Dreaming

Dreaming

Use this skill when an agent should wake up, read accumulated source evidence, and turn it into Signet ontology structure. The job is flexible bulk ingestion: transcripts, memory artifacts, source artifacts, notes, summaries, and imported records go in; the knowledge graph, scoped memories, and maintenance trail get better.

Dreaming maintains the graph by turning source and memory artifacts into entities, aspects, claim attributes, and links. Memory artifacts are evidence for attributes; the ontology control plane is the audited path that applies those attributes to the graph.

Apply first with provenance is the blanket rule for dreaming and ordinary graph maintenance. High-confidence, authorized maintenance should use audited operation handlers that apply directly and preserve evidence, source pointers, actor, confidence, and version history. Pending proposals are only for massive knowledge-graph refactors, risky/destructive changes, or cases where the operator explicitly asks for review before mutation.

Dreaming may save memories when the evidence supports durable recall, but not by calling the API remember endpoint. Save explicit source-backed memory artifacts or use the configured source/import machinery so provenance remains inspectable. Do not rewrite raw transcript/source artifacts or edit SQLite directly.

Inputs

Gather enough source evidence and graph context to infer useful ontology structure. Prefer recent transcript and memory-artifact windows first, then expand to bulk source sets when requested.

• recently saved memories (remember/CLI/MCP/plugin saves are immutable episodic evidence)
• recent session summaries
• raw transcripts and transcript artifacts
• recently saved memory artifacts
• source artifacts
• imported notes, documents, literature, or other indexed source records
• source-attributed epistemic assertions
• applied operation, version, merge, and proposal history
• existing entities, aspects, groups, claims, attributes, and links
• knowledge graph hygiene reports
• retrieval failures or feedback when available
• recent dreaming pass logs when available

Useful commands:

signet ontology pipeline explain --json
signet knowledge objects --json
signet ontology assertions --limit 50 --json
signet ontology proposals --status pending --json
signet ontology proposals --status applied --limit 50 --json
signet ontology proposals --status rejected --limit 50 --json
signet knowledge hygiene --json
signet dream status

Outputs

Produce the artifacts needed to complete the maintenance pass:

• applied ontology operations or an operation stream for the daemon control plane
• epistemic assertions for source-attributed claims that should not be collapsed into current truth
• source-backed memory artifacts for durable recall when the evidence warrants saving memory
• a dreaming log artifact with sources examined, changes made or proposed, rejected candidates, and questions
• a short summary of high-confidence graph and memory changes
• rejected candidates with reasons
• explicit questions where evidence is weak
• optional AGENTS.md, identity-file, or skill patch proposals as written artifacts, never as silent edits

Ontology operation line shape when batching is useful:

{"operation":"set_claim_value","payload":{"entity":"Signet","aspect":"architecture","group_key":"ontology","claim_key":"mutation_policy","value":"Dreaming and normal graph maintenance apply first through audited operations with provenance."},"reason":"Consolidated from cited transcript evidence.","evidence":[{"source_kind":"transcript","source_id":"session-key","quote":"..."}]}

Use one JSON object per line. Good operation streams usually contain a mix of:

• create_entity for concrete people, organizations, projects, tools, documents, products, places, and events that do not already exist
• create_aspect for new coherent rooms of knowledge under an entity
• set_claim_value for attributes and constraints, preserving group_key and claim_key as stable slots
• create_link for typed relationships between concrete entities
• archive_* or restore_claim_version only when evidence is strong and the operator asked for maintenance, not just ingestion

Use epistemic assertions when the source says who claimed, believed, observed, decided, preferred, denied, or questioned something. Assertions preserve attribution; they do not automatically make the asserted content current truth.

signet ontology assertion create \
  --entity "Signet" \
  --predicate claims \
  --speaker "Nicholai" \
  --asserted-at "2026-05-16T19:36:00.000Z" \
  --content "Signet should model who believes what over time." \
  --confidence 0.91 \
  --source-kind transcript \
  --source-id session-key

When bulk importing, use signet ontology assertion import --file assertions.json with this shape:

{
  "assertions": [
    {
      "entity": "Signet",
      "predicate": "believes",
      "content": "Signet should model who believes what over time.",
      "speaker": "Nicholai",
      "asserted_at": "2026-05-16T19:36:00.000Z",
      "confidence": 0.91,
      "evidence": [{ "source_kind": "transcript", "source_id": "session-key", "quote": "who believes what" }]
    }
  ]
}

Use audited entity merges for clear duplicate cleanup:

signet ontology entity merge "Canonical Entity" "Duplicate Entity" \
  --reason "Same source-backed entity after canonicalization" \
  --evidence-file evidence.json \
  --json

Use merge-plan to inspect impact or prepare a large graph refactor proposal:

signet ontology entity merge-plan "Canonical Entity" "Duplicate Entity" --json
signet ontology entity merge-plan "Canonical Entity" "Duplicate Entity" --propose --json

For large ingests, split work into coherent batches. Prefer fewer, high-confidence changes with direct evidence quotes over broad speculative coverage.

Episodic memories may carry a structured payload (entities/aspects) preserved verbatim from a structured remember save. That structured evidence renders into the dreaming context alongside the memory content and is genuinely citable: cite a quote drawn from the rendered structured form (for example an entity relationship line) when a fact lives only in the structured payload. Every evidence quote is validated against exactly the rendered source text the agent saw, so quote from the rendered form — never paraphrase, and never cite text that does not appear there.

Routing Rules

• Source-backed graph facts -> ontology operations through the control plane.
• Attributed claims, beliefs, observations, decisions, preferences, denials, and questions -> epistemic assertions through signet ontology assertion.
• Entity, aspect, group, claim, attribute, and link updates -> ontology operations.
• Clear duplicate entity cleanup -> audited signet ontology entity merge.
• Massive graph refactors or risky merge campaigns -> merge-plan --propose or explicit proposal imports.
• Durable recall lessons -> source-backed memory artifacts, not the API remember endpoint.
• Behavioral lessons -> AGENTS.md or identity-file patch proposals.
• Repeated procedures -> skill patch proposals.
• Source-backed concepts -> source/literature note proposals when that source workflow exists.
• Permissions and authority changes -> policy/authority proposals when that surface exists.

Do not collapse every observation into a memory. If the source teaches stable structure about the world, a project, a person, a system, a document, or a relationship, route it to the ontology. If the source says that a named actor believed, claimed, decided, denied, or questioned something, preserve that as an epistemic assertion first; only promote it to an ontology claim when the evidence supports treating it as current truth. If it teaches a behavioral preference or operating rule, route it to identity/AGENTS/skill patch proposals instead.

Ingestion Workflow

1. Inspect graph mutation state and existing ontology shape.
2. Read the requested transcript/artifact/source window.
3. Extract concrete semantic objects and stable facts.
4. Reconcile against existing entities, aspects, groups, claims, and pending proposals.
5. Capture attributed claims and beliefs as epistemic assertions before deciding whether they should become current ontology claims.
6. Apply straightforward, authorized maintenance through the control plane with evidence and actor provenance.
7. Use dry-run for selector validation or risky/destructive operations; use pending proposals only for massive graph refactors or when review is explicitly requested.
8. Save source-backed memory artifacts for durable recall when the pass learns something useful that is not already represented in the graph.
9. Keep a dreaming log with source ranges, changes, rejected candidates, and open questions.

When source volume is large, process in chunks and keep a dreaming log that records source ranges, skipped inputs, rejected candidates, and open questions.

Control-Plane Commands

Apply exact, authorized operations:

signet ontology stream apply ops.jsonl --json

Write proposals only for massive graph refactors or explicit review queues:

signet ontology stream apply proposals.jsonl --propose --json
signet ontology proposals --status pending --json

Use dry-run only when the operator asks for validation first, or when a risky or destructive maintenance batch needs a cheap selector check:

signet ontology stream apply ops.jsonl --dry-run --json

Hard Constraints

• Do not edit SQLite directly.
• Do not instruct an agent to silently mutate ontology state from LLM output.
• Do not call /api/memory/remember, /memory/remember, or equivalent remember endpoints from this skill.
• Preserve evidence for every graph mutation or memory artifact.
• Produce an evidence-backed mutation diff, not a vibe summary.
• Treat source memories, source artifacts, transcripts, and raw records as immutable provenance.
• Do not rewrite raw artifacts when ontology attributes change.
• Do not invent entities or attributes just to fill a schema. Weak evidence belongs in rejected candidates or open questions.
• Do not treat bulk ingestion as permission to apply low-confidence, ambiguous, destructive, or authority-changing mutations without review.
• Do not flatten "X said/believes/denies Y" into "Y is true"; use an epistemic assertion unless current-truth evidence is explicit.
• Do not create pending proposals for normal dreaming or graph maintenance when an audited apply-first operation is available.

Review Standard

Reject a candidate instead of proposing it when:

• evidence is missing or only paraphrased
• the selector is ambiguous and no stable id is available
• the mutation would archive or replace a protected entity, aspect, group, or constraint without explicit operator force
• the candidate creates a generic scaffolding entity instead of a concrete semantic object
• it duplicates an existing pending proposal

The final dreaming log should make rejected candidates and open questions as visible as applied operations.