| 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:
- Intake — unresolved
Concept topics (no ADDRESSED_BY edge).
- Acquire — semantically related sources per topic (vector search over the
KG; optional external X/SearXNG/scholarx when
KG_RESEARCH_EXTERNAL=1).
- Resolve — write
ADDRESSES / ADDRESSED_BY edges so the loop converges
(addressed topics stop re-surfacing; new open topics rise).
- Distil (optional,
KG_GOLDEN_DISTILL=1) — SpecDraft markdown written
to .specify/specs/kg-distilled/ (DRAFTS only).
- 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).
