refactor(token-meter): simplify singleton service (round 1)

packages/llm/token-meter/README.md

@@ -1,29 +1,26 @@
 # @deepseek-ai/dsh-token-meter
 
-Replay-aware token measurement through `ctx.tokenMeter`. The service binds one stable meter to each configured model and advances isolated per-model/per-session folds from the durable session log. Compaction consumes it today; other pressure-sensitive plugins can reuse the same accounting without depending on `CompactService`.
+Replay-aware token measurement through the singleton `ctx.tokenMeter` service. It advances one isolated fold per session from the durable log, so compaction and other pressure-sensitive plugins can share accounting without depending on `CompactService`.
 
-## Profiles and configuration
-
-The built-in `deepseek-v4-flash` and `deepseek-v4-pro` profiles each use a 128,000-token context window and four characters per estimated token. `models` merges overrides field-by-field, so changing only density keeps the built-in window. A custom model requires `contextWindow`; its `charsPerToken` defaults to `4`.
+## Configuration
 
 | Key | Default | Contract |
 |---|---:|---|
-| `models.<built-in>.contextWindow` | `128000` | Positive integer provider capacity. |
-| `models.<model>.charsPerToken` | `4` | Positive finite heuristic density. |
+| `contextWindow` | `128000` | Positive integer service-wide context capacity. |
 
-Resolving an unknown model throws `TokenMeterError` with code `TOKEN_METER_MODEL_UNCONFIGURED` and preserves the exact model name. Direct-construction profile validation uses `TOKEN_METER_INVALID_CONFIG`; Loader mounts first apply the package's Schemastery shape validation. There is no universal fallback window.
+The estimator intentionally uses one fixed heuristic: four characters per token plus structural overhead for roles, blocks, and request-envelope fields. `contextWindow` is the only deployment setting. Direct construction validates it; Loader mounts first apply the package's Schemastery shape validation.
 
 ## Measurement contract
 
-`ctx.tokenMeter.resolve(model)` returns a `ModelTokenMeter` with three operations:
+`ctx.tokenMeter` directly exposes three operations:
 
 - `measure(session, requestHeader?)` returns scalar request pressure at one consumed-log revision.
 - `measureSurface(session)` returns current surface nodes and their per-node prices at the same kind of revision.
-- `estimateMessage(message)` prices one detached message under that profile.
+- `estimateMessage(message)` prices one message with the fixed heuristic.
 
 Measurements are detached and deeply immutable. A caller that needs a consistent scalar/surface decision compares their `logRevision` values instead of copying the full history on every read.
 
-The fold tracks request headers and deltas, step boundaries, surface appends and replacements, successful assistant messages, provider usage, and assistant-chunk provenance. Provider usage is reused only when the handle's model and the canonical request envelope match the successful-call anchor. Otherwise the complete current envelope and surface are repriced under the requested model. Surface changes remain signed relative to a matching anchor, including negative deltas after shrinking replacements.
+The fold tracks request headers and deltas, step boundaries, surface appends and replacements, successful assistant messages, provider usage, and assistant-chunk provenance. Provider usage is reused only when the latest successful call's canonical request envelope matches the measured envelope; a later success replaces the earlier anchor. Otherwise the complete current envelope and surface are estimated. Surface changes remain signed relative to a matching anchor, including negative deltas after shrinking replacements.
 
 Usage accounting sums disjoint input, cache-read, cache-write, and output buckets; reasoning is not added again. Every successful call records an assistant anchor, including content-less calls. An explicit empty provenance list means a known empty provider stream, while absent legacy provenance conservatively treats the durable assistant output as provider output.
 
@@ -34,16 +31,12 @@ Usage accounting sums disjoint input, cache-read, cache-write, and output bucket
 - name: '@deepseek-ai/dsh-compact-basic'
 ```
 
-Both plugins have usable defaults for the bundled DeepSeek profiles. Custom deployments can override only the fields that differ:
+Both plugins have usable defaults. A deployment with a different capacity configures the meter once:
 
 ```yaml
 - name: '@deepseek-ai/dsh-token-meter'
   config:
-    models:
-      deepseek-v4-flash:
-        charsPerToken: 2
-      local-model:
-        contextWindow: 32768
+    contextWindow: 32768
 ```
 
 ## Model Experience
@@ -52,6 +45,6 @@ Indirectly, through consumers such as `dsh-compact-basic`; the service itself ad
 
 ## Known Limitations and Deferred Work
 
-- **Heuristic density still needs maintenance** — message content without provider usage is priced by configured character density plus structural overhead, not an exact provider tokenizer. CJK-heavy or provider-specific formats may need profile overrides.
-- **Provider usage is only reusable for an identical canonical envelope** — prompt, prefix, tools, or call-config changes deliberately fall back to full heuristic repricing.
+- **The fixed heuristic is approximate** — content without reusable provider usage is priced by character count plus structural overhead, not an exact provider tokenizer or request serializer.
+- **Provider usage is only reusable for an identical canonical envelope** — prompt, prefix, tools, model, or call-config changes deliberately fall back to full heuristic estimation.
 - **Legacy provenance is conservative** — assistant messages without `sourceEventSeqs` cannot distinguish provider output from listener rewrites, so the fold avoids claiming a known empty or exact chunk stream.