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* feat: improve memory prioritization — user preferences over procedural knowledge Inspired by OpenAI Codex's memory prompt improvements (openai/codex#14493) which focus memory writes on user preferences and recurring patterns rather than procedural task details. Key insight: 'Optimize for reducing future user steering — the most valuable memory prevents the user from having to repeat themselves.' Changes: - MEMORY_GUIDANCE (prompt_builder.py): added prioritization hierarchy and the core principle about reducing user steering - MEMORY_SCHEMA (memory_tool.py): reordered WHEN TO SAVE list to put corrections first, added explicit PRIORITY guidance - Memory nudge (run_agent.py): now asks specifically about preferences, corrections, and workflow patterns instead of generic 'anything' - Memory flush (run_agent.py): now instructs to prioritize user preferences and corrections over task-specific details * feat: more aggressive skill creation and update prompting Press harder on skill updates — the agent should proactively patch skills when it encounters issues during use, not wait to be asked. Changes: - SKILLS_GUIDANCE: 'consider saving' → 'save'; added explicit instruction to patch skills immediately when found outdated/wrong - Skills header: added instruction to update loaded skills before finishing if they had missing steps or wrong commands - Skill nudge: more assertive ('save the approach' not 'consider saving'), now also prompts for updating existing skills used in the task - Skill nudge interval: lowered default from 15 to 10 iterations - skill_manage schema: added 'patch it immediately' to update triggers |
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| .. | ||
| __init__.py | ||
| anthropic_adapter.py | ||
| auxiliary_client.py | ||
| context_compressor.py | ||
| display.py | ||
| insights.py | ||
| model_metadata.py | ||
| prompt_builder.py | ||
| prompt_caching.py | ||
| redact.py | ||
| skill_commands.py | ||
| trajectory.py | ||
| usage_pricing.py | ||