---

name: kag description: "Routes KAG knowledge-construction, question-answering, MCP, and benchmark workflows for the OpenSPG KAG package." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

KAG

Use this repo skill for the OpenSPG KAG package when the user wants to create, inspect, or use a KAG project, not when they want a generic graph database or generic RAG answer.

What this skill covers

KAG is a Python toolkit for knowledge augmented generation with three major surfaces:

• project and schema setup through knext
• knowledge construction through builder chains and index managers
• query-time reasoning, MCP serving, and benchmark automation

If the task is about a KAG checkout that may be stale, read references/repo-provenance.md first.

First checks

1. Confirm the package is installed with scripts/check_kag_install.py.
2. Inspect any kag_config.yaml with scripts/inspect_kag_config.py.
3. Read references/configuration-and-registry.md when you need config keys, registry names, or config discovery rules.
4. Read references/troubleshooting.md when imports, config discovery, model keys, or CLI behavior look wrong.

Choose the right sub-skill

sub-skills/knowledge-construction/

Use this when the user wants to create or restore a project, commit schema, run a builder, choose readers/splitters/extractors/vectorizers/writers, inspect index managers, or validate a project layout before building.

sub-skills/question-answering/

Use this when the user wants to ask questions against a KAG project, inspect solver pipelines, use knext reasoner or knext thinker, or debug query-time retrieval and answer generation.

sub-skills/mcp-and-automation/

Use this when the user wants to launch or validate kag mcp-server, plan MCP tool configs, submit builder jobs to a cluster, or dry-run benchmark automation.

Shared files

• references/cli-reference.md — CLI groups, flags, and when to use each command.
• references/configuration-and-registry.md — config discovery, registries, and dynamic component loading.
• references/troubleshooting.md — cross-cutting import, config, and runtime failures.
• scripts/check_kag_install.py — safe install/import/CLI smoke check from any working directory.
• scripts/inspect_kag_config.py — redacted summary of a KAG config file.

Runtime expectations

• The published distribution name is openspg-kag.
• The import names used by future agents are kag and knext.
• Console entry points are provided by the same distribution.
• import kag may need a discoverable kag_config.yaml; the bundled install check creates a temporary minimal config so the import can be tested from any directory.
• Keep runtime instructions self-contained. Do not refer future agents to original repo docs or examples outside this skill tree.

Common requests

• "Help me set up a KAG project"
• "Which KAG index should I use for this corpus?"
• "Why is my KAG answer missing references?"
• "Can I launch KAG as an MCP server or benchmark runner?"

Working style

• Start with a safe local check before a live server or graph mutation.
• Prefer bundled config and layout helpers over guessing from source tree names.
• Route the task to a sub-skill as soon as the workflow becomes specific.
• Keep provenance in mind before reusing the skill on a different checkout.
• Treat knowledge construction, query answering, and service automation as separate concerns until the user asks to connect them.

When to stop and read more

• If a request mentions project creation, schema commits, builder chains, or index selection, go to knowledge-construction.
• If a request mentions solver pipelines, GQL queries, answer traces, or knext reasoner, go to question-answering.
• If a request mentions mcp-server, builder submission, or benchmark planning, go to mcp-and-automation.

Provenance

Read references/repo-provenance.md before refreshing this skill or before deciding whether the current checkout still matches the generated knowledge.