docs(wording): replace shape with specific terms

CONTRIBUTING.md

@@ -189,7 +189,7 @@ deployment asks for it.
 | `src/deployment.rs`, `src/vector-layer.dockerfile` | `contract()`, the source the Dockerfile and the released chart are generated from, and the two-binary image override spliced onto scalo's generated Dockerfile |
 | `templates/bus.yaml`, `templates/direct.yaml` | Whole runnable Vector topologies, one per transport, commented per field |
 | `pipelines/filebeat/` | The shipped filebeat pipeline |
-| `docs/` | `architecture.md` for the shape and the invariants, `internals.md` for field-by-field depth, `migration.md`, `librdkafka.md`, the generated `config-schema.*` and `capability-catalog.*` |
+| `docs/` | `architecture.md` for why the service is built this way and the invariants, `internals.md` for field-by-field depth, `migration.md`, `librdkafka.md`, the generated `config-schema.*` and `capability-catalog.*` |
 | `tests/` | `integration`, `e2e`, `smoke`, and `TESTING.md` for how the broker and Vector binary are resolved |
 | `scripts/fetch-vector.sh`, `scripts/pgo-workload.sh` | Downloads the pinned Vector for tests, and drives the PGO/BOLT workload |
 
@@ -266,7 +266,7 @@ Outbound -- what depends on this repo:
 Two more relationships that are real but are NOT declared edges in the suite
 graph, so no gate enforces them:
 
-- **dfe-infra `apps.yaml`** is the SSoT for this app's shape -- multiplicity,
+- **dfe-infra `apps.yaml`** is the SSoT for how this app is deployed -- multiplicity,
   scaling, transports, endpoints and which compiler derives its routing.
   dfe-engine reads that manifest, so adding or changing the app is a manifest
   edit, never an engine release.

docs/architecture.md

@@ -12,7 +12,7 @@ already does the per-record transform work well, so building another transform
 engine was never the question. The question was the other half.
 
 Vector on its own is opaque to the platform around it. It has its own config
-shape, its own metrics endpoint, its own process lifecycle and its own idea of
+format, its own metrics endpoint, its own process lifecycle and its own idea of
 what "started" means. Every other DFE app presents one control surface: a
 big-dial YAML in the config registry, one ops port carrying `/livez`, `/readyz`
 and `/metrics`, an OTLP push, a consumer-lag scaling signal and a Helm chart
@@ -55,7 +55,7 @@ question asked of the wrong layer.
 | dfe-engine | What the app IS -- it reads `dfe-infra/apps.yaml`, compiles the Helm values and holds the config in its registry |
 | dfe-infra | The image pin and the GitOps deployment |
 
-Nothing about this app's shape is coded into dfe-engine. It is a `per_config`
+Nothing about how this app is deployed is coded into dfe-engine. It is a `per_config`
 app with `scale_deployed: true`, declared in `dfe-infra/apps.yaml` -- one
 deployment per source config, not a singleton, and KEDA drives the replica
 count. Its `default_in` list names only `docker-single`, so every other deploy

docs/internals.md

@@ -4,7 +4,7 @@
 
 dfe-transform-vector wraps Vector.dev as a managed subprocess to provide **Kafka -> transform -> Kafka** pipelines that the DFE platform manages, deploys and observes the same way as dfe-loader and dfe-receiver.
 
-Vector is powerful but opaque. This wrapper makes it behave like every other DFE service: the same health contract, metrics shape and scaling signal, a chart generated from its deployment contract, and the same Argo CD lifecycle. The app's shape is declared in dfe-infra's `apps.yaml`, not coded into dfe-engine.
+Vector is powerful but opaque. This wrapper makes it behave like every other DFE service: the same health contract, metric names and scaling signal, a chart generated from its deployment contract, and the same Argo CD lifecycle. How the app is deployed is declared in dfe-infra's `apps.yaml`, not coded into dfe-engine.
 
 ---
 
@@ -663,7 +663,7 @@ The mounted config is the chart's `config` with `configOverrides` merged over it
 
 ### 9.3 Parity With dfe-loader/dfe-receiver Charts
 
-All three are generated on the same scalo-service library chart, so the shape is shared:
+All three are generated on the same scalo-service library chart, so they share the same features:
 
 | Feature | dfe-loader | dfe-receiver | dfe-transform-vector |
 |---|---|---|---|
@@ -700,7 +700,7 @@ The app is in the default composition of `docker-single` only. Elsewhere an oper
 
 | Concern | Owner | Notes |
 |---|---|---|
-| App shape | dfe-infra `apps.yaml` | Multiplicity, scaling, transports, source binding |
+| App deployment | dfe-infra `apps.yaml` | Multiplicity, scaling, transports, source binding |
 | Config schema + validation | Wrapper (Rust) | `docs/config-schema.json` is generated from the config structs. dfe-engine judges a stored config against that mounted contract |
 | K8s manifests | Chart generated from `contract()` | On the scalo-service library chart (section 9) |
 | Config assembly (big dials → Vector YAML) | Wrapper (Rust) | The core novel work |