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Dynamic Code Injection (On-Demand File Fetch)

Problem

During an interactive coding session, a user or agent may need to inspect or modify files not originally loaded into the main context. Manually copying/pasting entire files into the prompt is:

  • Tedious and error-prone.
  • Wastes tokens on boilerplate (e.g., large config files).
  • Interrupts workflow momentum when switching between the editor and chat.

Solution

Allow on-demand file injection via special syntax (e.g., @filename or /load file) that automatically:

1. Fetches the requested file(s) from disk or version control. 2. Summarizes or extracts only the relevant portions (e.g., function bodies or specific line ranges) if the file is large. 3. Injects that snippet into the agent's current context, seamlessly extending its "memory" for the ongoing task.

Concretely: - A user types /load src/components/Button.js:lines 10–50 or @src/setup/db.js. - The agent's preprocessor intercepts this command, reads the specified file (or line range), and replaces the command with the file content (or trimmed snippet). - The rest of the prompt remains unchanged, so the agent can continue reasoning without restarting the conversation.

How to use it

  • Command Syntax Examples:
  • @path/to/file.ext → loads entire file if < 2,000 tokens; otherwise runs a heuristic summarizer.
  • /load path/to/file.ext:10-50 → loads exactly lines 10 through 50.

  • /summarize path/to/test_spec.py → runs a summary routine (e.g., extract docstrings + test names).

  • Implementation Steps:

  • Build a listener in your chat frontend or CLI that recognizes @ and /load tokens.
  • Map recognized tokens to file paths; verify permissions if outside project root.
  • Read file text, run a line-range parser or AST-based snippet extractor if needed.
  • Replace the token in the outgoing prompt with /// BEGIN <filename> …content… /// END <filename>.

  • Forward the augmented prompt to the LLM for inference.

  • Common Pitfalls:

  • Untrusted file paths: agent must validate that @../../../etc/passwd (for example) is disallowed.
  • Large injected files: if file > 4,096 tokens, automatically run a summarizer sub-routine to extract only function/method definitions.

Trade-offs

  • Pros:
  • Enables interactive exploration of code without leaving the chat environment.
  • Reduces human overhead: no manual copy/paste of code blocks.

  • Improves agent accuracy by ensuring the most relevant code is directly visible.

  • Cons/Considerations:

  • Requires the chat interface (or a proxy server) to have local file system access.
  • Potential security risk: if the agent can load arbitrary files, it could exfiltrate sensitive credentials unless carefully sandboxed.
  • Summarization heuristics may omit subtle context (e.g., private helper functions).

References

  • Adapted from "Dynamic Context Injection" patterns (e.g., at-mention in Claude Code) for general coding-agent use.
  • Common in AI-powered IDE plugins (e.g., GitHub Copilot X live code browsing).