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memorize

Use the memorize skill to store observations, entities, tasks, and claims into the filesystem-backed active memory. Covers session lifecycle, working-set writes, entity/task card creation, claim extraction, and candidate promotion.

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Tyler-R-Kendrick/agentic_speculative_knowledge
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10 de abril de 2026 às 14:25
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SKILL.md
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name
memorize
description
Use the memorize skill to store observations, entities, tasks, and claims into the filesystem-backed active memory. Covers session lifecycle, working-set writes, entity/task card creation, claim extraction, and candidate promotion.
# Memorize Store new knowledge into the agent's active memory layer. ## When to use - Starting or ending a working session - Recording observations, notes, or decisions - Registering entities or tasks encountered during work - Extracting structured claims from unstructured text - Promoting candidate memories to trusted status ## Quick start ```python import pathlib from src.api.memory_manager import MemoryManager mgr = MemoryManager(root_dir=pathlib.Path(".agent-memory")) # 1. Start a session session = mgr.start_session(current_goal="investigate auth failures") # 2. Record a working item item = mgr.add_working_item( item_type="observation", content="The auth service returned 401 after cert rotation.", ) # 3. Register an entity entity = mgr.add_entity( name="auth-service", entity_type="service", description="Handles authentication and token issuance.", ) # 4. Register a task task = mgr.add_task(title="Check certificate expiry dates", priority=1) # 5. Extract claims from text claims = mgr.extract_claims( text="The auth service failed after a certificate rotation.", source_ref="incident-report-42", ) # 6. Promote eligible memories results = mgr.promote_memories() # 7. Close the session mgr.end_session() ``` ## Key APIs | Class | Method | Purpose | |---|---|---| | `MemoryManager` | `start_session()` | Open a new working session with an optional goal | | `MemoryManager` | `end_session()` | Close the active session | | `MemoryManager` | `add_working_item()` | Append an observation, note, or decision to the working set | | `MemoryManager` | `add_entity()` | Write an entity card to active memory | | `MemoryManager` | `add_task()` | Write a task card to active memory | | `MemoryManager` | `extract_claims()` | Extract and persist structured claims from text | | `MemoryManager` | `promote_memories()` | Run governance rules and promote eligible candidates | | `SessionManager` | `create_session()` / `close_session()` | Low-level session lifecycle | ## Grounding - `src/api/memory_manager.py` — high-level orchestration API - `src/active_memory/session_manager.py` — session create / close / load - `src/active_memory/working_set.py` — JSONL working-set appender - `src/active_memory/entity_card.py` — entity card writer - `src/active_memory/task_card.py` — task card writer - `src/claims/extractor.py` — claim extraction from text - `src/governance/promotion.py` — promotion eligibility engine - `tests/unit/test_active_memory.py` — unit coverage - `notebooks/01_active_memory_basics.ipynb` — executable walkthrough ## Rules 1. Always use `MemoryManager` from `src/api/memory_manager.py` as the entry point; do not bypass it with low-level appenders unless extending the API itself. 2. Preserve the filesystem-first layout: `active/`, `journal/`, `claims/` directories under the memory root. 3. Keep claim extraction aligned with the existing `ClaimExtractor` pipeline rather than duplicating extraction logic. 4. Validate changes with `tests/unit/test_active_memory.py`.
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