Stop giving AI agents raw database connection strings.

Give them SchemaBrain instead — a read-only trust and intelligence layer where the agent never writes SQL, PII is refused before the query runs, and every call lands in a tamper-evident audit log.

Works with Claude Desktop · Claude Code · Cursor · Windsurf · any MCP host

SchemaBrain compiles every query from definitions you control — no path from a prompt to raw SQL at your database.

Three guarantees that close the trust gap between AI agents and your database:

• Read-only by architecture — twelve MCP tools, none of which can write. No execute() tool, no query() tool, no path from agent prompt to a write at your database.
• PII-aware refusal at retrieval — PII tags propagate from the physical schema through joins and metrics. If a query touches a blocked category, SchemaBrain refuses before the database is queried.
• Cryptographic audit chain — every call, refusal, and recovery is recorded in a SHA256-hashed append-only log (best-effort: a disk-full or no-writer configuration logs a warning and continues rather than failing the query). audit verify exits non-zero if any past row was rewritten.

See it in action — ask for something the schema can't answer, and it refuses instead of fabricating a join:

You: compute usage volume by plan tier

SchemaBrain → agent: { "kind": "unreachable_entity", "recovery": { "suggested_tool": "resolve_join" } } — there's no plan_id on usage events, so it won't invent one.

Claude: I can't fake that join — here's contracted revenue by plan tier instead, which actually resolves. ✓

→ Full session, with the SQL and results

Watch it run — a live Postgres schema becomes a governed knowledge graph, the firewall computes the safe metric and refuses the leaks, and every call lands in a tamper-evident audit log. No agent, no API key:

uvx schemabrain init
# then: Cmd+Q Claude Desktop, relaunch, and ask: "list the entities SchemaBrain knows about"
# prefer a persistent install? pipx install schemabrain (or) pip install schemabrain

Cost: $0 to run the bundled demo (pre-curated pack, no API key) · ~$0.03 to LLM-index a fresh 84-column schema · $0 to re-index unchanged schemas. Detail in Sample session.

Status: 0.6.0 (beta). Postgres supported today (the local store itself is SQLite). SQLite / Snowflake / BigQuery / MySQL source connectors on the roadmap.

Contents

Read next based on what you need:

Quickstart

Just want to see what it does? uvx schemabrain demo — one command, zero prompts. Builds the sample SaaS layer, then lets you open the dashboard or run a terminal firewall showcase. No API key, and no Docker for the dashboard / showcase paths. The steps below are for wiring SchemaBrain into your own agent against your own database.

Three steps from uvx schemabrain init to a working Claude Desktop integration. If you paste your own Postgres URL — no Docker needed, ~30s. Press Enter for the bundled demo and init invokes Docker + downloads a ~67 MB embedding model first time; ~45s once cached.

1. Install

uvx schemabrain init        # zero-install: runs the wizard in one shot
# or install persistently first:
pipx install schemabrain    # (or) pip install schemabrain
schemabrain --version

Source install (git clone + uv sync --extra dev) is documented in docs/setup.md.

2. Run the activation wizard

schemabrain init

init is a seven-stage wizard that takes you from "I have a Postgres database" to "Claude Desktop can answer questions about it" in one command. On first run it prompts for what it needs:

• A Postgres URL — paste your own connection string, or press Enter to spin up a local demo Postgres container with the bundled SaaS fixture (Docker is invoked automatically; idempotent on re-runs).
• An ANTHROPIC_API_KEY — optional. Skip and the wizard still wires Claude Desktop. On the demo path, entities + metrics + joins are pre-curated from a bundled YAML pack — the semantic layer works zero-config. On your own database, entity curation can run later via schemabrain entities suggest --apply once you have a key.

SchemaBrain init — activation wizard

  [1/7] Source check       ✓ source reachable + read-only
  [2/7] Index schema       ✓ 12 tables, 84 columns indexed
  [3/7] Curate entities    ✓ 12 entities applied (bundled demo pack)
  [4/7] Curate metrics     ✓ 5 metrics applied (bundled demo pack)
  [5/7] Curate joins       ✓ 11 canonical joins applied (bundled demo pack)
  [6/7] Wire host          ✓ wrote schemabrain entry to claude_desktop_config.json
                           (default; switch with --host claude-code|cursor|windsurf|manual)
  [7/7] Next               ✓ restart your MCP host, then ask: "list the entities SchemaBrain knows about"

Full wizard reference (stages explained, flags, dbt auto-detection, --print-only for non-Claude-Desktop hosts, --no-entities / --no-metrics / --no-joins opt-outs, cost-cap pauses): docs/setup.md.

3. Restart Claude Desktop and ask

1. Quit Claude Desktop fully — Cmd+Q, not just close the window. The MCP config is only read on cold start.

2. Relaunch.

3. New conversation:

   list the entities SchemaBrain knows about

If Claude calls list_entities and reports user, order, etc., you're done. If not, see Troubleshooting.

After the wizard, schemabrain inspect shows what the agent has and schemabrain tail streams every tool call live — see docs/operations.md.

Your project files

init writes just ./schemabrain.db (the local store — gitignore it) plus your host config. To tune the PII policy and semantic layer as editable YAML, re-run with --emit-yaml-dir:

schemabrain init --url-env DATABASE_URL --emit-yaml-dir ./schemabrain
# → ./schemabrain/pii_policy.yaml + entities/ + metrics/ + joins/

Edit a file, schemabrain apply ./schemabrain, schemabrain check to validate, restart serve. There is no schemabrain.yaml — config is CLI flags + SCHEMABRAIN_* env vars (auto-loaded from .env) + that YAML tree. Full map: Your project.

Safety guarantees

Six properties SchemaBrain enforces at the SQL boundary today:

1. Read-only by architecture, not configuration

The MCP surface exposes twelve tools — none of which can write. No execute(), no query(), no path from agent prompt to a write at your database, regardless of session state — the guarantee is structural, not a flag the agent can flip. schemabrain serve also pins default_transaction_read_only=on as belt-and-suspenders. Read-only by architecture →

2. PII-aware refusal at the get_metric tool boundary

Any get_metric touching a blocked PII category returns a refused envelope — the compiled SQL never runs and the refusal lands in mcp_audit. describe_entity enforces the same at the column level (blocked columns ship redacted=True). init blocks the catastrophic-leak set by default (credential,payment_card,government_id); --pii-block replaces the set, so widen by listing the full target. Detection is column-name pattern matching across twelve GDPR / CCPA / HIPAA / PCI categories; content-aware classification is on the roadmap. PII taxonomy & propagation →

3. Tamper-evident audit log

Every tool call writes one row to an append-only mcp_audit table — PII categories, content-addressable fingerprints, sha256 hash chain. audit verify re-walks the chain and exits non-zero if any past row was rewritten.

schemabrain audit verify   # exit 0 = chain clean

Tamper-evident audit chain →

4. Failure is a contract, not a string

Every non-success call — refused, error, or degraded — returns a structured recovery.suggested_args block, not a message to parse. PII blocks (status: "refused") ship the entity to retry; ambiguous dimensions and unreachable entities (status: "error") ship the candidate to pick or the next tool to call. Only policy refusals are refused; "I won't guess" is error with a recovery payload.

{ "status": "error", "kind": "ambiguous_time_dimension",
  "recovery": { "suggested_tool": "get_metric",
                "suggested_args": {"time_dimension": "order.placed_at"} } }

Structured recovery →

5. Compile path: definitions → parameterized SQL

Entities, metrics, and canonical joins compile to parameterized SQL SchemaBrain runs on its side. The agent sees rows + the SQL that ran — never arbitrary statements at your database. LLM-suggested definitions during init are reviewed and applied explicitly. Build your semantic layer →

6. Pluggable into any agent loop

The same MCP stdio surface Claude Desktop sees is exposed to any MCP host — your own Anthropic, OpenAI, or LangGraph loop included. examples/anthropic_demo.py is a ~260-LOC drop-in that wires Claude Haiku 4.5 to schemabrain serve and prints exactly which tools the agent chose. Anthropic SDK walkthrough →

Observability dashboard

SchemaBrain ships an opt-in, read-only dashboard over the same audit + PII + refusal data the MCP server is already writing. schemabrain dashboard boots a local FastAPI sidecar serving a pre-built static UI — no Node runtime, no network exposure, no write paths.

pip install "schemabrain[ui]"
schemabrain dashboard
# → http://127.0.0.1:7878

It's a viewer, not a console — no settings, no SQL pad, no write path. Nine read-only surfaces, each answering an operator question the MCP envelope alone never surfaces visually. The signature surface is the Knowledge Graph — your schema rendered as the same entity-relationship projection the semantic layer compiles joins against:

• Knowledge Graph (/graph) — how does my schema actually connect? Entities as nodes, canonical joins as edges (solid for declared FKs, dashed for log-mined), PII-bearing entities flagged, and refusal hotspots highlighted, with declared-FK cardinality shown on the highlighted join path — the schema as a graph, not a table list.
• Overview (/overview) — the home surface: entity / metric / join / catastrophic-PII counts at a glance.
• Entities (/entities) — a sortable index; drill into any entity's columns, PII, metrics, and canonical joins.
• Data Dictionary (/dict) — every table, column, type, PII class, join, and metric, with one-click Markdown export (the same artifact schemabrain docs writes).
• PII matrix (/pii) — which columns carry sensitive data? A heatmap with one row per classified column and one cell per PII category, each column tagged block / redact / allow by its advisory band. Columns in a catastrophic-leak category (credential, payment_card, government_id) are hard-blocked regardless of policy and pinned to the top — so you catch a payment_card column hiding inside users before you point an agent at a new schema, and see at a glance what trips the default --pii-block policy. Select any row to drill into its entity's columns, metrics, and joins.
• Refusals (/refusals) — what did SchemaBrain block, and what did the agent see? A chronological feed of held calls; expand any row to reveal the full envelope inline — the reason that fired (pii_blocked, allowlist_violation, fragment_unsafe, cost_cap_exceeded, ambiguous_resolution, schema_drift), the exact category set that intersected the policy, and the structured error.recovery (suggested tool + args) the agent got back to recover. Use it to triage "the agent says it can't access that" and to review whether those hints actually helped.
• Audit Viewer (/audit) — is the audit chain still intact? The visual face of the tamper-evident log: every tool call writes exactly one row — whatever the outcome — anchored by chain_hash = sha256(prev_hash || canonical(row)). An integrity strip reads not verified this session until you run a pass, then verified · n/N intact (or flags N rows edited after write); the Verify button re-walks the chain server-side and recomputes each visible row's RFC-6962 Merkle inclusion proof in your browser. Selecting a row opens the full body (tool, status, cost class, PII categories, fingerprint, chain_hash, and the proof ladder up to the root). Reload to pick up new calls.
• Policy (/policy) — the block / redact / allow grid the firewall enforces, with the always-on catastrophic-leak floor disclosed (it can't be removed). Changes are made via copy-the-CLI actions — the dashboard never writes.
• Drift (/drift) — config and enrichment drift the store can detect, each with a copy-the-CLI fix.

Knowledge Graph — your schema as the entity-relationship projection the semantic layer compiles joins against; catastrophic-PII entities flagged, the canonical join path traced.

The dashboard binds 127.0.0.1 only — there is no --host flag, by design. It's read-only and reads the same SQLite store serve writes to. No agent talks to it.

Dashboard guide → · PII matrix → · Refusals → · Audit Viewer →

Works with

SchemaBrain speaks the Model Context Protocol over stdio. schemabrain init --host <X> writes first-party config for four MCP clients; everything else that speaks MCP stdio works via --host manual (prints the snippet, you paste).

First-party wiring

schemabrain init --host <X> writes the MCP entry directly into the host's config file.

Any other MCP stdio host

schemabrain init --host manual prints the JSON entry to stdout — paste it into whatever host config you're using. Any client that launches a subprocess and speaks MCP stdio should work in principle; we have not exhaustively tested each. Common targets:

• Zed — full walkthrough at docs/setup/zed.md
• Codex CLI (working path for ChatGPT users) — full walkthrough at docs/setup/codex.md
• Cline (VS Code extension) — schemabrain init --host manual prints the mcpServers block; paste it into Cline's settings via MCP Servers → Configure MCP Servers. Full walkthrough at docs/setup/cline.md
• Continue — paste into ~/.continue/config.json
• Your own agent loop — see examples/anthropic_demo.py for a ~250-LOC Anthropic-SDK reference

The 12-tool surface, PII-aware refusal, audit chain, and recovery contracts are transport-agnostic — any compliant stdio MCP client gets the same guarantees.

Agent frameworks

The same stdio MCP surface is reachable from any framework that can spawn an MCP server. The Anthropic SDK path is first-party-tested; the others work in principle if the framework's MCP integration speaks stdio.

• Anthropic SDK — first-party walkthrough at docs/setup/manual.md; reference loop at examples/anthropic_demo.py
• LangChain / LangGraph — via langchain-mcp-adapters
• Pydantic AI — via its built-in MCP support
• CrewAI / AutoGen / Agno / custom loops — any framework with a stdio MCP client works in principle; we have not tested each

We don't ship per-framework adapters; the framework's standard MCP client is sufficient.

Not yet supported (cloud / HTTPS hosts)

SchemaBrain v0.6 ships stdio only — no HTTPS / SSE transport. Clients that require a cloud HTTPS endpoint do not work today:

• ChatGPT Connectors — see the honest gap page for workarounds and the v0.5+ roadmap
• Hosted MCP gateways — by design (local-first wedge; see vs Querybear)

If you need ChatGPT support today, a community stdio→HTTPS bridge (mcp-remote, mcp-proxy, etc.) may work; we have not validated any specific bridge against the PII / audit / recovery semantics.

Security posture for procurement / review →

Sample session

Real Claude Desktop session against the bundled SaaS fixture (12 tables, 84 columns, 12 entities). Condensed for length — a real agent explores list_metrics / suggest_joins first, then concludes the path is unreachable:

You: Using SchemaBrain, compute our usage volume broken down by plan tier.

Claude: (calls get_metric(name="usage_volume", group_by=["plan.title"]). The metric anchors on usage_event; reaching plan would need a join that isn't in the schema, so it refuses:)

config.json

Copy

{ "status": "error",
  "error": {
    "kind": "unreachable_entity",
    "message": "entity 'plan' is not reachable from metric anchor 'usage_event'; no canonical join exists. Run `schemabrain joins suggest` to surface candidate joins.",
    "recovery": { "suggested_tool": "resolve_join",
                  "suggested_args": { "entity_a": "usage_event", "entity_b": "plan" } } } }

(Claude confirms at the join layer — resolve_join("usage_event", "plan") returns kind: no_canonical_join. usage_events carries only workspace_id, never a plan_id, and no table bridges the two.)

Verdict up front: I can't compute usage-by-plan, and you shouldn't want me to fake it — there is no modeled relationship between usage events and plans in this schema. usage_events records a workspace_id, not a plan, and nothing joins the two. "Usage volume by plan tier" is unanswerable until that link exists — and I'm not going to invent it.

What I can give you — contracted revenue by plan tier (this path fully resolves: subscription_item → subscription → plan, both fk_constraint joins, confidence: HIGH):

Dockerfile

Copy

SELECT p.title                                     AS plan_tier,
       SUM(si.unit_price_cents * si.seats) / 100.0  AS revenue   -- prices stored in integer cents
FROM   public.subscription_items si
JOIN   public.subscriptions      s ON si.subscription_id = s.id
JOIN   public.plans              p ON s.plan_id          = p.id
GROUP BY p.title
ORDER BY revenue DESC;

plan_tier	revenue
Enterprise	$379,620.00
Pro	$539.00
Free	$54.00

Two honest caveats:

• Contracted, not collected. This is subscription line-item revenue (seats × unit_price_cents), not paid invoices. For billed totals, the total_revenue metric anchors on invoices instead.
• Integer cents. unit_price_cents is stored as an integer; the / 100.0 converts to currency.

The differentiator is what didn't happen: most LLM-over-database tools, asked for usage-by-plan, would confidently emit JOIN plans p ON usage_events.plan_id = p.id against a plan_id column that doesn't exist. SchemaBrain refused — get_metric returned kind: unreachable_entity with recovery.suggested_tool: resolve_join, not prose. The agent acted on the structured recovery contract programmatically instead of fabricating a join. Refusal-not-fabrication is the safety mechanism, demonstrated live.

Cost. ~$0.0004/column with Claude Haiku 4.5 (cryptic-name columns can opt into Sonnet 4.6 via --enable-sonnet). The bundled 12-table fixture (84 columns, 12 entities + 5 metrics + 11 joins) ships pre-curated, so the demo path applies it for $0 — no API key. Indexing those 84 columns with LLM column descriptions comes to about $0.03. The Pagila DVD-rental sample (87 columns after partition deduplication) is the directly measured reference — $0.0299 in 105s. Re-indexing an unchanged schema is $0 — content-addressable fingerprinting skips the LLM call entirely.

To verify Claude's SQL is mechanically correct (and that flagged caveats are the actual data behavior), see Validating SQL Claude generates.

Run this exact session yourself: schemabrain init walks you to a wired Claude Desktop in one command; then ask Claude "Using SchemaBrain, compute our usage volume broken down by plan tier." and watch the refuse-then-pivot live.

Where it's going

SchemaBrain is evolving into a trust and intelligence layer between AI agents and your database — it gives the agent a semantic map of your schema, compiles answers from definitions you control, and keeps every call PII-aware and audited. SQL-boundary safety is one proof-point of that layer, not the whole identity.

That posture rests on a semantic substrate. You can't refuse "this query touches PII" without knowing which columns are PII. You can't answer "join through this junction" without canonical-join definitions. You can't serve a metric without knowing its grain.

So the engineering order is schema intelligence → semantic substrate → trust primitives. Today the agent never writes raw SQL: it calls get_metric and the semantic-layer tools, SchemaBrain compiles parameterized SQL the agent never sees, and you get PII-aware refusal, structured recovery on every refused or degraded call, read-only execution with statement timeouts and row caps, and a tamper-evident audit chain. That def-driven, compiled-SQL posture is the default and the recommended one. Inspecting arbitrary agent-emitted SQL (validate_query / execute) is a later, optional opt-in lane — not the direction we're pivoting to. See the Roadmap.

Roadmap

The v0.5 / v1 / v2 / v3 labels are roadmap milestone names, not package versions. The package follows strict semver — 1.0.0 is reserved for an API that's been battle-tested by external users without a forced break. See ADR-0003.

The full, living roadmap — including explicit non-goals and how to influence priorities — lives in ROADMAP.md.

Now — shipping in v0.6.x

What you get from pip install schemabrain:

• MCP server, 12 read-only tools — find_relevant_tables, find_relevant_entities, describe_table, describe_column, describe_entity, list_entities, list_metrics, list_joins, suggest_joins, resolve_join, get_example_queries, get_metric.
• Def-driven compilation — the agent never writes raw SQL; answers compile from definitions you control, with read-only execution enforced at the database layer plus statement timeouts and row caps.
• Schema-intelligence engine — index Postgres into a local SQLite store; cost-capped LLM semantic enrichment (with opt-in Sonnet routing for cryptic columns, --enable-sonnet); on-device embeddings (BAAI/bge-small ONNX); semantic table retrieval (cosine similarity over those embeddings, ranked per-table by best-matching column); entity identification with rationale + confidence; declared-FK, query-log, and dbt-relationships join mining; a persisted canonical join graph with multi-hop BFS; and a metrics layer.
• Trust & safety — PII classification (60 rules across 12 categories) with per-column confidence, tag propagation, a catastrophic-leak floor (grouping by a PII column refuses as row-level disclosure), an editable policy (block / redact / allow plus per-column overrides), and a tamper-evident sha256 hash-chained audit log with browser-verifiable RFC-6962 Merkle proofs and audit verify.
• Graph-led dashboard, 9 surfaces — a signature interactive Knowledge Graph, plus Overview, Entities (sortable index + drilldown with a semantic pane), Data Dictionary (Export-to-Markdown), PII matrix, Refusals, Audit Viewer, an editable Policy editor, and Drift intelligence. Dual-theme, opt-in, read-only, 127.0.0.1-only.
• CLI — init, demo, index, import dbt, inspect, diff, check, entities, joins, metrics, policy {show, apply, tag}, docs, dashboard, doctor, serve, audit. Distributed on PyPI (Apache-2.0 licensed) and as a headless Docker image.

Later — roadmap (deferred; future direction only)

Phase 2 — differentiators

• Query cost estimation (EXPLAIN of the compiled SQL)
• Tenant-isolation detection — missing-filter and cross-tenant-join checks
• Impact analysis across definitions
• Usage intelligence — hotspots and dead-table detection
• A general policy-rule grammar
• Implicit-FK discovery without query logs
• Context budgeting for tool responses

Phase 3 — exploratory

• Persistent agent memory
• Multi-agent coordination
• Remote MCP transport plus a thin client SDK
• An optional, opt-in agent-authored-SQL lane (validate_query / execute) behind an explicit flag. Def-driven compiled SQL stays the default and recommended posture; this lane is for teams that want parse-before-execute over arbitrary agent-emitted SQL, should it land. It is not shipped, and it is not a planned pivot away from the def-driven default.

Everything on this roadmap is open source.

Troubleshooting

The five most common first-run failures. Full troubleshooter in docs/setup/manual.md.

• pip install schemabrain gave me an older version. Check schemabrain --version. If it doesn't match the latest release your pip cache is stale — run pip install --upgrade schemabrain. schemabrain init writes the same version into the Claude Desktop snippet so it stays reproducible across restarts. When you installed from PyPI and uv is on your PATH, the snippet runs uvx schemabrain==<pin> (bump the pin manually after a pip upgrade); otherwise — a non-PyPI install (local wheel, editable, or git checkout) or no uvx — it pins the absolute path of the installed schemabrain entry point, which tracks the environment you ran init from.
• init reports source unreachable. Postgres may not be ready on first run — wait a few seconds and re-run. For your own database, verify host, port, and credentials. Connection URLs in any form are accepted (postgresql://, postgres://, postgresql+psycopg://).
• The first init or schemabrain index hangs for ~60 seconds. Normal. The first index downloads the ONNX embedding model (~67 MB) and makes one LLM call per column. Subsequent runs are fast.
• init fails at stage 6 "wire host". Claude Desktop must be installed first — SchemaBrain writes into its config file, which doesn't exist until Claude Desktop has launched at least once.
• Claude Desktop doesn't show SchemaBrain after restart. Cmd+Q is required (close-window doesn't trigger a re-read of MCP config). Run schemabrain doctor to verify the config landed. If doctor says everything's good but Claude Desktop still doesn't see the tool, check ~/Library/Logs/Claude/mcp*.log.
• Apple Silicon + Python 3.12. fastembed's onnxruntime dependency ships no arm64 wheel for Python 3.12+, so embeddings can't build. init catches this at preflight and tells you to either use Python 3.11 (e.g. pyenv local 3.11.10) or re-run with --no-embed (keyword search instead of semantic — everything else works).

Documentation

FAQ

Does my data leave my machine? Only LLM-enriched column descriptions and the redacted sample values that feed them. Three regex passes (email, US SSN, credit-card-shaped digit runs) run on every sample before it leaves the profiler module — see schemabrain/profiler/stats.py. The Anthropic API call sends column metadata + redacted samples + sibling-column context — no raw rows. Embeddings are generated locally via fastembed (BAAI/bge-small-en-v1.5, ONNX, ~67 MB).

What databases work today? Postgres 16+ is the only source connector today (the local store itself is a SQLite file). A SQLite source connector, plus Snowflake / BigQuery / MySQL, is mostly a new DataSource implementation plus a profiler tweak — on the v1.x roadmap.

Why MCP and not a REST API? The consumer is an agent, not a service. MCP standardizes tool registration, schema description, and request/response transport. Agents discover SchemaBrain natively and get its tool surface — no API wrapper, no SDK to maintain per language.

Is this a semantic layer like Cube or dbt Semantic Layer? Not exactly — SchemaBrain is the trust and intelligence layer between AI agents and your database, built on a semantic-layer substrate. Entities, metrics, and canonical joins are first-class persisted definitions (list_entities, describe_entity, resolve_join, get_metric), and they make the safety primitives possible — read-only-by-architecture, PII-aware refusal, audit chain. The semantic substrate is the foundation; SQL-boundary safety, including the firewall, is one proof-point of the layer, not its whole identity. Full comparison vs Cube / dbt-mcp / Vanna / WrenAI in docs/landscape.md.

More questions answered in docs/setup/manual.md (why local embeddings, more troubleshooting).

Running it on your own Postgres?

If you're pointing AI agents at a real (non-demo) Postgres, I'd genuinely like to hear how it goes — what worked, what broke, what felt sharp or rough. Open a GitHub Discussion or a GitHub issue, or reach me on GitHub (@Arun-kc). Happy to help you wire it up.

Contributors

Contributing & License

PRs welcome. The bar is high — see CONTRIBUTING.md for the test-first / 99%-coverage / conventional-commits / architecture-invariants checklist. CI enforces all of it.

Bugs and feature requests use the structured templates in .github/ISSUE_TEMPLATE/. Issues without a reproduction (bugs) or a clear underlying problem (features) get closed with a request to re-open with the right info.

Apache 2.0.