add-graphjin-database
Use when adding a new GraphJin database, warehouse, or CQL/NoSQL backend; building a simulator because no live service is available; wiring a dialect, discovery, tests, scripts, README/CONFIG/FEATURES, or website database support surfaces.
git clone --depth 1 https://github.com/dosco/graphjin /tmp/add-graphjin-database && cp -r /tmp/add-graphjin-database/.claude/skills/add-graphjin-database ~/.claude/skills/add-graphjin-databaseSKILL.md
# Add GraphJin Database Use this skill to add a database end to end without leaving half-public support behind. GraphJin is a compiler, not a resolver stack: push behavior into dialect, discovery, schema metadata, and tests. ## Workflow 1. Ground in docs and repo truth. - Read the database's official SQL, DDL, type, catalog, and limitations docs before coding. - Inspect existing GraphJin seams: config validation, dialect factory, introspection, `tests/dbint_test.go`, hosted emulators, scripts, README, CONFIG, FEATURES, and website components. - Decide and state the public support level: experimental queries, mutations, subscriptions, full support, or simulator-only. 2. Build a simulator first when live service access is unavailable or slow. - Add `tests/hostedemu/<db>` and `tests/<db>emu` around `hostedemu.NewConnector`. - Add `tests/<db>.sql` using database-native DDL, types, constraints, and metadata features. - Keep parser/translator code owned by the target database. Reuse hosted emulator patterns, not another database's grammar, unless docs prove the syntax is identical. - Translate setup and discovery into DuckDB, but expose the database's real discovery surfaces. - Add parser, type mapping, metadata, discovery, wrapper, default-fixture, and large-catalog tests. 3. Use the simulator to build public support. - Register the DB type in `core/config.go`, the psql dialect factory, subscription dialect lookup, introspection switches, and schema-DDL/diff code if public DDL support is claimed. - Add a DB-owned dialect. Inherit behavior only after target-specific tests prove it. - Add discovery using the docs-recommended catalog path first, with fallbacks only where needed. - Wire `tests/dbint_test.go`, focused skip helpers for unsupported features, and `scripts/test-<db>.sh`. - Keep GraphJin's collision-free managed artifact SQLite database runtime-only. Its identifier is allocated at startup and must never become an application source, primary database candidate, catalog source, or security-report database. 4. Update public surfaces only after the runtime is wired. - Update README, CONFIG, FEATURES, website database logos/frontpage copy, website database matrix, and test-parallel scripts. - If the database uses `graphjin serve new --db-url`, update the URL parser and the rendered `cmd/tmpl/prod.yml` and `cmd/tmpl/agentic.yml` sources. Verify a real generated app loads without test-only placeholder replacement. - Make feature claims match tests. Use "experimental" and "query/discovery only" when writes, subscriptions, GIS, full-text, or migrations are not verified. - Preserve unrelated dirty worktree edits. Patch on top; never revert user changes. - Do not add artifact, watch, agent, stateful-MCP, or primitive-tool enablement to new `dev.yml` / `agentic.yml` examples; parsed mode defaults already provide them. Configure an artifact source only when documenting a shared clustered store. 5. Verify and leave evidence. - Run simulator tests, dialect/introspection tests, the new `scripts/test-<db>.sh`, and affected shared suites. - If shared compiler or introspection code changed, run existing dialect scripts likely to be affected. - Before finalizing, recheck docs/website claims against the support level that actually passed. ## References - Read `references/checklist.md` for the detailed implementation checklist and Redshift-specific notes.
Use when running GraphJin's own environment and evaluation workflows in this repository — generating a suite, cloning or minting a world, authoring tasks, serving graded episodes, sampling, exporting trajectories, publishing a benchmark run — or when changing code those workflows depend on.
Use when setting up a training or evaluation loop against a GraphJin agent environment — running the container, reading /health, driving episodes hosted or step-by-step or with your own agent over MCP, splitting train from eval, exporting trajectories, and deciding whether two rewards can be compared.
Create, extend, run, baseline, and diagnose GraphJin agent evaluations through the graphjin eval CLI.