add descriptions and example data to associations and classes in prep for feeding schema context into text mining pipeline

AGENTS.md

@@ -6,16 +6,16 @@ Schema and generated objects for biolink data model and upper ontology
 ## Repo management
 
 This repo uses `uv` for managing dependencies. Never use commands like `pip` to add or manage dependencies.
-`uv run` is the best way to run things, unless you are using `justfile` or `makefile` target
+`uv run` is the best way to run things, unless you are using a `makefile` target
 
 `mkdocs` is used for documentation.## This is a LinkML Schema repository
 
 Layout:
 
- * `src/biolink_model/schema/biolink_model.yaml` - LinkML source schema (edit this)
+ * `biolink_model.yaml` - LinkML source schema (edit this)
  * `project` - derived files (do not edit these directly, they are derived from the LinkML)
  * `src/docs` - source markdown for documentation
- * `docs` - derived docs - do not edit these directly
+ * `docs` - derived docs - do NOT edit these directly
  * `src/data/examples/{valid,invalid}` - example data files
     * always include positive examples of each class in the `valid` subfolder
     * include negative examples for unit tests and to help illustrate pitfalls
@@ -24,10 +24,9 @@ Layout:
 
 Building and testing:
 
-* `just --list` to see all commands
-* `just gen-project` to generate `project` files
-* `just test` to test schema and pos/neg examples
-* `just lint` analogous to ruff for python
+* `make --help` to see all commands
+* `make gen-project` to generate `project` files
+* `make test` to test schema and pos/neg examples
 
 These are wrappers on top of existing linkml commands such as `gen-project`, `linkml-convert`, `linkml-run-examples`.
 You can run the underlying commands (with `uv run ...`) but in general justfile targets should be favored.
@@ -39,5 +38,5 @@ Best practice:
 * Include meaningful descriptions of each element
 * always edit biolink-model.yaml in the root of the project.
 * map to standards where appropriate (e.g. dcterms)
-* Never guess OBO term IDs. Always use the OLS MCP to look for relevant ontology terms
+* Never guess OBO term IDs. Always use the OLS API to look for relevant ontology terms
 * be proactive in using due diligence to do deep research on the domain, and look at existing standards

Makefile

@@ -110,7 +110,7 @@ gen-project: $(PYMODEL)
 	# keep these in sync between PROJECT_FOLDERS and the includes/excludes for gen-project and test-schema
 	$(RUN) gen-project \
 		--exclude excel \
-		--include graphql \
+		--exclude graphql \
 		--include jsonld \
 		--exclude markdown \
 		--include prefixmap \
@@ -139,7 +139,7 @@ tests:
 	$(RUN) codespell
 	$(RUN) yamllint -c .yamllint-config biolink-model.yaml
 
-test: test-schema test-python test-examples lint spell
+test: test-schema test-python lint spell
 
 test-schema: gen-project
 
@@ -202,7 +202,6 @@ gendoc: $(DOCDIR)
 	cp $(DEST)/jsonld/biolink_model.context.jsonld $(DOCDIR)/context.jsonld ; \
 	cp $(DEST)/jsonld/biolink_model.jsonld $(DOCDIR)/biolink-model.jsonld ; \
 	cp $(DEST)/jsonschema/biolink_model.schema.json $(DOCDIR)/biolink-model.json ; \
-	cp $(DEST)/graphql/biolink_model.graphql $(DOCDIR)/biolink-model.graphql ; \
 	cp $(DEST)/shex/biolink_model.shex $(DOCDIR)/biolink-modeln.shex ; \
 	cp $(DEST)/shacl/biolink_model.shacl.ttl $(DOCDIR)/biolink-model.shacl.ttl ; \
 	cp $(DEST)/prefixmap/* $(DOCDIR) ; \
@@ -229,7 +228,7 @@ MKDOCS = $(RUN) mkdocs
 mkd-%:
 	$(MKDOCS) $*
 
-PROJECT_FOLDERS = sqlschema shex shacl protobuf prefixmap owl jsonschema jsonld graphql excel
+PROJECT_FOLDERS = sqlschema shex shacl protobuf prefixmap owl jsonschema jsonld excel
 git-init-add: git-init git-add git-commit git-status
 git-init:
 	git init