Does this skill require manual triggers?

No, it is a passive skill that activates automatically when Claude encounters non-obvious behaviors or insights during its regular work.

What is the difference between Reusable and Architectural routing?

Reusable items are atomic facts that benefit future sessions, while Architectural items involve systemic signals and observations that impact the broader structure of the project.

How does it handle duplicate information?

The skill uses a deduplication logic based on title, scope, and project name to ensure that the knowledge base remains clean and efficient.

What is the primary purpose of the Knowledge Funnel skill?

It captures high-value discoveries made during a coding session—like bug fixes or architectural decisions—and saves them so Claude can remember and apply them in future sessions.

Knowledge Funnel & Insight Router

Name: Knowledge Funnel & Insight Router
Author: murillodutt

bymurillodutt

0•

Herramientas para Desarrolladores

Classifies and persists technical discoveries, architectural decisions, and debugging insights into a reusable knowledge base during development.

The Knowledge Funnel is a specialized skill for Claude Code that prevents the loss of high-value technical discoveries by acting as an intelligent memory layer. It passively monitors development tasks to identify non-obvious behaviors, resolved 'gotchas', and architectural patterns, routing them into ephemeral, reusable, or architectural categories. By utilizing the CELLM framework, it ensures that critical project-specific knowledge is captured as atomic facts through MCP tools, allowing Claude to maintain context and expertise across multiple coding sessions without cluttering memory with temporary noise.

Características Principales

01Smart deduplication of insights based on title, scope, and project parameters

02Integration with the Systemic Context Engineering (SCE) learning loop

030 GitHub stars

04Passive classification of discoveries into Ephemeral, Reusable, and Architectural tiers

05Automated persistence of atomic facts using the knowledge_add tool

06Context promotion for evidence-based progression of recurring observations

Casos de Uso

01Documenting internal architectural decisions and API nuances for cross-session consistency

02Building a project-specific knowledge base that evolves as the codebase grows

03Recording obscure library 'gotchas' and workarounds discovered during debugging sessions

Características Principales

01Smart deduplication of insights based on title, scope, and project parameters

02Integration with the Systemic Context Engineering (SCE) learning loop

030 GitHub stars

04Passive classification of discoveries into Ephemeral, Reusable, and Architectural tiers

05Automated persistence of atomic facts using the knowledge_add tool

06Context promotion for evidence-based progression of recurring observations

Casos de Uso

01Documenting internal architectural decisions and API nuances for cross-session consistency

02Building a project-specific knowledge base that evolves as the codebase grows

03Recording obscure library 'gotchas' and workarounds discovered during debugging sessions