القائمة
Use the recall skill to retrieve composed context from active memory, trusted Terminus graphs, and optional speculative branches. Also covers journal-backed history of past working-memory mutations.
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
| name | recall |
| description | Use the recall skill to retrieve composed context from active memory, trusted Terminus graphs, and optional speculative branches. Also covers journal-backed history of past working-memory mutations. |
Retrieve past knowledge from the memory layers.
import pathlib
from datetime import date
from src.api.memory_manager import MemoryManager
from src.retrieval.composer import RetrievalComposer
from src.journal.appender import JournalAppender
root = pathlib.Path(".agent-memory")
# High-level retrieval via MemoryManager
mgr = MemoryManager(root_dir=root)
context = mgr.retrieve_context(include_terminus=False)
# context keys: session, working_items, entities, tasks, claims,
# terminus_memories, speculative_inference, facet_relations
# Include trusted Terminus memories and speculative inference
full = mgr.retrieve_context(
include_terminus=True,
include_speculative=True,
inference_branch="inference/sess-1",
)
# Journal history for today
appender = JournalAppender(root)
events = appender.read_day(date.today())
for event in events:
print(event.mutation_kind, event.changed_files)
from src.retrieval.composer import RetrievalComposer
from src.terminus.adapter import TerminusMemoryRepository
repo = TerminusMemoryRepository(url="http://localhost:6363")
composer = RetrievalComposer(root, terminus_repo=repo)
trusted = composer.retrieve(include_terminus=True)
exploratory = composer.retrieve(
include_terminus=True,
include_speculative=True,
inference_branch="inference/sess-1",
)
| Class | Method | Purpose |
|---|---|---|
MemoryManager | retrieve_context() | High-level composed retrieval with opt-in layers |
RetrievalComposer | retrieve() | Low-level retrieval combining active, Terminus, and speculative sources |
JournalAppender | read_day() | Read mutation events for a specific date |
ActiveRetriever | get_session() / get_working_set() / get_entities() / get_tasks() / get_claims() | Individual active-memory readers |
src/retrieval/composer.py — composed retrieval from multiple layerssrc/retrieval/active_retriever.py — filesystem active-memory readersrc/retrieval/terminus_retriever.py — Terminus trusted/speculative readersrc/journal/appender.py — journal day-file readertests/integration/test_retrieval.py — integration coveragenotebooks/03_historical_recall.ipynb — executable walkthroughRetrievalComposer from src/retrieval/composer.py to merge context layers rather than hand-merging sources.JournalAppender in src/journal/appender.py as the source for mutation history.include_speculative=True.tests/integration/test_retrieval.py.Use the speculate skill when you need critique-ready hypotheses from new observations: recall context, run the mutation pipeline on a fresh observation, retrieve ranked inference and facet results from an inference/* branch, and present candidate text, provenance, assumptions, uncertainty, and review priority without promoting anything to trusted memory.
Use the discover skill to find connections between claims through facet relations and to rank inference and facet candidates using manifold geometry. Covers facet modeling, manifold scoring, relatedness, distance, uncertainty, and retrieval of discovery results.
Use the infer skill to generate speculative inference candidates from claims, persist them on branch-local inference graphs, and run the full mutation pipeline that coordinates active memory, claims, Terminus persistence, and speculative ranking.
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.
Use the reflect skill to persist memories, claims, and inference nodes into the TerminusDB temporal graph. Covers branch lifecycle, trusted session writes, temporal recall, and the separation between session and inference branches.