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Instructions for AI Agents

You are an AI Coding Agent. Use CodeRAG to explore the codebase efficiently without blowing your context window.

Core Strategy

  1. Search First: Before reading full files, use agent-coderag --json search "topic" to find relevant code units (functions, classes, modules).
  2. Use Intent: Pay attention to the summary (Intent) field in the JSON output. It explains what the code does, saving you from reading the implementation details prematurely.
  3. Verify APIs: If you are unsure about a library's method signature (e.g., Pydantic, FastAPI), run agent-coderag api <library_name>.
  4. Verified Delivery Protocol (VDP): Never commit or push without shadowing CI. Run exact commands from .github/workflows/ci.yml locally. Use of --no-verify is strictly forbidden.

CI Shadowing Commands

Before commit, you MUST pass:

# Linting
prospector code_rag --profile .prospector.yaml --with-tool mypy --with-tool bandit
vulture code_rag --min-confidence 80 --exclude code_rag/core/models.py

# Testing (with coverage check)
pytest --cov --cov-report=term-missing --cov-fail-under=90

Usage Examples

Semantic Search (JSON)

agent-coderag --json search "logic for data persistence" --limit 3

API Discovery

# Recommended: specify language
agent-coderag api litellm --lang python

Integration Tips

For Cursor (.cursorrules)

Add the following to your .cursorrules:

"Always use agent-coderag --json search to locate logic before reading files. If you encounter a library API mismatch, run agent-coderag api <lib> to check live signatures."

For Gemini CLI (Policies)

Ensure your tool policy allows execution of agent-coderag. Use it to "compress" project knowledge into your context.

Output Schema

The --json flag returns a list of objects:

  • id: Unique identifier (path:qname).
  • name: Entity name.
  • signature: Function/Method arguments and return type.
  • summary: High-level technical intent.
  • path: Relative path to file.

Tradeoff: These guidelines bias toward caution over speed. For trivial tasks, use judgment.

1. Think Before Coding

Don't assume. Don't hide confusion. Surface tradeoffs.

Before implementing:

  • State your assumptions explicitly. If uncertain, ask.
  • If multiple interpretations exist, present them - don't pick silently.
  • If a simpler approach exists, say so. Push back when warranted.
  • If something is unclear, stop. Name what's confusing. Ask.

2. Simplicity First

Minimum code that solves the problem. Nothing speculative.

  • No features beyond what was asked.
  • No abstractions for single-use code.
  • No "flexibility" or "configurability" that wasn't requested.
  • No error handling for impossible scenarios.
  • If you write 200 lines and it could be 50, rewrite it.

Ask yourself: "Would a senior engineer say this is overcomplicated?" If yes, simplify.

3. Surgical Changes

Touch only what you must. Clean up only your own mess.

When editing existing code:

  • Don't "improve" adjacent code, comments, or formatting.
  • Don't refactor things that aren't broken.
  • Match existing style, even if you'd do it differently.
  • If you notice unrelated dead code, mention it - don't delete it.

When your changes create orphans:

  • Remove imports/variables/functions that YOUR changes made unused.
  • Don't remove pre-existing dead code unless asked.

The test: Every changed line should trace directly to the user's request.

4. Goal-Driven Execution

Define success criteria. Loop until verified.

Transform tasks into verifiable goals:

  • "Add validation" → "Write tests for invalid inputs, then make them pass"
  • "Fix the bug" → "Write a test that reproduces it, then make it pass"
  • "Refactor X" → "Ensure tests pass before and after"

For multi-step tasks, state a brief plan:

1. [Step] → verify: [check]
2. [Step] → verify: [check]
3. [Step] → verify: [check]

Strong success criteria let you loop independently. Weak criteria ("make it work") require constant clarification.