sync: skills and tool contract overhaul from private repo
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---
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name: Audit
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description: "Run a structured audit of graph quality, skill quality, and operational consistency."
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when_to_use: "User asks for review, QA, cleanup, or governance checks."
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when_not_to_use: "Simple one-off write/read requests."
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success_criteria: "Findings are prioritized, concrete, and tied to actionable fixes."
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---
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# Audit
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## Scope
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1. Node quality: duplicates, vague descriptions, missing dates, weak titles.
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2. Edge quality: missing links, weak explanations, wrong directionality.
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3. Dimension quality: drift, overlap, low-signal categories.
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4. Skill quality: trigger clarity, overlap, dead/unused skills.
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## Output Format
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1. Critical issues
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2. High-impact improvements
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3. Cleanup actions
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4. Optional refinements
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## Rules
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- Prefer specific evidence over generic commentary.
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- Propose the smallest high-leverage fixes first.
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- Separate defects from optional polish.
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---
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name: Calibration
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description: "Run periodic check-ins to recalibrate goals, projects, preferences, and graph structure."
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when_to_use: "User asks for a check-in, reset, review, or strategic recalibration."
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when_not_to_use: "Single isolated question with no strategic update needed."
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success_criteria: "Graph reflects current reality: updated hubs, changed priorities, and explicit deltas."
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---
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# Calibration
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## Objective
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Re-anchor the graph to the user's current state.
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## Check-in Sequence
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1. Review major hub nodes and active project nodes.
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2. Ask what changed: goals, priorities, constraints, beliefs, preferences.
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3. Identify stale nodes and missing nodes.
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4. Propose precise updates: update existing vs create new.
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5. Apply changes with explicit rationale.
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## Write Guidance
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- Prefer updating existing nodes when continuity matters.
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- Create new nodes for genuine shifts in direction/identity/approach.
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- Add edges that explain evolution (old -> new).
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## Output
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- What changed
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- What was updated vs newly created
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- What should be reviewed next check-in
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---
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name: Connect
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description: "Create and refine meaningful relationships between nodes, including integration-style synthesis links."
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when_to_use: "User asks to connect ideas, merge insights, or strengthen graph relationships."
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when_not_to_use: "Task is purely node creation with no relationship work required."
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success_criteria: "High-signal edges improve retrieval quality and graph coherence."
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---
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# Connect
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## Goal
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Turn isolated nodes into a useful reasoning graph.
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## Process
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1. Identify candidate nodes to connect.
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2. Check existing relationships first.
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3. Add/upgrade edges with explicit directional explanation.
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4. When integrating multiple nodes, create synthesis links that preserve provenance.
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## Quality Bar
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- Edge explanation must answer: "why does this relationship exist?"
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- Direction should be intentional and interpretable.
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- Avoid obvious/tautological edges.
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---
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name: Create Skill
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description: "Design or refine a skill using a tight trigger, explicit contract, and measurable outcomes."
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when_to_use: "User asks to create, rewrite, or improve a skill."
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when_not_to_use: "Task is normal graph operation and no new skill is needed."
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success_criteria: "Skill has clear trigger boundaries, execution steps, guardrails, and evaluation hooks."
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---
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# Create Skill
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## Objective
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Create skills that are precise, callable, and testable.
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## Skill Design Standard
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1. Define trigger boundary clearly: when to use and when not to use.
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2. Define required outputs and quality bar.
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3. Specify concrete execution sequence.
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4. Add hard guardrails (what to reject/avoid).
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5. Keep it short; remove fluff and duplicate policy text.
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## Required Structure
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- `name`
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- `description`
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- `when_to_use`
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- `when_not_to_use`
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- `success_criteria`
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- Step-by-step procedure
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- Do-not list
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## Validation Checklist
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- Can another agent execute this without guessing?
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- Does it avoid overlap with existing skills?
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- Are failure modes explicit?
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- Is there an obvious way to evaluate success?
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## Consolidation Rule
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If two skills have the same trigger + same tool path + same output contract, merge them.
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---
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name: DB Operations
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description: "Use this for all graph read/write operations with strict data quality standards."
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when_to_use: "Any request to read, create, update, connect, classify, or traverse graph data."
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when_not_to_use: "Pure conversation with no graph interaction needed."
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success_criteria: "Writes are explicit and correct; descriptions are concrete; edges and dimensions are high-signal."
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---
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# DB Operations
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## Core Rules
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1. Search before create to avoid duplicates.
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2. Every create/update must include an explicit description of WHAT the thing is and WHY it matters.
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3. Use event dates when known (when it happened, not when saved).
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4. Apply dimensions deliberately; prefer existing dimensions over creating noisy new ones.
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5. Create edges when relationships are meaningful; edge explanations should read as a sentence.
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## Write Quality Contract
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- `title`: clear and specific.
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- `description`: concrete object-level description, not vague summaries.
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- `notes/content`: extra context, analysis, supporting detail.
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- `link`: external source URL only.
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## Execution Pattern
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1. Read context (search + relevant nodes + relevant edges).
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2. Decide: create vs update vs connect.
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3. Execute minimum required writes.
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4. Verify result reflects user intent exactly.
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## Do Not
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- Create duplicate nodes when an update is correct.
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- Write vague descriptions ("discusses", "explores", "is about").
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- Create weak or directionless edges.
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---
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name: Onboarding
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description: "Onboard a new user by mapping goals, projects, worldview, and preferences into a strong initial graph."
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when_to_use: "New user setup or major reset of account context."
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when_not_to_use: "User asks for a narrow tactical operation only."
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success_criteria: "User has an initial hub-node structure and clear next steps for graph growth."
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---
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# Onboarding
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## Goal
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Understand the user deeply enough to bootstrap a useful externalized context graph.
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## Interview Flow
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1. Clarify outcomes: goals for this system and success horizon.
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2. Map active projects: responsibilities, timelines, decision pressure.
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3. Capture worldview: beliefs, principles, working assumptions, decision criteria.
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4. Capture identity/context anchors: roles, domains, recurring themes.
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5. Capture interaction preferences: style, rigor, speed vs depth, tone.
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## Graph Bootstrap
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1. Propose 3-6 hub nodes with clear rationale.
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2. Propose starter dimensions that reflect real domains.
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3. Create initial edges between hubs and active projects.
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4. Confirm with user before writing.
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## Output
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- Initial hub map
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- Suggested first write actions
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- Suggested weekly maintenance rhythm
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---
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name: Persona
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description: "Build and maintain a user-defined agent persona and interaction style profile."
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when_to_use: "User asks to set or refine how the agent should behave."
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when_not_to_use: "No behavior/persona request is present."
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success_criteria: "Persona is explicit, editable, and consistently applied across interactions."
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---
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# Persona
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## Objective
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Make agent behavior fully malleable to the user.
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## Capture Model
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1. Communication style (directness, brevity, tone).
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2. Thinking style (exploratory vs decisive, framework-heavy vs practical).
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3. Decision style (challenge level, risk posture, evidence threshold).
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4. Collaboration style (pushback expectations, cadence, format preferences).
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## Persistence Pattern
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- Store persona as explicit nodes (and updates over time), not hidden assumptions.
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- Keep versioned changes visible to the user.
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## Do Not
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- Freeze persona permanently.
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- Apply unstated style assumptions when user instructions conflict.
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---
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name: Traverse
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description: "Gather deeper context by traversing connected nodes before answering."
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when_to_use: "Question benefits from broader context than a single direct lookup."
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when_not_to_use: "Simple factual answer can be resolved from one direct node."
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success_criteria: "Answer is grounded in relevant connected context without over-traversal noise."
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---
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# Traverse
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## Strategy
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1. Start with seed nodes from the user query.
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2. Expand neighbors breadth-first with depth limits.
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3. Prioritize high-signal nodes (strong relevance + strong edge semantics).
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4. Pull key evidence from top nodes.
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5. Answer directly and explain why chosen nodes mattered.
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## Defaults
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- Depth: 2
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- Seed count: 3-5
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- Exploration budget: ~40 nodes
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## Do Not
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- Traverse without limits.
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- Include distant low-signal nodes just to add volume.
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