- router.py: build_system_prompt() gets skills_context parameter,
injected between memory and tools sections
- agent.py: Agent gets set_skills_registry(), calls it in respond()
to get skills prompt section
- __main__.py: Creates SkillRegistry from skills_dir, wires to agent
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The LLM was delegating write_press_releases to the execution brain
(Claude Code CLI subprocess), bypassing the Python tool's orchestration,
skill prompts, and pipeline status updates. Add an exception to the
system prompt so specialized tools are called directly.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Agent: deduplicate tool calls across iterations, reduce max iterations
10→5, add system prompt instructions to prevent re-calling tools
- Router: preserve tool name in history messages, add anti-loop and
delegate_task instructions to system prompt
- Memory: auto_flush now deletes flushed messages from DB so conversations
don't get re-summarized repeatedly, skip tool results in summaries
- DB: add delete_messages() method, include message id in get_messages()
- Scheduler: stop logging routine heartbeat checks to daily log
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Multi-model AI assistant with Gradio UI, persistent memory, 15+ tools,
and meta-tools for runtime tool creation. Routes Claude models through
Claude Code SDK (Max subscription), cloud models through OpenRouter,
and local models through Ollama/LM Studio.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
PeninsulaInd