القائمة
Store, retrieve, and evolve contextual and procedural knowledge using vector search, memory layers, and active forgetting.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Plan, reason, and verify across multiple levels using multi-step reasoning, tree-of-thought, and structured agent workflows.
Detect and repair security vulnerabilities or operational errors in the agent's own code and environment without manual intervention.
Generate and apply complete design systems from text descriptions or visual examples using AI and modern tooling.
Adapt user interfaces automatically based on device, viewport, user preferences, and contextual signals.
Build and query knowledge graphs automatically to capture relationships between entities, concepts, and project artifacts.
Run agent tasks in isolated, sandboxed environments to prevent malicious or untrusted code from affecting the host system.
| name | semantic-memory-fabric |
| description | Store, retrieve, and evolve contextual and procedural knowledge using vector search, memory layers, and active forgetting. |
Category: Agent Memory Priority: High
This skill extends DevinOS memory beyond simple file-based entries into a semantic memory fabric. It stores contextual, procedural, and semantic knowledge in a searchable, layered structure, and supports active forgetting, self-correction, and retrieval-augmented generation.
To give DevinOS a rich, long-term memory that can recall relevant experiences, apply knowledge across new contexts, and correct outdated memories without human intervention.
Use this skill when:
memory_manager.py.Scenario: The agent is starting a new project that needs authentication.
Agent Action:
Scenario: The agent retrieves every memory about "React" because the current task mentions React Native.
Agent Action: