CLAUDE_PLUGIN_ROOT}/reference/claim-map.md first. This is the routing index — it shows which topic areas are relevant to the user's question and which claims to start with. Do NOT skip this step and search blindly.
Step 2: Search the Appropriate Tier
For WHY questions (Research Graph):
mcp__qmd__deep_search query="[user's question rephrased as a search]" collection="methodology" limit=10
Use mcp__qmd__deep_search (hybrid + LLM reranking) for conceptual questions because the best connections often use different vocabulary than the question. Results will include all kinds; prioritize kind: research results.
For HOW questions (Guidance Docs):
mcp__qmd__search query="[key terms from question]" collection="methodology" limit=5
Use keyword search first since guidance docs use consistent terminology. Fall back to semantic if keyword misses. Prioritize kind: guidance results.
For WHAT questions (Domain Examples):
mcp__qmd__vector_search query="[domain + what the user wants to see]" collection="methodology" limit=5
Use semantic search to find the most relevant domain examples even if the exact domain name differs. Prioritize kind: example results.
direct file reads/grep on ${CLAUDE_PLUGIN_ROOT}/methodology/ and ${CLAUDE_PLUGIN_ROOT}/reference/
For Reference documents:
Read specific reference documents based on the topic. The claim-map will indicate which reference docs are relevant. Load the 2-4 most relevant — not all of them.
Step 3: Read Deeply
Do NOT skim search results. For the top 3-7 results:
Read the full claim note or document
Follow wiki links to related claims (1 hop)
Note connections between claims that strengthen the answer
The depth principle: A shallow answer citing 10 claims is worse than a deep answer weaving 4 claims into a coherent argument. Read fewer sources more deeply.
Step 4: Check User Context
If the question involves the user's specific system:
Read derivation — ops/derivation.md contains their dimension positions, vocabulary, constraints, and the reasoning behind every configuration choice
Apply vocabulary — use ${CLAUDE_PLUGIN_ROOT}/reference/vocabulary-transforms.md to translate universal terms into their domain language. Answer about "reflections" not "claims" if they are running a therapy system
Check constraints — reference ${CLAUDE_PLUGIN_ROOT}/reference/interaction-constraints.md to see if their configuration creates specific pressures relevant to the question
Cite dimension-specific research — use ${CLAUDE_PLUGIN_ROOT}/reference/dimension-claim-map.md to ground answers in the specific claims that inform their configuration
Step 5: Check Local Methodology
Read ops/methodology/ for system-specific self-knowledge. Methodology notes may be more current than the derivation document — they capture ongoing operational learnings that the original derivation did not anticipate.
# Load all methodology notesfor f in ops/methodology/*.md; doecho"=== $f ==="cat"$f"echo""done
When methodology notes address the user's question:
Cite them alongside research claims: "Your system's methodology notes say [X], which aligns with the research claim [[Y]]"
If methodology notes contradict research claims, flag the tension: "Your methodology note says [X], but the research suggests [Y] — this may be worth investigating with /{DOMAIN:rethink}"
Methodology notes about system behavior are more authoritative for "how does MY system work" questions than the general research graph
When to prioritize methodology over research:
Questions about "why does my system do X" — methodology notes capture the specific rationale
Questions about behavioral patterns — methodology notes capture system-specific learnings
Questions about configuration — methodology notes may document post-init changes not in derivation.md
When to prioritize research over methodology:
Questions about "why is X a good idea in general" — research claims provide the theoretical foundation
Questions about alternative approaches — the research graph covers options the system did not choose
Questions about methodology comparisons — research claims compare traditions systematically
Answer Synthesis
Structure
Every answer follows this structure:
Direct answer — Lead with the answer, not the search process. Do not say "I searched for X and found Y." Say what the answer IS.
Research backing — What specific claims support this answer. Cite by title: "According to [[claim title]]..."
Practical implications — What this means for the user's specific situation. Use their domain vocabulary if available from derivation.
Tensions or caveats — Any unresolved conflicts, limitations, or situations where the answer might not hold. The research has genuine tensions — share them honestly.
Further exploration — Related claims or topics the user might want to explore. These are departure points, not assignments.
Sources consulted — Briefly note which knowledge layers were used: "Research: [N] claims consulted. Local methodology: [M] notes consulted." When local methodology was relevant, name the specific note: "Your methodology note [[title]] informed the [specific part] of this answer."
Quality Standards
Ground answers in specific claims, not general knowledge. The knowledge base exists so answers are evidence-based. If you answer from general knowledge without consulting the graph, you are bypassing the tool.
Acknowledge gaps honestly. When the research does not cover something, say so. "The current research graph doesn't have claims about X" is a legitimate answer. Do not fabricate coverage.
Distinguish certainty levels. Some claims are well-established with multiple supporting claims. Others are preliminary observations or research directions. The confidence field in claim frontmatter signals this:
No confidence field — standard established claim
confidence: speculative — early-stage observation, not fully evaluated
confidence: emerging — promising but needs more support
confidence: supported — well-evidenced claim
confidence: established — foundational, widely supported
status: archived — superseded or dissolved claim
Be opinionated. The research has positions. Share them. "The research strongly suggests X because..." is better than "some sources say X, others say Y." If there IS genuine disagreement, present it as a tension, not as false balance.
Translate to user context. When the user has a derivation, apply findings to their system. Generic advice is less useful than specific application. "In your therapy vault, this means..." is better than "in general, this means..."
Worked Examples
Example 1: WHY Question
Question: "Why does my system use atomic notes instead of longer documents?"
Classification: WHY -> Primary: Research Graph. Secondary: Reference (dimension-claim-map).
Search:
mcp__qmd__deep_search query="atomic notes vs compound documents granularity" collection="methodology" limit=8
Read ops/derivation.md — check user's granularity position
Answer:
Your system uses atomic granularity because your conversation signaled "precise claims from papers." The research shows that atomic notes enable independent linking and recombination — according to [[atomic notes maximize recombinable surface area]], each note can be linked from any context without dragging unrelated content along. Your processing pipeline (extract -> reflect -> reweave -> verify) specifically requires atomic granularity to maintain the link fabric, because [[verify phase checks link density per note]] and compound documents would inflate link counts artificially.
The trade-off: atomic notes create more files and require denser navigation structures. [[flat organization requires semantic search at scale]] explains why your system includes semantic search — without it, finding notes in a flat atomic structure becomes impractical beyond ~50 notes.
Tension: [[composability and context compete at the note level]] — making notes small enough to link cleanly sometimes makes them too small to carry their own argument. Your system handles this through the description field and topic map context phrases.
Example 2: HOW Question
Question: "How do I process a large research paper?"
Classification: HOW -> Primary: Guidance Docs. Secondary: Research Graph.
mcp__qmd__deep_search query="context degradation large documents extraction" collection="methodology" limit=5
Answer:
For sources over 2500 lines, chunk into segments of 350-1200 lines and process each chunk with fresh context. [Guidance: pipeline processing workflow] explains the chunking strategy in detail.
The reason is [[context quality degrades as conversation grows]] — your extraction quality in the later sections of a 3000-line document will be measurably worse than in the early sections. Fresh context per chunk ensures each section gets your best attention.
Practical steps:
Run /seed to create the extraction task
/reduce will automatically detect the source size and plan chunks
Each chunk extracts independently with a running duplicate list across chunks
The final extraction report covers all chunks combined
Your config has processing.depth: standard, which means chunks process sequentially in the current session. If you switch to deep, each chunk would get its own isolated context (maximum quality, but slower).
Example 3: WHAT Question
Question: "What would a cooking recipe vault look like?"
Classification: WHAT -> Primary: Domain Examples. Secondary: Guidance Docs.
Read closest domain examples for structural inspiration
Answer:
[Answer synthesized from examples, showing concrete folder structure, note examples, topic map examples, and vocabulary choices specific to a culinary domain. References the closest existing domain example for structural patterns.]
Anti-Patterns
What NOT to Do
Anti-Pattern
Why It Fails
Instead
Answer without searching
Bypasses the knowledge base entirely
Always search, even for "obvious" questions
List claims without synthesis
Dumps search results, forces user to connect dots
Weave claims into a coherent argument
Search only one tier
Misses HOW when answering WHY, or vice versa
Check if secondary tier strengthens the answer
Ignore user's derivation
Generic advice when specific is available
Read ops/derivation.md for user context
Use universal vocabulary
Says "notes" when their system says "reflections"
Apply vocabulary transforms
Fabricate claim citations
Cites claims that do not exist in the graph
Only cite claims you actually read
Skip the claim-map
Searches blindly without routing
Read claim-map first for topic orientation
Present false balance
"Some say X, others say Y" when research has a clear position
Be opinionated — share the research's position
The Honesty Standard
If the knowledge base genuinely does not cover a topic:
Say so explicitly: "The current research graph doesn't have claims about X."
Offer what IS available: "The closest related research is about Y, which suggests..."
Flag it as a gap: "This might be worth investigating as a research direction."
Do NOT extrapolate wildly from tangentially related claims. An honest "I don't know, but here's what's adjacent" is more valuable than a fabricated answer.
Domain-Aware Answering
When the user's question involves their specific system (not abstract methodology):
Step 1: Read Their Derivation
Check for ops/derivation.md to understand:
Their dimension positions (granularity, organization, processing depth, etc.)
Their vocabulary choices (what are "notes" called in their domain?)
Their tradition mapping (which methodology preset, if any?)
Their personality settings (formal/warm, clinical/conversational)
Their constraint profile (which interaction constraints are active?)
Step 2: Apply Domain Vocabulary
Use ${CLAUDE_PLUGIN_ROOT}/reference/vocabulary-transforms.md to translate universal terms into their domain language. This is not cosmetic — it is about making the answer native to their system.
Universal Term
Therapy Domain
PM Domain
Research Domain
notes
reflections
decisions
claims
topic map
theme map
project map
MOC
reduce
surface
extract
reduce
reflect
connect
link
reflect
inbox
journal
intake
inbox
Step 3: Check Interaction Constraints
Reference ${CLAUDE_PLUGIN_ROOT}/reference/interaction-constraints.md to understand whether their configuration creates specific pressures relevant to the question. Some dimension combinations create tensions that affect the answer:
High granularity + flat organization = needs strong semantic search
Permissive selectivity + deep processing = high volume, slower throughput
Self space enabled + warm personality = rich identity layer
Step 4: Cite Dimension-Specific Research
Use ${CLAUDE_PLUGIN_ROOT}/reference/dimension-claim-map.md to ground answers in the specific claims that inform their configuration choices. This makes the answer traceable: "Your system does X because claim Y supports it for your configuration."
