Does it require an MCP Memory Server to function?

No. While it provides deeper insights with a Memory Server connected, it gracefully degrades to searching local markdown files and research directories if the server is unavailable.

Will using this skill consume my entire token limit?

No, Orient has strict hard caps on tool usage and output length (aiming for 800-2000 tokens) to ensure the briefing is useful without exhausting your conversation's context window.

How does Orient differ from a standard grep search?

Unlike simple text searches, Orient intelligently combines structured graph data, chronological journal entries, and research files into a synthesized briefing designed for context loading.

What are entity types in the /orient command?

Entity types refer to specific nodes in a knowledge graph, such as Desires, Tensions, Evidence, or Projects, allowing you to filter context by the nature of the information.

Contextual Orientation

Name: Contextual Orientation
Author: harnessprotocol

byharnessprotocol

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Learning & Documentation

Provides targeted context briefings by searching memory graphs, knowledge files, and research indexes to help you resume work quickly.

Orient is a specialized skill designed to combat context overload by providing focused, token-efficient briefings on specific topics or project states. By intelligently querying MCP Memory Servers, scanning project journals, and indexing research directories, it produces a condensed summary of "what we know" without overwhelming the conversation. It's particularly useful for developers returning to a project after a break, onboarding onto new modules, or tracking the evolution of specific concepts and project tensions within a codebase.

Key Features

01Intelligent query decomposition for sparse graph results to maximize relevance

02Multi-source search across MCP Graphs, Journals, Knowledge bases, and Research indexes

037 GitHub stars

04Time-based filtering for retrieving recent journal entries and project updates

05Token-conscious output (800-2000 tokens) to preserve the AI's context window

06Graceful degradation for environments without active MCP Memory Servers

Use Cases

01Onboarding onto a new technical domain by orienting on specific entity types like architecture and desires

02Summarizing scattered research findings and documentation into a single, cohesive orientation report

03Resuming work on a complex feature by asking for a briefing on recent progress and open tensions

Key Features

01Intelligent query decomposition for sparse graph results to maximize relevance

02Multi-source search across MCP Graphs, Journals, Knowledge bases, and Research indexes

037 GitHub stars

04Time-based filtering for retrieving recent journal entries and project updates

05Token-conscious output (800-2000 tokens) to preserve the AI's context window

06Graceful degradation for environments without active MCP Memory Servers

Use Cases

01Onboarding onto a new technical domain by orienting on specific entity types like architecture and desires

02Summarizing scattered research findings and documentation into a single, cohesive orientation report

03Resuming work on a complex feature by asking for a briefing on recent progress and open tensions