What is Agent Skill FContext?

Agent Skill FContext (fcontext) is a powerful framework designed to solve the critical problem of context loss and knowledge fragmentation when working with modern AI coding agents. Traditional AI sessions are ephemeral; agents forget everything between interactions, across different agent platforms (like Copilot, Claude, or Cursor), and lack mechanisms for team-wide knowledge sharing. FContext captures, structures, and persists the accumulated knowledge, project history, and domain expertise derived from AI interactions, ensuring that every new session starts with a rich, relevant understanding of the ongoing work.

This persistence transforms AI usage from a series of disconnected prompts into a continuous, evolving development process. For individuals, it means your accumulated expertise is never lost, making your AI assistant smarter with every interaction. For teams and enterprises, fcontext enables true knowledge synchronization, ensuring all team members' agents operate with a shared, up-to-date understanding of requirements, architecture, and domain specifics, leading to consistent, auditable, and industrial-grade AI output.

Key Features

FContext is built around robust mechanisms for context management and secure, local data storage:

• Cross-Session Memory: AI accumulates knowledge topics across sessions, stored persistently in .fcontext/_topics/, ensuring conclusions and learnings survive restarts.
• Cross-Agent Portability: Seamlessly switch between mainstream AI coding agents (Copilot, Claude, Cursor, Trae, OpenCode, OpenClaw) without losing continuity, using agent-native configuration files.
• Experience Packs: Allows users to export and import structured domain knowledge, facilitating instant onboarding and knowledge transfer across different projects or teams.
• Document Indexing: Automatically indexes common document types (PDF, DOCX, XLSX, PPTX, Keynote, EPUB) and converts them to Markdown for AI consumption, storing them in .fcontext/_cache/.
• Living Project Summary: Maintains a dynamic, AI-updated _README.md file (.fcontext/_README.md) that the agent reads first in every new session, providing immediate project context.
• Requirements Management: Tracks the evolution history of stories, tasks, and bugs from initial documentation to final delivery, stored in .fcontext/_requirements/.
• Offline & Secure by Design: All context data is stored locally within the project's .fcontext/ directory. There is no cloud dependency, no API key storage, and no telemetry, ensuring compliance and data security.

How to Use Agent Skill FContext

Getting started with fcontext involves a simple, four-step initialization process within your project directory:

1. Installation: Install the tool via PyPI using Python 3.9+:

pip install fcontext

2.  **Initialization:** Navigate to your project root and initialize the fcontext structure:
    ```bash
cd your-project
fcontext init

This creates the necessary `.fcontext/` directory structure.

3. Agent Activation: Enable context continuity for your preferred AI agent. For example, to enable support for GitHub Copilot: ```bash fcontext enable copilot

    (Supported agents include `claude`, `cursor`, `trae`, `opencode`, and `openclaw`.)
4.  **Indexing Content:** Index relevant project documents (like specifications or documentation folders) so the AI can reference them:
    ```bash
fcontext index docs/

Once initialized, your activated AI agent will automatically read the persisted context, project map, and indexed documents at the start of every new session, ensuring continuous workflow.

Use Cases

FContext is invaluable in scenarios where context retention and shared knowledge are paramount for efficiency and quality:

1. Picking Up Where You Left Off: A developer stops work on a complex debugging session late in the day. The next morning, instead of spending 30 minutes re-establishing the state, fcontext ensures the agent immediately recalls the exact variables, error logs, and hypotheses from the previous session via the _topics/ store.
2. Team Onboarding and Handoffs: A new engineer joins a project. By importing the team's established Experience Pack, their AI agent instantly understands the established coding patterns, domain-specific terminology, and architectural decisions, drastically reducing ramp-up time.
3. Multi-Agent Workflow Transition: A user prototypes a feature using Claude for high-level planning, then switches to Cursor for detailed, in-file refactoring. FContext ensures that Cursor reads the plan generated by Claude without requiring manual copy-pasting of context.
4. Auditable Compliance Environments: In regulated industries, fcontext tracks the full evolution history of a requirement (from a Jira ticket reference in _requirements/ to the final code implementation), providing an auditable, traceable link for compliance checks.
5. Large Codebase Navigation: For massive repositories, the auto-generated Workspace Map (_workspace.map) allows agents to quickly understand file relationships and dependencies, preventing the agent from getting lost or proposing irrelevant changes.

FAQ

Q: Is my data sent to the cloud or any third-party servers? A: No. FContext is designed for security and compliance. All context data, including indexed documents and session history, is stored locally within the project's .fcontext/ directory. No API keys or telemetry are transmitted.

Q: Which AI agents are officially supported? A: FContext supports major coding agents including GitHub Copilot, Claude Code, Cursor, Trae, OpenCode, and OpenClaw. Support is managed via agent-specific configuration files located in the .fcontext/ directory.

Q: What happens if I switch projects? A: Context is project-specific. When you move to a new project directory, you must run fcontext init again if it hasn't been initialized. You can then use Experience Packs to import relevant domain knowledge from previous projects if necessary.

Q: How does fcontext handle large numbers of documents? A: FContext indexes documents (PDF, DOCX, etc.) and converts them into structured Markdown within the cache. It intelligently manages this indexed knowledge, ensuring the AI can retrieve relevant snippets without overloading the context window during standard session reads.

Q: Can I share my accumulated knowledge with a teammate? A: Yes. This is achieved through Experience Packs. You can export a curated set of knowledge or requirements from your .fcontext/ directory and share it with your teammate, who can then import it into their local environment, ensuring team-wide context synchronization.