kg-modality-timeseries

name	kg-modality-timeseries
skill_type	skill
description	Append and query native time-series data in the epistemic-graph engine's TSDB — append points, range/window scans, as-of lookups, and gap-filling. Use when you need time-series storage or analytics next to the graph ("record metric points", "query a time range", "as-of value at time T", "gap-fill a series").
license	MIT
tags	["graph-os","engine","modality","timeseries","tsdb","metrics"]
tier	modality
wraps	["engine_timeseries"]
metadata	{"author":"Genius","version":"0.1.0"}

KG Modality — Time Series (native TSDB)

Condensed intent-surface note (Seam 8). Under the default intent surface (MCP_TOOL_MODE=intent), engine_timeseries 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=["engine_timeseries"]) once per session (as below), then proceed exactly as documented; or (2) call the write intent verb with the same natural-language request — the resolver routes to engine_timeseries for you and returns the result plus a routing justification. Set MCP_TOOL_MODE=condensed/verbose/both to expose the granular tools eagerly instead.

Fronts the epistemic-graph engine's timeseries domain: a native time-series database co-located with the graph. Supports append, range and window scans, as-of (point-in-time) lookups, and gap-filling — so temporal metrics live on the same substrate as the entities they describe, queryable cross-modally alongside graph and tabular data.

This is the modality tier — a thin wrapper over the low-level engine surface (engine_timeseries), action-routed 1:1 over the epistemic_graph client's TimeSeriesClient. The action set is discovered from the client (append, range, window, asof, gapfill, …); call with an empty action to list the live set.

How to reach it

Via the multiplexer:

1. load_tools(tools=["engine_timeseries"]).
2. engine_timeseries(action="", params_json="{}") — list actions.
3. engine_timeseries(action="range", params_json="{...}", graph="") — invoke.
4. unload_tools(...) when done.

Direct MCP on graph-os: engine_timeseries is a registered tool; per-method verbose tools appear under MCP_TOOL_MODE=verbose|both.

REST twin: POST /engine/timeseries with body {"action": "<method>", "params_json": "{...}", "graph": ""}.

Example

// discover the TSDB actions the live engine supports
engine_timeseries(action="", params_json="{}")

// append a point and read a window (exact args come from the action list)
engine_timeseries(action="append",
  params_json="{\"series\": \"cpu.util\", \"ts\": 1700000000, \"value\": 0.42}")
engine_timeseries(action="window",
  params_json="{\"series\": \"cpu.util\", \"start\": 1699990000, \"end\": 1700000000}")