Store a structured fact in Sibyl Memory using Coherra's schema.

Wraps memory_remember with a versioned body shape so all Coherra entities are uniform and auditable.

Args: category: Logical grouping, e.g. "people", "projects", "prefs". name: Unique-within-category key, e.g. "alice", "acme-deal". value: The fact to store — any JSON-serialisable value. confidence: Optional float 0.0–1.0 expressing certainty. source: Optional provenance tag: "conversation", "tool", "inference", etc.

Returns: {"ok": True, "category": ..., "name": ...} on success.

Fetch a single Coherra entity by (category, name).

Returns the full entity including the structured Coherra body (value, confidence, source, created_at, updated_at, version). Raises a NOT_FOUND error if the entity does not exist.

Args: category: The category the entity was stored under. name: The unique-within-category key.

Returns: {"ok": True, "category": ..., "name": ..., "body": {...}}

List entities managed by Coherra, optionally filtered by category.

Returns entities most-recently-updated first. Each result includes the structured Coherra body so the caller can inspect value, confidence, source, and timestamps without a separate recall call.

Args: category: Optional category filter. Omit to list all categories. limit: Maximum entities to return (default 50, max 200).

Returns: {"ok": True, "category": ..., "count": N, "results": [...]}

Run a full Coherra memory audit and return the results.

Scans all entities in Sibyl Memory for three classes of drift:

• contradiction two names that likely refer to the same fact but carry different values

• duplicate two entities in the same category with nearly identical values

• stale an entity whose body.updated_at exceeds the per-category threshold from coherra:config

Side-effects:

• Seeds coherra:config with default thresholds if absent

• Writes results to coherra:last_audit (memory_set_state)

• Appends a coherra_scan journal event (memory_record_event)

Returns: { "ok": True, "timestamp": "", "health_score": 0-100, "entity_count": N, "issue_count": N, "issues_found": [ {severity, category, name, detail, ...} ] }

Apply one repair to a flagged issue from the last audit.

Actions by severity: stale: "archive" — forget the entity "refresh" — bump updated_at to now (fact still true) duplicate: "merge" — keep higher-confidence entry, archive the other contradiction: "keep_a" — keep primary (category/name), archive related "keep_b" — keep related_entity, archive primary "merge_manual" — archive both, write new canonical entity at primary address using supplied value

Args: issue_id: 10-char hex id from coherra_audit issues_found list. action: Repair action string (see above). value: Required only for contradiction/merge_manual — the correct canonical value to write.

Returns: {ok, issue_id, severity, action_taken, archived, written}

Batch-apply all safe repairs from the last audit.

Auto-applies:

• stale → archive (entity is old, remove it)

• duplicate → merge (keep higher-confidence copy, archive the other)

Contradictions are NEVER auto-resolved — they require explicit human choice via coherra_repair with keep_a / keep_b / merge_manual.

Returns: {ok, fixed_count, skipped_contradictions, results: [...]}

Import JSON data from another tool into Coherra.

Runs the full onboarding pipeline:

1. Parse the JSON into individual records

2. Categorize each record (category, name, confidence)

3. Write to Sibyl Memory using the Coherra schema

4. Audit for contradictions, duplicates, staleness

5. Auto-repair safe issues (duplicates + stale)

6. Flag contradictions for manual review

Args: import_data: A dict or list representing the JSON export. Accepts flat dicts, nested dicts, lists of dicts (with key/value fields), or lists of strings.

Returns: { "ok": True, "imported": N, # records written "auto_cleaned": X, # safe issues fixed "flagged_for_review": Y, # contradictions needing manual fix }