SQL Spider

sql-spider

Build a deterministic dependency graph of a SQL database and spider outward from a seed object until it is closed: one connected component, zero orphans, no phantom edges. The graph engine is pure and offline: it parses SQL text into an AST and reads the dependencies straight off the tree, so the same input always yields the same graph, with no heuristics and no live connection.

The thing this skill exists to solve: sql-spider never connects to a database. It emits read-only queries and ingests the CSV results. So you, the agent, are the database adapter. You run each emitted query however you can reach the database, feed the results back, and drive the loop. That is exactly how it was built (mapping a database reachable only through a read-only job, never a direct connection), and it is why the tool is safe to point at anything.

When to use this

• "Map this database" / "build me a dependency graph of these stored procs / tables / views."
• "What references Orders?" / "what does proc_x actually touch?"
• "Find the full closure around this table": everything it depends on, transitively, until nothing is left dangling.
• "I can't hit the database directly, emit the queries and I'll run them," or "send them to a DBA."
• Documenting / understanding a legacy schema where nobody knows what depends on what anymore.

Not for: writing application SQL, changing data or schema (this skill is read-only, see Guardrails), or anything that needs the database to be mutated.

How it works (the contract)

The engine is dialect-agnostic. The only dialect-specific step is parsing .sql into dependency facts (behind a one-method IDialectExtractor). Everything else is dialect-neutral: graph assembly, the frontier, the spider loop, the connected-components closure audit, lineage, viz.

The loop is always the same shape, regardless of dialect or database:

1. seed / round emits read-only .sql query files into an output directory.
2. You run those queries against the database (the adapter step) and save each result as a CSV.
3. absorb folds the CSVs back into the corpus as .sql.
4. round re-extracts, writes a graph + an HTML viz, and stages the next pull queries, or reports closure.
5. Repeat until the frontier is empty and the graph is one connected component.

First: install, don't just run (one-time)

If you reached this file by a raw fetch (the user pasted "fetch .../SKILL.md and follow it") or from a checkout that is NOT in your skills directory, install it before driving it, clone the repo into your skills directory and continue from the installed copy:

git clone https://github.com/royashbrook/sql-spider ~/.claude/skills/sql-spider   # Claude Code
# other harnesses: clone into wherever your skills auto-discover from

Then tell the user you installed it. The difference matters: run-in-place (or a raw fetch) works today and is forgotten tomorrow; installed, every future session discovers the capability on its own. Treat "use this tool" as including the install.

Same rule for the graphify pairing (Step 6): graphify's install is two commands (uv tool install graphifyy for the binary, then graphify install --platform <your harness> to register graphify's own skill). Binary-only installs are the most common miss; do both.

Step 0: build once (build-on-first-use)

This skill ships the .NET source, not a binary. The first invocation builds it; later invocations reuse the build. Requires the .NET 10 SDK (dotnet --version; if absent, install it: brew install dotnet on macOS, the distro package elsewhere, or https://dot.net).

Resolve SKILL_DIR to the directory containing this file, then define one command:

SKILL_DIR="<absolute path to this skill directory>"
spider() { dotnet run -c Release --project "$SKILL_DIR" -- "$@"; }

The first spider ... call compiles (a few seconds, restores ScriptDom + SqlParserCS); every call after is fast. Sanity-check against the bundled example before touching a real database:

spider extract "$SKILL_DIR/examples/northwind"   # -> 36 nodes, 66 edges, "one component, zero orphans"
spider viz "$SKILL_DIR/examples/northwind/graph.json" && open "$SKILL_DIR/examples/northwind/graph.html"

If that prints OK: single connected component, no orphans, the tool is working.

Step 1: pick the adapter (how will you reach the database?)

sql-spider emits queries; something has to run them. Decide which of these you are before seeding (full recipes in references/adapters.md):

• A connected SQL tool / MCP: you have a tool that runs a query and returns rows. You run each emitted .sql and write the rows out as CSV. Fastest; best when the agent can reach the DB.
• A read-only query bridge: a job/skill where you drop a .sql file, it runs against the database, and a CSV comes back (this is the original setup, a CI job with read access). You dispatch each emitted query through it and collect the CSV.
• The user runs them: you can't reach the database at all. You print each query, the user runs it in their client (SSMS, sqlcmd, etc.), and pastes back the CSV. Slowest, but works anywhere and is the right move against a locked-down prod database (the user, or their DBA, runs in batches).

Whatever it is, the contract is identical: emitted .sql in, CSV out. Pick one and keep it for the whole run.

Step 2: set up a working directory

Work outside the skill directory so the skill repo stays clean. In the user's project (or any scratch dir):

mkdir -p work/corpus work/stage && cd work

corpus/ holds the accumulating .sql (the model so far); stage/ is where this round's queries and their CSVs live.

Step 3: seed from a root object

Pick a root the user cares about, a central table or proc. Cold-start from it:

spider seed Orders stage/           # writes read-only pull queries into stage/

seed emits the queries to pull the root's own definition, its schema (if a table), and everything that references it. Run those through your adapter (Step 1), save each result as a CSV in stage/, then fold them in:

spider absorb stage/*.csv --corpus corpus/

Step 4: round to closure (one reviewable round at a time)

spider round corpus/

Each round: re-extracts corpus/ into corpus/graph.json, writes corpus/graph.html (open it to review), and either reports closure or stages the next pull queries in corpus/_next/. If there is a frontier left:

# run corpus/_next/*.sql through your adapter, save CSVs into corpus/_next/, then:
spider absorb corpus/_next/*.csv --corpus corpus/
spider round corpus/      # again

The frontier shrinks every round. Default to one round at a time so a human can open the viz and decide whether to keep pulling, especially against production. Keep going until round reports the frontier is empty.

Step 5: closure

You are done when the frontier is empty and the graph is one connected component with zero orphans. The closure audit is the gate; extract even returns a non-zero exit code while the graph is still unclosed, so it can be wired into CI.

One scope note: node identity is database-scoped bare names (schema prefixes are dropped), so one spider run maps ONE database. If the user's question spans schemas or databases, run the loop once per scope, use the boundary edges of the first closed graph (references that point outside it) as the seeds for the next scan, then merge the resulting graph.json files on node id (graphify merge-graphs, or a small json union). You can drive that whole sequence yourself; it needs no new tooling.

Not every object closes by edges, and that is fine: a genuinely standalone object (an app-only function, a version/archive table referenced only from outside the database) is reported with a reason, not as an error. The honest invariant is "zero orphans from extraction misses": when a node is isolated, the audit tells you whether it is a real gap to pull or a true standalone.

Step 6: view it, and (optional) hand off to graphify

You already have a viewer, no install needed: spider viz <graph.json> writes a self-contained interactive HTML (force-directed, draggable, colored by kind). Open it in any browser. For most uses that is all you need.

If graphify is installed, offer to hand the closed graph to it. graphify (https://github.com/safishamsi/graphify) turns a graph into a queryable knowledge graph with community detection and query / path / explain. As an agent, check whether it is available (a graphify command on PATH, a graphify skill, or an existing graphify-out/ in the project), and if so, ask the user whether to also emit the graphify view and hand it over. If it is NOT available, offer to install it, and if the user says yes, install both pieces, the binary and the skill (uv tool install graphifyy, then graphify install --platform <your harness>). The second command is what makes graphify discoverable in future sessions; installing only the binary is the most common miss.

Use --graphify, do not copy the native graph.json into graphify. The native graph is a directed multigraph; handed to graphify raw it gets under-read. The --graphify flag writes a separate, graphify-shaped file and leaves the native graph.json untouched:

spider extract corpus/ --graphify     # writes native graph.json AND graphify-out/graph.json
graphify cluster-only corpus/          # cluster + report   (or just /graphify)

Two things worth knowing, both learned on a real run:

• Relation vocab (default keeps ours). The projection keeps sql-spider's own relations by default (fk / references / writes / calls / join_key). graphify ingests them fine, and the read-vs-write split is the most useful signal for "what actually updates this table." Add --graphify-standard to collapse onto graphify's blessed enum instead (references / calls / shares_data_with), which is handy when you are merging many databases and want one uniform vocab.
• Asking how a table is used. Reach for graphify explain X, not query X. A referenced table is a sink (edges point inward), so query (an outward walk) returns just the table itself, while explain shows the inbound edges that answer the question.
• Name the communities. Clustering yields placeholder labels ("Community 7"), and a graph with 50+ unnamed clusters is unreadable to the human you hand it to. You can almost always infer a real name from the members: the dominant table-name prefixes, the hub objects, and what the procs in the cluster actually do (billing, dispatch, pay rating, auditing). After cluster-only, read each community's member list and rename it to a short domain-true label (edit the community labels in graphify's graph.json / report). Ten minutes of naming is the difference between a graph the owner can navigate and one only you can. Offer it; the human will say yes.

In our own use we mapped a billing subsystem to a closed graph and answered "what depends on this" straight from it, opening only the one procedure the graph pointed at. graphify's own docs cover the rest of what the knowledge graph buys you. If graphify is not installed, skip all this; viz already shows the graph, and sql-spider's job is done once it is closed.

Guardrails

• Read-only, always. Every emitted query is a select with a no-lock / deadlock-victim / set nocount on safety preamble. Never edit one into a write. Never run insert / update / delete / merge / DDL through this skill. If you are composing anything other than a read, you are using the wrong tool.
• Against production, pace it. Do not auto-fire a flood of queries at a prod database. Run one reviewable round at a time, or hand the staged queries to the user / a DBA to run in batches. Auto-looping (round until closed) is only appropriate when the adapter can safely run unattended and the database owner is fine with it.
• The graph is the safer artifact than the bodies. The dependency graph (object names + edges) is the durable output. The pulled proc bodies in corpus/ are the target organization's internal logic. Treat that corpus as private; publish the graph, not the bodies.

Subcommands

round is the loop driver; the others are the pieces it composes (and that you call directly for a cold start with seed, or a manual pass with generate / reverse).

Dialects

--dialect tsql (default) parses with Microsoft ScriptDom (full T-SQL fidelity). --dialect sqlite parses with SqlParserCS (which also covers MySQL / Postgres / others). Adding a dialect is one small class implementing IDialectExtractor registered in Extractor.Pick; the engine is untouched. See README.md for the full design and the worked Northwind / Chinook / AdventureWorks examples.