fix(skill): re-inject skill index into local-agent system prompt

The contributor's original PR (#1917) appended an ``Available Skills``
index to the system prompt before the LLM saw the user message, so the
LLM could decide whether to activate a skill. ``7145447b`` removed the
text-marker activation flow and, together with it, the entire system
prompt injection — but the Tool Call replacement only put the available
skills inside the ``activate`` tool's description. In practice the LLM
ignores tool descriptions for selection and goes straight to native
tools, so user-visible skill activation silently broke.

Restore the injection, adapted for the Tool Call era:

- SkillManager regains ``get_skill_index(bound_skills)`` and
  ``build_skill_aware_prompt_addition(bound_skills)``. The addendum
  carries only ``name (display_name): description`` for each
  pipeline-visible skill plus one instruction line pointing at the
  ``activate`` tool. No SKILL.md contents — KV cache stays clean
- PreProcessor appends the addendum to the first system message (or
  inserts a new one) of ``query.prompt.messages`` for the local-agent
  runner. Handles plain-string and ContentElement[] bodies. Skips
  cleanly when no skills are visible
- 3 new test_preproc cases: injection happens, bound-skills subset
  honoured, empty addendum touches nothing. 280 passed

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

src/langbot/pkg/pipeline/preproc/preproc.py

@@ -248,30 +248,67 @@ class PreProcessor(stage.PipelineStage):
         query.prompt.messages = event_ctx.event.default_prompt
         query.messages = event_ctx.event.prompt
 
-        # =========== Store bound skills for runtime visibility checks ===========
-        # Skills are now activated through the `activate` tool (Tool Call mechanism),
-        # not through system prompt injection. This aligns with Claude Code's design.
+        # =========== Skill awareness for the local-agent runner ===========
+        # The actual activation goes through the ``activate`` Tool Call so the
+        # LLM doesn't see full SKILL.md instructions until it commits to a
+        # skill (Claude Code's progressive disclosure). But the LLM still has
+        # to KNOW which skills exist to make that choice, so we:
+        #   1. resolve the pipeline's bound skills and stash them in
+        #      ``query.variables['_pipeline_bound_skills']`` for downstream
+        #      visibility checks (skill loader, native exec workdir);
+        #   2. inject a short ``Available Skills`` index (name + description
+        #      only) into the system prompt. The contributor's original PR
+        #      relied on this injection; without it the LLM never discovers
+        #      the skills are there and just calls native tools instead.
         if selected_runner == 'local-agent' and self.ap.skill_mgr:
-            # Get bound skills from pipeline extensions_preferences
             pipeline_data = await self.ap.pipeline_service.get_pipeline(query.pipeline_uuid)
             extensions_prefs = (pipeline_data or {}).get('extensions_preferences', {})
             enable_all_skills = extensions_prefs.get('enable_all_skills', True)
 
             if enable_all_skills:
-                bound_skills = None  # None = all skills available
+                bound_skills = None  # None = all loaded skills are visible
             else:
-                # Get specific bound skill names
                 bound_skills = extensions_prefs.get('skills', [])
 
-            # Store bound skills in query variables for runtime path visibility checks
             query.variables['_pipeline_bound_skills'] = bound_skills
 
-            loaded_count = len(self.ap.skill_mgr.skills)
+            skill_addition = self.ap.skill_mgr.build_skill_aware_prompt_addition(
+                bound_skills=bound_skills,
+            )
+            if skill_addition:
+                # Append to the first system message; create one if the
+                # prompt has none. Handles both plain-string and
+                # content-element (list) message bodies.
+                if query.prompt.messages and query.prompt.messages[0].role == 'system':
+                    head = query.prompt.messages[0]
+                    if isinstance(head.content, str):
+                        head.content = head.content + skill_addition
+                    elif isinstance(head.content, list):
+                        appended = False
+                        for ce in head.content:
+                            if getattr(ce, 'type', None) == 'text':
+                                ce.text = (ce.text or '') + skill_addition
+                                appended = True
+                                break
+                        if not appended:
+                            head.content.append(provider_message.ContentElement(type='text', text=skill_addition))
+                else:
+                    query.prompt.messages.insert(
+                        0,
+                        provider_message.Message(role='system', content=skill_addition.strip()),
+                    )
                 self.ap.logger.debug(
-                f'Skills available for activate tool: '
+                    f'Skill index injected into system prompt: '
                     f'pipeline={query.pipeline_uuid} '
-                f'loaded_skills={loaded_count} '
-                f'bound_skills={bound_skills or "all"}'
+                    f'bound_skills={bound_skills or "all"} '
+                    f'loaded_skills={len(self.ap.skill_mgr.skills)}'
+                )
+            else:
+                self.ap.logger.debug(
+                    f'No skills available for prompt injection: '
+                    f'pipeline={query.pipeline_uuid} '
+                    f'loaded_skills={len(self.ap.skill_mgr.skills)} '
+                    f'bound_skills={bound_skills}'
                 )
 
         return entities.StageProcessResult(result_type=entities.ResultType.CONTINUE, new_query=query)

src/langbot/pkg/skill/manager.py

@@ -86,3 +86,50 @@ class SkillManager:
     def get_skill_by_name(self, name: str) -> dict | None:
         """Get skill data by name."""
         return self.skills.get(name)
+
+    def get_skill_index(self, bound_skills: list[str] | None = None) -> str:
+        """Render the pipeline-visible skills as a short ``name: description``
+        index suitable for the system prompt.
+
+        ``bound_skills`` follows the same convention as
+        ``query.variables['_pipeline_bound_skills']``: ``None`` means every
+        loaded skill is exposed; an explicit list filters to that subset.
+        Returns an empty string when no skills are visible.
+        """
+        lines: list[str] = []
+        for skill in self.skills.values():
+            name = skill.get('name')
+            if not name:
+                continue
+            if bound_skills is not None and name not in bound_skills:
+                continue
+            display = skill.get('display_name') or name
+            description = (skill.get('description') or '').strip().replace('\n', ' ')
+            lines.append(f'- {name} ({display}): {description}')
+
+        if not lines:
+            return ''
+        return 'Available Skills:\n' + '\n'.join(lines)
+
+    def build_skill_aware_prompt_addition(self, bound_skills: list[str] | None = None) -> str:
+        """Build the system-prompt addendum that makes the LLM aware of the
+        pipeline-visible skills.
+
+        Only metadata (name + description) is injected — the full SKILL.md is
+        loaded later via the ``activate`` Tool Call, protecting KV cache and
+        matching Claude Code's progressive disclosure pattern. Returns an
+        empty string when no skills are visible (no prompt change at all).
+        """
+        skill_index = self.get_skill_index(bound_skills)
+        if not skill_index:
+            return ''
+        return (
+            '\n\n'
+            f'{skill_index}\n\n'
+            "When the user's request clearly matches one or more skills "
+            'based on their descriptions above, call the `activate` tool with '
+            'the skill name to load its full instructions. Only the name and '
+            'description are visible here; the actual instructions arrive as '
+            'the tool result. If no skill is a clear match, respond normally '
+            'without activating any skill.'
+        )

tests/unit_tests/test_preproc.py

@@ -132,3 +132,73 @@ async def test_preproc_disables_skill_authoring_tools_when_skill_service_missing
 
     assert result.result_type == entities_module.ResultType.CONTINUE
     app.tool_mgr.get_all_tools.assert_awaited_once_with(None, None, include_skill_authoring=False)
+
+
+@pytest.mark.asyncio
+async def test_preproc_injects_skill_index_into_system_prompt():
+    """The Tool Call activation pattern still needs the LLM to know which
+    skills exist. PreProcessor must append the SkillManager's index
+    addendum to the first system message."""
+    preproc_module, entities_module = _import_preproc_modules()
+
+    app = _make_app(skill_service=SimpleNamespace())
+    addendum = '\n\nAvailable Skills:\n- demo (demo): Demo skill.\n\nCall activate ...'
+    app.skill_mgr.build_skill_aware_prompt_addition = Mock(return_value=addendum)
+
+    query = _make_query()
+    result = await stage_process_capture(preproc_module, app, query)
+
+    assert result.result_type == entities_module.ResultType.CONTINUE
+    app.skill_mgr.build_skill_aware_prompt_addition.assert_called_once_with(bound_skills=None)
+    head = query.prompt.messages[0]
+    assert head.role == 'system'
+    assert head.content.endswith(addendum)
+
+
+@pytest.mark.asyncio
+async def test_preproc_respects_pipeline_bound_skills_subset():
+    """When ``enable_all_skills`` is false the bound list is passed through
+    so the addendum only mentions skills allowed for this pipeline."""
+    preproc_module, entities_module = _import_preproc_modules()
+
+    app = _make_app(skill_service=SimpleNamespace())
+    app.pipeline_service.get_pipeline = AsyncMock(
+        return_value={
+            'extensions_preferences': {
+                'enable_all_skills': False,
+                'skills': ['only-this'],
+            }
+        }
+    )
+    app.skill_mgr.build_skill_aware_prompt_addition = Mock(return_value='')
+
+    query = _make_query()
+    result = await stage_process_capture(preproc_module, app, query)
+
+    assert result.result_type == entities_module.ResultType.CONTINUE
+    app.skill_mgr.build_skill_aware_prompt_addition.assert_called_once_with(bound_skills=['only-this'])
+    assert query.variables.get('_pipeline_bound_skills') == ['only-this']
+
+
+@pytest.mark.asyncio
+async def test_preproc_skips_injection_when_addendum_is_empty():
+    """No visible skills → system prompt is left untouched (no
+    ``Available Skills`` block appended)."""
+    preproc_module, entities_module = _import_preproc_modules()
+
+    app = _make_app(skill_service=SimpleNamespace())
+    app.skill_mgr.build_skill_aware_prompt_addition = Mock(return_value='')
+
+    query = _make_query()
+    result = await stage_process_capture(preproc_module, app, query)
+
+    assert result.result_type == entities_module.ResultType.CONTINUE
+    if query.prompt and query.prompt.messages:
+        assert 'Available Skills' not in (query.prompt.messages[0].content or '')
+
+
+async def stage_process_capture(preproc_module, app, query):
+    """Run PreProcessor.process and return the result while keeping ``query``
+    accessible to the assertions (process mutates query in place)."""
+    stage = preproc_module.PreProcessor(app)
+    return await stage.process(query, 'PreProcessor')