feat(computer-use): cua-driver backend, universal any-model schema

Background macOS desktop control via cua-driver MCP — does NOT steal the
user's cursor or keyboard focus, works with any tool-capable model.

Replaces the Anthropic-native `computer_20251124` approach from the
abandoned #4562 with a generic OpenAI function-calling schema plus SOM
(set-of-mark) captures so Claude, GPT, Gemini, and open models can all
drive the desktop via numbered element indices.

- `tools/computer_use/` package — swappable ComputerUseBackend ABC +
  CuaDriverBackend (stdio MCP client to trycua/cua's cua-driver binary).
- Universal `computer_use` tool with one schema for all providers.
  Actions: capture (som/vision/ax), click, double_click, right_click,
  middle_click, drag, scroll, type, key, wait, list_apps, focus_app.
- Multimodal tool-result envelope (`_multimodal=True`, OpenAI-style
  `content: [text, image_url]` parts) that flows through
  handle_function_call into the tool message. Anthropic adapter converts
  into native `tool_result` image blocks; OpenAI-compatible providers
  get the parts list directly.
- Image eviction in convert_messages_to_anthropic: only the 3 most
  recent screenshots carry real image data; older ones become text
  placeholders to cap per-turn token cost.
- Context compressor image pruning: old multimodal tool results have
  their image parts stripped instead of being skipped.
- Image-aware token estimation: each image counts as a flat 1500 tokens
  instead of its base64 char length (~1MB would have registered as
  ~250K tokens before).
- COMPUTER_USE_GUIDANCE system-prompt block — injected when the toolset
  is active.
- Session DB persistence strips base64 from multimodal tool messages.
- Trajectory saver normalises multimodal messages to text-only.
- `hermes tools` post-setup installs cua-driver via the upstream script
  and prints permission-grant instructions.
- CLI approval callback wired so destructive computer_use actions go
  through the same prompt_toolkit approval dialog as terminal commands.
- Hard safety guards at the tool level: blocked type patterns
  (curl|bash, sudo rm -rf, fork bomb), blocked key combos (empty trash,
  force delete, lock screen, log out).
- Skill `apple/macos-computer-use/SKILL.md` — universal (model-agnostic)
  workflow guide.
- Docs: `user-guide/features/computer-use.md` plus reference catalog
  entries.

44 new tests in tests/tools/test_computer_use.py covering schema
shape (universal, not Anthropic-native), dispatch routing, safety
guards, multimodal envelope, Anthropic adapter conversion, screenshot
eviction, context compressor pruning, image-aware token estimation,
run_agent helpers, and universality guarantees.

469/469 pass across tests/tools/test_computer_use.py + the affected
agent/ test suites.

- `model_tools.py` provider-gating: the tool is available to every
  provider. Providers without multi-part tool message support will see
  text-only tool results (graceful degradation via `text_summary`).
- Anthropic server-side `clear_tool_uses_20250919` — deferred;
  client-side eviction + compressor pruning cover the same cost ceiling
  without a beta header.

- macOS only. cua-driver uses private SkyLight SPIs
  (SLEventPostToPid, SLPSPostEventRecordTo,
  _AXObserverAddNotificationAndCheckRemote) that can break on any macOS
  update. Pin with HERMES_CUA_DRIVER_VERSION.
- Requires Accessibility + Screen Recording permissions — the post-setup
  prints the Settings path.

Supersedes PR #4562 (pyautogui/Quartz foreground backend, Anthropic-
native schema). Credit @0xbyt4 for the original #3816 groundwork whose
context/eviction/token design is preserved here in generic form.

agent/context_compressor.py

@@ -150,6 +150,31 @@ def _append_text_to_content(content: Any, text: str, *, prepend: bool = False) -
     return text + rendered if prepend else rendered + text
 
 
+def _strip_image_parts_from_parts(parts: Any) -> Any:
+    """Strip image parts from an OpenAI-style content-parts list.
+
+    Returns a new list with image_url / image / input_image parts replaced
+    by a text placeholder, or None if the list had no images (callers
+    skip the replacement in that case). Used by the compressor to prune
+    old computer_use screenshots.
+    """
+    if not isinstance(parts, list):
+        return None
+    had_image = False
+    out = []
+    for part in parts:
+        if not isinstance(part, dict):
+            out.append(part)
+            continue
+        ptype = part.get("type")
+        if ptype in ("image", "image_url", "input_image"):
+            had_image = True
+            out.append({"type": "text", "text": "[screenshot removed to save context]"})
+        else:
+            out.append(part)
+    return out if had_image else None
+
+
 def _truncate_tool_call_args_json(args: str, head_chars: int = 200) -> str:
     """Shrink long string values inside a tool-call arguments JSON blob while
     preserving JSON validity.
@@ -578,10 +603,12 @@ class ContextCompressor(ContextEngine):
             if msg.get("role") != "tool":
                 continue
             content = msg.get("content") or ""
-            # Skip multimodal content (list of content blocks)
+            # Multimodal content — dedupe by the text summary if available.
             if isinstance(content, list):
                 continue
             if not isinstance(content, str):
+                # Multimodal dict envelopes ({_multimodal: True, content: [...]}) and
+                # other non-string tool-result shapes can't be hashed/deduped by text.
                 continue
             if len(content) < 200:
                 continue
@@ -599,8 +626,20 @@ class ContextCompressor(ContextEngine):
             if msg.get("role") != "tool":
                 continue
             content = msg.get("content", "")
-            # Skip multimodal content (list of content blocks)
+            # Multimodal content (base64 screenshots etc.): strip the image
+            # payload — keep a lightweight text placeholder in its place.
+            # Without this, an old computer_use screenshot (~1MB base64 +
+            # ~1500 real tokens) survives every compression pass forever.
             if isinstance(content, list):
+                stripped = _strip_image_parts_from_parts(content)
+                if stripped is not None:
+                    result[i] = {**msg, "content": stripped}
+                    pruned += 1
+                continue
+            if isinstance(content, dict) and content.get("_multimodal"):
+                summary = content.get("text_summary") or "[screenshot removed to save context]"
+                result[i] = {**msg, "content": f"[screenshot removed] {summary[:200]}"}
+                pruned += 1
                 continue
             if not isinstance(content, str):
                 continue