Ghostlight/notes/ghostlight-current-system-map.md

Ghostlight Current System Map

Ghostlight still does not have a full runtime. The live system is a set of validated artifact seams for producing reviewed training data.

Control Flow

1. Rehydrate from state/map.yaml, notes/fresh-workspace-handoff.md, this file, and notes/ghostlight-implementation-plan.md.
2. Run npm run state:status.
3. Work one bounded organ.
4. Validate the seam that matters.
5. Persist only belief-changing evidence.
6. Commit and push completed work.

Core Surfaces

• state/map.yaml: canonical mission, boundaries, live architecture, next action.
• state/evidence.jsonl: distilled belief-changing evidence only.
• state/evidence.archive.jsonl: older evidence preserved for archaeology.
• notes/fresh-workspace-handoff.md: compact re-entry packet.
• notes/ghostlight-implementation-plan.md: near-term implementation sequence.
• docs/architecture/: detailed contracts and rationale.

Active Artifact Pipeline

Aetheria lore/source context
  -> lore grounding digest / source notes
  -> canonical agent and scene state
  -> projected local context
  -> responder packet
  -> sandboxed responder output
  -> review and leakage audit
  -> mutation receipt
  -> updated fixture / future training corpus

Important Contracts

• Agent state: schemas/agent-state.schema.json
• Projection examples: schemas/projection-example.schema.json
• Lore grounding: schemas/lore-grounding-digest.schema.json
• Projected local context: schemas/projected-local-context.schema.json
• Coordinator artifacts: schemas/coordinator-artifact.schema.json
• Responder packets: schemas/responder-packet.schema.json
• Responder outputs: schemas/responder-output.schema.json

Current Live Example

• Research-enabled packet: examples/responder-packets/scene-02-sanctuary-intake.sella_ren.packet.research.v0.json
• It uses responder_scoped_repository_search, now treats allowed_scope as seed scope, permits bounded traversal through relevant AetheriaLore links, and requires consulted_refs, followed_refs, research_trace, plus research_summary.
• First research-enabled capture: experiments/responder-packets/cold-wake-sanctuary-intake-sella-research-enabled-v0.capture.json
• First research-enabled mutation receipt: experiments/responder-packets/cold-wake-sanctuary-intake-sella-research-enabled-v0.mutation.json
• First research-enabled aftermath state: examples/agent-state.cold-wake-story-lab.after-sella-research-conditions.json
• The capture is useful draft data. Its research trace is coordinator_reconstructed, not runner_captured, so it audits the lore constraints but does not prove the responder's actual tool path.
• Future accepted research-enabled gold captures must use runner_captured research trace status.
• First accepted runner-traced coordinator-scoped sample: experiments/responder-packets/cold-wake-callisto-provenance-veyr-trace-v0.capture.json
• It starts from examples/agent-state.cold-wake-story-lab.after-sella-research-conditions.json, projects Veyr for scene-03-callisto-provenance, embeds coordinator-retrieved lore as packet-visible source excerpts, runs a no-fork packet-only responder, and mutates state into examples/agent-state.cold-wake-story-lab.after-veyr-category-offer.json.
• Parent sessions currently receive only the subagent final message, not a visible tool-call transcript. Treat subagent research as self-reported unless the worker returns explicit research notes or a future runner captures calls.

Archived Receipts

Old local-model prototype files with qwen in their paths remain useful for validation and lessons about structured-output failure, object custody, embodiment, prompt leakage, and materialization. They are not active direction. Do not let them steer gold responder data unless the task is explicitly model plumbing.

Missing Organs

• Deterministic speaker-local input slicer
• Full prompt renderer
• Classifier/appraiser pipeline
• Relationship/perception updater
• State mutator model
• Student projector
• Full scene/event loop implementation
• Culture prior engine
• Automatic promotion of branch outcomes into canonical state

Current North Star

Generate high-quality, sandboxed, source-grounded responder/coordinator samples that can later train specialized Ghostlight models while staying usable by a game engine: exact inputs, exact outputs, provenance, review labels, state mutation receipts, and clear branch consequences.