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feat: refresh course through workflow and goal loops

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This commit is contained in:
Haoran
2026-07-30 19:14:04 +08:00
parent 2dd1852d9e
commit cb8fae1bdd
125 changed files with 10882 additions and 7661 deletions
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6
s10_system_prompt/code.py
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@@ -1,6 +1,6 @@
#!/usr/bin/env python3
"""
s10: System Prompt — Runtime prompt assembly with caching.
s10: Context Assembly — Runtime model-input assembly with caching.
Run: python s10_system_prompt/code.py
Need: pip install anthropic python-dotenv + .env with ANTHROPIC_API_KEY
@@ -55,7 +55,7 @@ def assemble_system_prompt(context: dict) -> str:
tools = ", ".join(context.get("enabled_tools", []))
if tools:
sections.append(f"Available tools: {tools}.")
sections.append(f"Working directory: {context.get("workspace", WORKDIR)}")
sections.append(f"Working directory: {context.get('workspace', WORKDIR)}")
# Conditional — memory loaded when MEMORY.md exists and has content
memories = context.get("memories", "")
@@ -199,7 +199,7 @@ def agent_loop(messages: list, context: dict):
if __name__ == "__main__":
print("s10: system prompt — runtime assembly")
print("s10: context assembly — runtime model input")
print("Enter a question, press Enter to send. Type q to quit.\n")
history = []
context = update_context({}, [])