smithery.ai

glean

Surface emergent patterns and insights from the AI-ready vault memory system. Use periodically to discover connections between memories, identify recurring themes, and generate meta-insights that aren't obvious from individual memories.

First seen Mar 23, 2026

Installation

$ npx skills add https://smithery.ai

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

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Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 3,173 B
  • docs SUMMARY.md 249 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 2 installs

SKILL.md

Glean

Surface emergent patterns and insights from memory.

Purpose

Individual memories capture discrete information. Gleaning reveals:

  • Connections between seemingly unrelated memories
  • Recurring themes or patterns
  • Evolution of thinking over time
  • Gaps or contradictions in stored knowledge
  • Meta-insights that emerge from the collection

Workflow

  1. Load memories for analysis

- If $ARGUMENTS provided, focus on that domain - Otherwise, analyze across all memory types - Prioritize recent and high-importance memories

  1. Identify patterns

- Recurring concepts across memories - Related decisions or approaches - Evolving understanding of topics - Clusters of related information

  1. Surface insights

- What themes emerge? - What connections weren't obvious? - What contradictions exist? - What gaps should be filled?

  1. Optionally store meta-memories

- Patterns discovered can become new memories - Type: Usually Strategic or Procedural

Analysis Dimensions

Dimension Questions
Temporal How has thinking evolved? What changed?
Conceptual What themes recur? What clusters form?
Contradictions Where do memories conflict?
Gaps What's missing? What should be captured?
Connections What links across domains?

Parameters

  • $ARGUMENTS (optional): Domain or topic to focus analysis on

Default Paths

Analyzes memories at: Areas/AI/Memory/

Related Skills

  • /recall {terms} - Search for specific memories
  • /remember - Store patterns discovered as new memories
  • /reflect - Session-end memory consolidation

Output Format

Patterns Observed:

  • List of 3-5 patterns or themes

Connections Found:

  • Links between memories that weren't explicitly made

Insights:

  • 1-3 meta-observations about the memory collection

Recommendations:

  • Suggested memories to create, update, or consolidate

Example

User: /glean ai-ready-vault

Response: "Gleaning patterns from AI Ready Vault memories...

Patterns Observed:

  1. Composability is a recurring theme—skills, tiers, teaching approach all emphasize modularity
  2. 'Let the work speak' appears in both personal branding and product design decisions
  3. Education-first positioning across all strategic decisions

Connections Found:

  • Teaching pattern (show→problem→conceptual) mirrors the product evolution (demo→pain→solution)
  • Skills library concept directly enables the 'incremental adoption' goal

Insights:

  • The project philosophy is internally consistent—same principles apply at multiple levels
  • Strong bias toward empowering users vs. prescribing solutions

Recommendations:

  • Consider creating a 'design principles' memory that captures the recurring themes
  • The composability pattern could become a procedural memory for future projects"