feat: add skill prompt

private_gpt/chat/input_models.py

@@ -109,6 +109,13 @@ class PromptConfig(BaseModel):
             "available paths) when any code execution tool is present."
         ),
     )
+    skills: bool = Field(
+        default=False,
+        description=(
+            "Enable skill management instructions (when to load/unload skills, "
+            "workflow guidance) when any skill management tool is present."
+        ),
+    )
 
 
 class System(BaseModel):

private_gpt/components/prompts/prompt_builder.py

@@ -554,6 +554,29 @@ class PromptBuilderService:
             logger.warning("PromptBuilder: failed to render %s: %s", template_path, exc)
             return PromptTemplate(template="")
 
+    def create_skills_prompt(
+        self,
+        tools: list["ToolSpec"],
+        few_shots: bool = False,
+    ) -> BasePromptTemplate:
+        """Create the skill management instructions prompt.
+
+        Renders ``chat/tools/skills.j2`` with the active skill tool namespace.
+        Returns an empty template when the template is missing or rendering fails.
+        """
+        namespace = _build_tool_namespace(tools)
+        template_path = "chat/tools/skills.j2"
+        try:
+            template = self.template_service.get_template(template_path)
+            rendered = template.render(
+                namespace=namespace,
+                few_shots=str(few_shots),
+            )
+            return PromptTemplate(template=rendered.strip())
+        except Exception as exc:
+            logger.warning("PromptBuilder: failed to render %s: %s", template_path, exc)
+            return PromptTemplate(template="")
+
     def create_thinking_guidelines(self, few_shots: bool = True) -> BasePromptTemplate:
         """Create the thinking/reasoning guidelines prompt.
 

private_gpt/components/prompts/templates/chat/tools/skills.j2

@@ -0,0 +1,30 @@
+{% set list_tool = namespace.tools.get("list_skills_tool_name") %}
+{% set load_tool = namespace.tools.get("load_skill_tool_name") %}
+{% set unload_tool = namespace.tools.get("unload_skill_tool_name") %}
+<skills>
+Skills are curated instruction sets that give you specialised knowledge and workflows for specific domains. Use them whenever the user's request falls within a skill's area of expertise.
+
+**When to use skills:**
+- The user's request mentions a topic or task that a skill might cover.
+- A task requires domain-specific steps, tools, or conventions you wouldn't know otherwise.
+- The user explicitly asks you to use a skill.
+
+**Workflow:**
+1. Call `{{ list_tool }}` to see available skills and their descriptions.
+2. Call `{{ load_tool }}` with the skill name that best matches the request.
+3. Follow the instructions returned by `{{ load_tool }}` when completing the task.
+4. Call `{{ unload_tool }}` when the task is done and the skill is no longer needed.
+
+**Rules:**
+- Always list skills before loading to confirm the name is correct.
+- Load at most one skill at a time unless the task clearly spans multiple domains.
+- If no skill matches the request, proceed without loading any.
+- Never invent skill names; only use names returned by `{{ list_tool }}`.
+
+{% if few_shots == "True" %}
+Examples:
+- User asks to "analyse this dataset using the data-science skill" → call `{{ list_tool }}`, then `{{ load_tool }}` with name `data-science`.
+- User asks a general question unrelated to any skill → skip skill tools and answer directly.
+- After finishing a skill-guided task → call `{{ unload_tool }}` with the skill name.
+{% endif %}
+</skills>

private_gpt/server/chat/interceptors/platform_guidelines_interceptor.py

@@ -19,7 +19,10 @@ from private_gpt.components.engines.chat_loop.models.chat_loop_phase import (
 )
 from private_gpt.components.engines.citations.types import Document
 from private_gpt.components.prompts.prompt_builder import PromptBuilderService
-from private_gpt.components.tools.tool_names import CODE_EXECUTION_INTERNAL_TOOLS
+from private_gpt.components.tools.tool_names import (
+    CODE_EXECUTION_INTERNAL_TOOLS,
+    SKILL_MANAGEMENT_TOOLS,
+)
 
 if TYPE_CHECKING:
     from private_gpt.chat.input_models import PromptConfig
@@ -31,6 +34,7 @@ _SOURCE_TOOL_INSTRUCTIONS = "platform:tool_instructions"
 _SOURCE_CITATIONS = "platform:citations"
 _SOURCE_THINKING = "platform:thinking"
 _SOURCE_CODE_EXECUTION = "platform:code_execution"
+_SOURCE_SKILLS = "platform:skills"
 
 _ALL_PLATFORM_SOURCES = frozenset(
     {
@@ -38,6 +42,7 @@ _ALL_PLATFORM_SOURCES = frozenset(
         _SOURCE_CITATIONS,
         _SOURCE_THINKING,
         _SOURCE_CODE_EXECUTION,
+        _SOURCE_SKILLS,
     }
 )
 
@@ -86,6 +91,10 @@ class PlatformGuidelinesInterceptor(ChatRequestLoopInterceptor):
         if prompt.code_execution and self._has_code_execution_tool(tools):
             stack = self._inject_code_execution(stack, tools)
 
+        # 5. Skill management instructions
+        if prompt.skills and self._has_skill_management_tool(tools):
+            stack = self._inject_skills(stack, tools)
+
         state.input.context_stack = stack
         context.set_state(state)
 
@@ -154,6 +163,20 @@ class PlatformGuidelinesInterceptor(ChatRequestLoopInterceptor):
             )
         return stack
 
+    def _inject_skills(
+        self, stack: ContextStack, tools: list[ToolSpec]
+    ) -> ContextStack:
+        content = self._prompt_builder.create_skills_prompt(tools).format()
+        if content:
+            stack = stack.append_layer(
+                ToolInstructionsLayer(
+                    tool_name="skills",
+                    instructions=content,
+                    source=_SOURCE_SKILLS,
+                )
+            )
+        return stack
+
     @staticmethod
     def _has_code_execution_tool(tools: list[ToolSpec]) -> bool:
         for tool in tools:
@@ -165,6 +188,17 @@ class PlatformGuidelinesInterceptor(ChatRequestLoopInterceptor):
                 return True
         return False
 
+    @staticmethod
+    def _has_skill_management_tool(tools: list[ToolSpec]) -> bool:
+        for tool in tools:
+            try:
+                canonical = tool.get_original_tool_name()
+            except ValueError:
+                canonical = tool.name or ""
+            if canonical in SKILL_MANAGEMENT_TOOLS:
+                return True
+        return False
+
     # ------------------------------------------------------------------
     # Cached content loaders
     # ------------------------------------------------------------------