autonomous-research-loop

name	autonomous-research-loop
domain	agent-tools
skill_type	skill
description	Runs one propose-only self-evolution "golden loop" cycle over the Knowledge Graph. Use when the user wants to "run the research loop", "evolve the knowledge graph", "find and address open topics", "propose specs/teams from the KG", or "self-improve the codebase". Intake of unresolved topics → acquire related sources → ADDRESSES-resolve (converge) → optional distil spec DRAFTS into .specify/ → synthesize a team PROPOSAL. Never auto-merges or executes anything — every artifact is a draft/proposal a human reviews.
license	MIT
tags	["knowledge-graph","research","self-evolution","golden-loop","propose-only","orchestration"]
metadata	{"version":"1.2.1","author":"Genius"}

Autonomous Research Loop (propose-only)

Drives the KG's self-evolution golden loop — one cycle of:

1. Intake — unresolved Concept topics (no ADDRESSED_BY edge).
2. Acquire — semantically related sources per topic (vector search over the KG; optional external X/SearXNG/scholarx when KG_RESEARCH_EXTERNAL=1).
3. Resolve — write ADDRESSES / ADDRESSED_BY edges so the loop converges (addressed topics stop re-surfacing; new open topics rise).
4. Distil (optional, KG_GOLDEN_DISTILL=1) — SpecDraft markdown written to .specify/specs/kg-distilled/ (DRAFTS only).
5. Synthesize — a TeamSpec/AgentSpec proposal addressing the topics, persisted to the KG (not executed).

Propose-only guarantee: no code execution, no PR merge, no edits outside .specify/ drafts and KG proposal nodes.

Run it

On-demand via the graph-os MCP orchestration tool:

graph_orchestrate(action="golden_loop", max_topics=5)

…or directly:

KG_DAEMON_ROLE=client python -c \
  "from agent_utilities.knowledge_graph.research.golden_loop import run_golden_loop_cycle; \
   import json; print(json.dumps(run_golden_loop_cycle(max_topics=5), default=str, indent=2))"

Always-on daemon (optional)

Set KG_GOLDEN_LOOP=1 (interval KG_GOLDEN_LOOP_INTERVAL, default 3600s) on the host daemon (gateway / graph-os-daemon) to run the cycle continuously and throttled behind the foreground gate. Off by default (autonomous LLM work is opt-in). Still propose-only.

Output

A JSON report: topics_intake, topics_resolved, sources_linked, spec_drafts (paths), team (lead + members + persisted node/edge counts), errors.

Prerequisites

• Embeddings backfilled (the host daemon's embed_backfill thread) so acquire's vector search has substrate.
• A reachable graph backend (tiered: epistemic L1 + pggraph L3).