#!/usr/bin/env python3 """ s10: System Prompt — Runtime prompt assembly with caching. Run: python s10_system_prompt/code.py Need: pip install anthropic python-dotenv + .env with ANTHROPIC_API_KEY Changes from s09: - PROMPT_SECTIONS: topic-keyed dict of prompt fragments - assemble_system_prompt(context): select + join sections by real state - get_system_prompt(context): deterministic cache via json.dumps - agent_loop uses get_system_prompt(context) instead of hardcoded SYSTEM Memory section loads when .memory/MEMORY.md exists (real state, not keywords). """ import os, subprocess, json from pathlib import Path try: import readline readline.parse_and_bind('set bind-tty-special-chars off') except ImportError: pass from anthropic import Anthropic from dotenv import load_dotenv load_dotenv(override=True) if os.getenv("ANTHROPIC_BASE_URL"): os.environ.pop("ANTHROPIC_AUTH_TOKEN", None) WORKDIR = Path.cwd() MEMORY_DIR = WORKDIR / ".memory" MEMORY_INDEX = MEMORY_DIR / "MEMORY.md" client = Anthropic(base_url=os.getenv("ANTHROPIC_BASE_URL")) MODEL = os.environ["MODEL_ID"] # ── Prompt Sections ── PROMPT_SECTIONS = { "identity": "You are a coding agent. Act, don't explain.", "tools": "Available tools: bash, read_file, write_file.", "workspace": f"Working directory: {WORKDIR}", "memory": "Relevant memories are injected below when available.", } def assemble_system_prompt(context: dict) -> str: """Select and join prompt sections based on current context.""" sections = [] # Always loaded — identity, tools, workspace sections.append(PROMPT_SECTIONS["identity"]) sections.append(PROMPT_SECTIONS["tools"]) sections.append(PROMPT_SECTIONS["workspace"]) # Conditional — memory loaded when MEMORY.md exists and has content memories = context.get("memories", "") if memories: sections.append(f"Relevant memories:\n{memories}") return "\n\n".join(sections) _last_context_key = None _last_prompt = None def get_system_prompt(context: dict) -> str: """Cache wrapper — reassemble only when context changes. Uses json.dumps for deterministic serialization, not Python's hash() which has process randomization and fails on nested dicts/lists. This cache only avoids redundant string assembly within a process. Real Claude Code additionally protects API-level prompt cache via stable section ordering and SYSTEM_PROMPT_DYNAMIC_BOUNDARY. """ global _last_context_key, _last_prompt key = json.dumps(context, sort_keys=True, ensure_ascii=False, default=str) if key == _last_context_key and _last_prompt: print(" \033[90m[cache hit] system prompt unchanged\033[0m") return _last_prompt _last_context_key = key _last_prompt = assemble_system_prompt(context) loaded = ["identity", "tools", "workspace"] if context.get("memories"): loaded.append("memory") print(f" \033[32m[assembled] sections: {', '.join(loaded)}\033[0m") return _last_prompt # ── Tools ── def safe_path(p: str) -> Path: path = (WORKDIR / p).resolve() if not path.is_relative_to(WORKDIR): raise ValueError(f"Path escapes workspace: {p}") return path def run_bash(command: str) -> str: try: r = subprocess.run(command, shell=True, cwd=WORKDIR, capture_output=True, text=True, timeout=120) out = (r.stdout + r.stderr).strip() return out[:50000] if out else "(no output)" except subprocess.TimeoutExpired: return "Error: Timeout (120s)" def run_read(path: str, limit: int | None = None) -> str: try: lines = safe_path(path).read_text().splitlines() if limit and limit < len(lines): lines = lines[:limit] + [f"... ({len(lines) - limit} more lines)"] return "\n".join(lines) except Exception as e: return f"Error: {e}" def run_write(path: str, content: str) -> str: try: file_path = safe_path(path) file_path.parent.mkdir(parents=True, exist_ok=True) file_path.write_text(content) return f"Wrote {len(content)} bytes to {path}" except Exception as e: return f"Error: {e}" TOOLS = [ {"name": "bash", "description": "Run a shell command.", "input_schema": {"type": "object", "properties": {"command": {"type": "string"}}, "required": ["command"]}}, {"name": "read_file", "description": "Read file contents.", "input_schema": {"type": "object", "properties": {"path": {"type": "string"}, "limit": {"type": "integer"}}, "required": ["path"]}}, {"name": "write_file", "description": "Write content to a file.", "input_schema": {"type": "object", "properties": {"path": {"type": "string"}, "content": {"type": "string"}}, "required": ["path", "content"]}}, ] TOOL_HANDLERS = {"bash": run_bash, "read_file": run_read, "write_file": run_write} # ── Context ── def update_context(context: dict, messages: list) -> dict: """Derive context from real state: which tools exist, whether memory files exist.""" memories = "" if MEMORY_INDEX.exists(): content = MEMORY_INDEX.read_text().strip() if content: memories = content return { "enabled_tools": list(TOOL_HANDLERS.keys()), "workspace": str(WORKDIR), "memories": memories, } # ── Agent Loop ── def agent_loop(messages: list, context: dict): """Main loop — uses assembled system prompt instead of hardcoded SYSTEM.""" system = get_system_prompt(context) while True: response = client.messages.create( model=MODEL, system=system, messages=messages, tools=TOOLS, max_tokens=8000) messages.append({"role": "assistant", "content": response.content}) if response.stop_reason != "tool_use": return results = [] for block in response.content: if block.type != "tool_use": continue print(f"\033[36m> {block.name}\033[0m") handler = TOOL_HANDLERS.get(block.name) output = handler(**block.input) if handler else f"Unknown: {block.name}" print(str(output)[:200]) results.append({"type": "tool_result", "tool_use_id": block.id, "content": output}) messages.append({"role": "user", "content": results}) # Re-evaluate context and prompt after each tool round context = update_context(context, messages) system = get_system_prompt(context) if __name__ == "__main__": print("s10: system prompt — runtime assembly") print("Enter a question, press Enter to send. Type q to quit.\n") history = [] context = update_context({}, []) while True: try: query = input("\033[36ms10 >> \033[0m") except (EOFError, KeyboardInterrupt): break if query.strip().lower() in ("q", "exit", ""): break history.append({"role": "user", "content": query}) agent_loop(history, context) context = update_context(context, history) for block in history[-1]["content"]: if getattr(block, "type", None) == "text": print(block.text) print()