fix: satisfy lint curly rule (#6310)

* fix: satisfy lint curly rule

* docs: apply oxfmt formatting

docs/.i18n/README.md

@@ -21,6 +21,7 @@ This folder stores **generated** and **config** files for documentation translat
 ```
 
 Fields:
+
 - `source`: English (or source) phrase to prefer.
 - `target`: preferred translation output.
 

docs/concepts/session-pruning.md

@@ -4,29 +4,34 @@ read_when:
   - You want to reduce LLM context growth from tool outputs
   - You are tuning agents.defaults.contextPruning
 ---
+
 # Session Pruning
 
 Session pruning trims **old tool results** from the in-memory context right before each LLM call. It does **not** rewrite the on-disk session history (`*.jsonl`).
 
 ## When it runs
+
 - When `mode: "cache-ttl"` is enabled and the last Anthropic call for the session is older than `ttl`.
 - Only affects the messages sent to the model for that request.
- - Only active for Anthropic API calls (and OpenRouter Anthropic models).
+- Only active for Anthropic API calls (and OpenRouter Anthropic models).
- - For best results, match `ttl` to your model `cacheControlTtl`.
+- For best results, match `ttl` to your model `cacheControlTtl`.
- - After a prune, the TTL window resets so subsequent requests keep cache until `ttl` expires again.
+- After a prune, the TTL window resets so subsequent requests keep cache until `ttl` expires again.
 
 ## Smart defaults (Anthropic)
+
 - **OAuth or setup-token** profiles: enable `cache-ttl` pruning and set heartbeat to `1h`.
 - **API key** profiles: enable `cache-ttl` pruning, set heartbeat to `30m`, and default `cacheControlTtl` to `1h` on Anthropic models.
 - If you set any of these values explicitly, OpenClaw does **not** override them.
 
 ## What this improves (cost + cache behavior)
+
 - **Why prune:** Anthropic prompt caching only applies within the TTL. If a session goes idle past the TTL, the next request re-caches the full prompt unless you trim it first.
 - **What gets cheaper:** pruning reduces the **cacheWrite** size for that first request after the TTL expires.
 - **Why the TTL reset matters:** once pruning runs, the cache window resets, so follow‑up requests can reuse the freshly cached prompt instead of re-caching the full history again.
 - **What it does not do:** pruning doesn’t add tokens or “double” costs; it only changes what gets cached on that first post‑TTL request.
 
 ## What can be pruned
+
 - Only `toolResult` messages.
 - User + assistant messages are **never** modified.
 - The last `keepLastAssistants` assistant messages are protected; tool results after that cutoff are not pruned.
@@ -34,35 +39,43 @@ Session pruning trims **old tool results** from the in-memory context right befo
 - Tool results containing **image blocks** are skipped (never trimmed/cleared).
 
 ## Context window estimation
+
 Pruning uses an estimated context window (chars ≈ tokens × 4). The base window is resolved in this order:
-1) `models.providers.*.models[].contextWindow` override.
+
-2) Model definition `contextWindow` (from the model registry).
+1. `models.providers.*.models[].contextWindow` override.
-3) Default `200000` tokens.
+2. Model definition `contextWindow` (from the model registry).
+3. Default `200000` tokens.
 
 If `agents.defaults.contextTokens` is set, it is treated as a cap (min) on the resolved window.
 
 ## Mode
+
 ### cache-ttl
+
 - Pruning only runs if the last Anthropic call is older than `ttl` (default `5m`).
 - When it runs: same soft-trim + hard-clear behavior as before.
 
 ## Soft vs hard pruning
+
 - **Soft-trim**: only for oversized tool results.
   - Keeps head + tail, inserts `...`, and appends a note with the original size.
   - Skips results with image blocks.
 - **Hard-clear**: replaces the entire tool result with `hardClear.placeholder`.
 
 ## Tool selection
+
 - `tools.allow` / `tools.deny` support `*` wildcards.
 - Deny wins.
 - Matching is case-insensitive.
 - Empty allow list => all tools allowed.
 
 ## Interaction with other limits
+
 - Built-in tools already truncate their own output; session pruning is an extra layer that prevents long-running chats from accumulating too much tool output in the model context.
 - Compaction is separate: compaction summarizes and persists, pruning is transient per request. See [/concepts/compaction](/concepts/compaction).
 
 ## Defaults (when enabled)
+
 - `ttl`: `"5m"`
 - `keepLastAssistants`: `3`
 - `softTrimRatio`: `0.3`
@@ -72,33 +85,37 @@ If `agents.defaults.contextTokens` is set, it is treated as a cap (min) on the r
 - `hardClear`: `{ enabled: true, placeholder: "[Old tool result content cleared]" }`
 
 ## Examples
+
 Default (off):
+
 ```json5
 {
   agent: {
-    contextPruning: { mode: "off" }
+    contextPruning: { mode: "off" },
-  }
+  },
 }
 ```
 
 Enable TTL-aware pruning:
+
 ```json5
 {
   agent: {
-    contextPruning: { mode: "cache-ttl", ttl: "5m" }
+    contextPruning: { mode: "cache-ttl", ttl: "5m" },
-  }
+  },
 }
 ```
 
 Restrict pruning to specific tools:
+
 ```json5
 {
   agent: {
     contextPruning: {
       mode: "cache-ttl",
-      tools: { allow: ["exec", "read"], deny: ["*image*"] }
+      tools: { allow: ["exec", "read"], deny: ["*image*"] },
-    }
+    },
-  }
+  },
 }
 ```
 

src/agents/context-window-guard.ts

@@ -11,7 +11,9 @@ export type ContextWindowInfo = {
 };
 
 function normalizePositiveInt(value: unknown): number | null {
-  if (typeof value !== "number" || !Number.isFinite(value)) return null;
+  if (typeof value !== "number" || !Number.isFinite(value)) {
+    return null;
+  }
   const int = Math.floor(value);
   return int > 0 ? int : null;
 }