| name | kg-evaluate |
| skill_type | skill |
| description | Evaluates agents/harnesses and reasons over learned world models — scoring, harness gates, world-model rollouts, forecasting, causal/invariant analysis, specialization cycles. Use for eval & simulation — "score these outputs", "run the harness gate", "forecast", "world-model rollout". |
| license | MIT |
| tags | ["graph-os","evaluate"] |
| tier | core |
| metadata | {"author":"Genius","version":"0.1.0"} |
kg-evaluate
Condensed intent-surface note (Seam 8). Under the default intent surface (MCP_TOOL_MODE=intent), graph_evaluate 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_evaluate"]) once per session (as below), then proceed exactly as documented; or (2) call the why intent verb with the same natural-language request — the resolver routes to graph_evaluate 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_evaluate covers evaluation, gates and world-model reasoning. Actions: evaluate/evaluate_alpha (score outputs), evaluate_harness, guard_corpus, harness_gate (formal no-regression SHACL gate, AHE-3.53), check_constraints, specialize (SAI specialization cycle, AU-AHE.harness.sai-controller), world_model_rollout (KG-2.73b), latent_efficiency_benchmark, evolve_model, forecast, causal, invariant.
Invoke
- MCP:
load_tools(tools=["graph_evaluate"]), then graph_evaluate(action="harness_gate", ...).
- REST twin:
POST /graph/evaluate with {"action": "forecast", ...}.
Example
graph_evaluate(action="world_model_rollout", query="ingestion lane under full-fleet reingest")
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.
