🎬 CLI demos

14 frame-perfect SVG casts — install through deploy, local through Snowflake, the AI copilot, agentPolicy enforcement, day-2 incident response, and agent-loop compaction. Each one carries a takeaway popup with the punchline numbers. Click play — the SVG only animates after you opt in (no autoplay, no JS).

Convinced? → Install in 30 seconds. Want longer-form proof of specific workflows? → See it run (5 narrative scenarios, ~50 s each, with takeaway numbers).

Install + run, locally

Start here. No cloud account, no credit card, ~30 seconds end-to-end.

fluid init my-project --quickstart → validate → plan → apply

Install the CLI, scaffold the Customer 360 Analytics contract from the quickstart template, validate it against the 0.7.2 schema, preview the plan, and apply it against the local DuckDB provider. Two exposes — a master table and a high-value-customer view — land as Parquet under output/.

Same contract, different cloud

Swap binding.platform and re-deploy. The contract, schema, dq.rules, and the multi-stage build all stay byte-identical — only the cloud-specific binding fields change.

GCP / BigQuery

GCP quickstart — install, swap one line, deploy

From the local Customer 360 contract to a fully-deployed BigQuery dataset, in seconds. The `git diff` shows only the binding block changing — schema, dq.rules, and the 5-stage build all stay byte-identical.

AWS / Athena

AWS quickstart — S3 + Glue + Athena

Same Customer 360 contract, AWS provider extra installed, binding swapped to the AWS platform. S3 bucket provisioned, Glue catalog auto-created, Athena made queryable — all from fluid apply.

Snowflake

Snowflake quickstart — dry-run flow

The dry version: env-file credentials, contract validation, plan preview, and apply --mode dry-run rendering DDL without firing it. For the live-auth version see snowflake-real below.

AI copilot — full Gemini-powered flow

The fluid forge AI copilot generating a finance-domain contract end-to-end: project memory loaded, finance domain expertise pack applied (SOX + GDPR), local context discovered, a Gemini streaming call, and the contract emerging block-by-block with the agentPolicy gate. The hand-scripted version below mirrors the real-API flow at frame-perfect fidelity; a real-capture script (scripts/demos/forge_gemini_real_capture.py) is preserved for users who want to record an actual session.

fluid forge --domain finance --llm-provider gemini --llm-model gemini-2.5-flash

The full agent flow: memory → domain pack → discovery → streaming → contract emit (schema, dq.rules, accessPolicy, agentPolicy, sovereignty) → auto-validation → memory persist.

Snowflake live-auth dry-run

The snowflake-biz-lab flow at full fidelity: env credentials sourced, real validate --strict, plan against the live account, apply --mode dry-run rendering DDL without firing it, then policy-apply --mode check over the compiled IAM bindings.

Snowflake — validate → plan → apply --mode dry-run → policy-apply --mode check

Live auth (account=acme-demo placeholder; the scrubber substitutes the real account name). No DDL fires, no RBAC mutates — just the auth + connectivity + dry-render flow you'd run before a real production deploy.

AI copilot — interview shape only

fluid forge --blank skips the LLM call entirely and just scaffolds the structured stub for the chosen domain. Useful when you know what you want and don't need an LLM round-trip.

fluid forge --blank --domain finance

The blank skeleton with finance-domain defaults pre-seeded: SOX/GDPR regulatory framework, 'training' and 'fine_tuning' denied use cases, Gold layer assignment. Fill in the expose blocks yourself — no LLM call.

Policy + IAM compilation

The policy-check → generate artifacts → policy-apply --mode check triple. Validates the access policy, compiles to native cloud IAM (BigQuery/Snowflake/AWS), and runs the bindings in check-only mode (no live IAM mutations).

policy-check → generate artifacts → policy-apply --mode check

Three commands, full policy round-trip from declarative `accessPolicy.grants` in YAML to native cloud IAM JSON, then a dry-run that shows exactly which bindings would apply against the deployed state.

agentPolicy enforcement (LLM / AI governance)

Declare agentPolicy in YAML, validate it, see the enforcement summary, watch a replay of agent reads (allow/deny) against the policy.

agentPolicy — declare, validate, gate (validate → policy-check → audit)

The YAML block (allowedModels, deniedUseCases, canStore, auditRequired) → validate → policy-check enforcement summary → 4 replayed agent reads (gpt-4 allowed, claude-3 + training denied, unlisted model denied, gemini summarization allowed).

Long-form scenario casts

The 5 casts below pair with the See it run page — each tells a story (problem → CLI flow → punchline) at ~30-50 seconds with takeaway numbers.

`$0.03` per data product — three providers, one contract

fluid forge data-model — same intent, three providers, real cost figures

Eight lines of intent YAML. Anthropic, OpenAI, Ollama — same flag swapped each time. All three emit the same valid contract, the same dbt project layout. Real production token counts and costs.

Six months → sixty seconds — source-aligned Bronze

fluid init --discover postgres://... — Bronze contract in 60 seconds

Connect, scan information_schema (47 ms), infer 28 tables / 143 columns / 12 PII candidates / 8 foreign keys, emit a complete Bronze contract.fluid.yaml, validate, apply against embedded DuckDB. 6.2 seconds total. Then show the engine swap path.

23 questions, skipped — guided UX

fluid forge — guided UX in action

47 ms welcome scan finds 3 CSVs + 2 dbt models + 1 README, infers domain (finance) and PII (5 columns). 5-mode picker. 4 questions answered (most accept the inferred default with ↵). Cost-cap progress in real time ($0.000 → $0.021 of $0.050 cap). Pre-write panel shows exactly what will + won't change.

3am Slack ping → ship in 90 seconds

PagerDuty: freshness SLA breached. fluid runs status shows 3 consecutive failures. fluid runs logs --component dlq surfaces the root cause. fluid runs diff shows what changed since the last OK run. One-line contract fix. fluid ship. 87 seconds end-to-end. Recovered 12,361 rows.

`$0.50` → `$0.05` — agent-loop compaction

Agent-loop compaction — three strategies, real before/after costs

20-turn baseline: $0.503/run, super-linear context bloat (5K → 67K → 298K tokens). Three strategies side-by-side: truncate (5.8× cheaper), summarize (9.3× cheaper), hybrid (10.5× cheaper). One env var: FLUID_COMPACTION_STRATEGY=hybrid.

How the casts are produced

The pipeline that built each SVG above:

   scripts/demos/<name>.py            ← cast generator
                ↓
        /tmp/casts/<name>.cast.raw    ← raw asciinema cast (gitignored)
                ↓
        scripts/cast-v3-to-v2.py      ← format conversion (asciinema 3.x → 2.x)
                ↓
        scripts/scrub-cast.py         ← strip API keys, JWTs, env-shaped secrets
                ↓
        svg-term --in <cast>          ← render to animated SVG (no --window;
                                          our <CliCast> component supplies
                                          the terminal chrome)
                ↓
   docs/.vuepress/public/demos/<name>.svg   ← the only file that gets committed

Two passes of secret-scanning happen:

scrub-cast.py redacts known formats (AIza…, sk-…, sk-ant-…, JWTs, KEY=…/SECRET=…/PASSWORD=… 16+ char values) and substitutes literal $SNOWFLAKE_ACCOUNT/$SNOWFLAKE_USER/$GEMINI_API_KEY env values for friendly placeholders.
Final-SVG grep in generate-demos.sh re-scans the output before keeping the file. If any leak pattern matches in the post-scrub SVG, the file is deleted and the build fails.

The .cast.raw working files live in /tmp/casts/ (gitignored) and are deleted at the end of each render.

To regenerate everything:

scripts/generate-demos.sh                 # regenerate every cast
scripts/generate-demos.sh --safe-only     # only the credential-free casts
scripts/generate-demos.sh forge-gemini    # one specific cast

Source for each cast lives at scripts/demos/<name>.py — review or fork freely.