| name | kg-loops |
| skill_type | skill |
| description | The single entrypoint for long-running objectives (research/develop/skill Loops) plus observing and steering the self-evolution flywheel. Use to run or steer Loops and evolution — "submit a Loop", "drive this Loop", "show evolution state", "review a spec proposal". |
| license | MIT |
| tags | ["graph-os","loops","evolution"] |
| tier | core |
| metadata | {"author":"Genius","version":"0.1.0"} |
kg-loops
Condensed intent-surface note (Seam 8). Under the default intent surface (MCP_TOOL_MODE=intent), graph_loops is held back from the default tool list (nothing removed — REST + _execute_tool still reach it exactly as documented below). Two ways to use this skill unchanged: (1) load_tools(tools=["graph_loops"]) once per session (as below), then proceed exactly as documented; or (2) call the act intent verb with the same natural-language request — the resolver routes to graph_loops for you and returns the result plus a routing justification. Set MCP_TOOL_MODE=condensed/verbose/both to expose the granular tools eagerly instead.
graph_loops (CONCEPT:AU-KG.research.these-properties-carry) is one entrypoint for Loops of kind research|develop|skill. Actions: submit (objective + kind [+ validation_cmd/end_state for develop, skill_ref for skill]), list, run (advance all active Loops one cycle), drive (run ONE Loop by id to completion, durably/resumable), cancel, prioritize. Transparency + steering: state (live EvolutionState — stage + why, saturation, open-gaps trend, velocity, backlog), specs (SpecProposal backlog), review (approve|edit|reject a distilled spec before it develops).
Invoke
- MCP:
load_tools(tools=["graph_loops"]), then graph_loops(action="state").
- REST twin:
POST /graph/loops with {"action": "submit", "objective": "...", "kind": "develop", "validation_cmd": "pytest -q"}.
Example
graph_loops(action="review", loop_id="spec:123", ...) # approve|edit|reject a distilled spec
Delegation
If graph-os is reachable, offload composite multi-step work via graph_orchestrate (execute_agent / execute_workflow) instead of hand-running the steps — let the local LLM + Loop engine do it, and resolve only the exceptions.
