RC-CHN
73e665bef7
Add explicit Neo lifecycle instructions to the main agent prompt so skill creation and updates follow payload -> candidate -> promotion instead of direct local folder writes. Clarify lifecycle tool descriptions and parameter semantics, including skill_key/source_execution_ids usage and stable release sync_to_local behavior, to reduce ambiguity and improve consistent skill publishing.
543 lines
17 KiB
Python
543 lines
17 KiB
Python
import json
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from collections.abc import Awaitable, Callable
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from dataclasses import dataclass, field
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from typing import Any
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from astrbot.api import FunctionTool
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from astrbot.core.agent.run_context import ContextWrapper
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from astrbot.core.agent.tool import ToolExecResult
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from astrbot.core.astr_agent_context import AstrAgentContext
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from astrbot.core.skills.neo_skill_sync import NeoSkillSyncManager
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from ..computer_client import get_booter
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def _to_jsonable(model_like: Any) -> Any:
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if isinstance(model_like, dict):
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return model_like
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if isinstance(model_like, list):
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return [_to_jsonable(i) for i in model_like]
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if hasattr(model_like, "model_dump"):
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return _to_jsonable(model_like.model_dump())
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return model_like
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def _to_json_text(data: Any) -> str:
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return json.dumps(_to_jsonable(data), ensure_ascii=False, default=str)
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def _ensure_admin(context: ContextWrapper[AstrAgentContext]) -> str | None:
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if context.context.event.role != "admin":
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return "error: Permission denied. Skill lifecycle tools are only allowed for admin users."
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return None
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async def _get_neo_context(
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context: ContextWrapper[AstrAgentContext],
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) -> tuple[Any, Any]:
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booter = await get_booter(
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context.context.context,
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context.context.event.unified_msg_origin,
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)
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client = getattr(booter, "bay_client", None)
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sandbox = getattr(booter, "sandbox", None)
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if client is None or sandbox is None:
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raise RuntimeError(
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"Current sandbox booter does not support Neo skill lifecycle APIs. "
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"Please switch to shipyard_neo."
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)
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return client, sandbox
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@dataclass
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class NeoSkillToolBase(FunctionTool):
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error_prefix: str = "Error"
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async def _run(
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self,
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context: ContextWrapper[AstrAgentContext],
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neo_call: Callable[[Any, Any], Awaitable[Any]],
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error_action: str,
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) -> ToolExecResult:
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if err := _ensure_admin(context):
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return err
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try:
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client, sandbox = await _get_neo_context(context)
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result = await neo_call(client, sandbox)
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return _to_json_text(result)
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except Exception as e:
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return f"{self.error_prefix} {error_action}: {str(e)}"
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@dataclass
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class GetExecutionHistoryTool(NeoSkillToolBase):
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name: str = "astrbot_get_execution_history"
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description: str = "Get execution history from current sandbox."
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parameters: dict = field(
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default_factory=lambda: {
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"type": "object",
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"properties": {
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"exec_type": {"type": "string"},
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"success_only": {"type": "boolean", "default": False},
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"limit": {"type": "integer", "default": 100},
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"offset": {"type": "integer", "default": 0},
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"tags": {"type": "string"},
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"has_notes": {"type": "boolean", "default": False},
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"has_description": {"type": "boolean", "default": False},
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},
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"required": [],
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}
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)
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async def call(
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self,
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context: ContextWrapper[AstrAgentContext],
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exec_type: str | None = None,
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success_only: bool = False,
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limit: int = 100,
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offset: int = 0,
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tags: str | None = None,
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has_notes: bool = False,
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has_description: bool = False,
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) -> ToolExecResult:
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return await self._run(
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context,
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lambda _client, sandbox: sandbox.get_execution_history(
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exec_type=exec_type,
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success_only=success_only,
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limit=limit,
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offset=offset,
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tags=tags,
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has_notes=has_notes,
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has_description=has_description,
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),
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error_action="getting execution history",
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)
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@dataclass
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class AnnotateExecutionTool(NeoSkillToolBase):
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name: str = "astrbot_annotate_execution"
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description: str = "Annotate one execution history record."
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parameters: dict = field(
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default_factory=lambda: {
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"type": "object",
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"properties": {
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"execution_id": {"type": "string"},
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"description": {"type": "string"},
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"tags": {"type": "string"},
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"notes": {"type": "string"},
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},
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"required": ["execution_id"],
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}
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)
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async def call(
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self,
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context: ContextWrapper[AstrAgentContext],
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execution_id: str,
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description: str | None = None,
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tags: str | None = None,
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notes: str | None = None,
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) -> ToolExecResult:
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return await self._run(
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context,
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lambda _client, sandbox: sandbox.annotate_execution(
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execution_id=execution_id,
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description=description,
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tags=tags,
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notes=notes,
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),
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error_action="annotating execution",
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)
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@dataclass
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class CreateSkillPayloadTool(NeoSkillToolBase):
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name: str = "astrbot_create_skill_payload"
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description: str = (
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"Step 1/3 for Neo skill authoring: create immutable payload content and return payload_ref. "
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"Use this to store skill_markdown and structured metadata; do NOT write local skill folders directly."
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)
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parameters: dict = field(
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default_factory=lambda: {
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"type": "object",
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"properties": {
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"payload": {
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"anyOf": [{"type": "object"}, {"type": "array"}],
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"description": (
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"Skill payload JSON. Typical schema: {skill_markdown, inputs, outputs, meta}. "
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"This only stores content and returns payload_ref; it does not create a candidate or release."
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),
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},
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"kind": {
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"type": "string",
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"description": "Payload kind.",
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"default": "astrbot_skill_v1",
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},
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},
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"required": ["payload"],
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}
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)
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async def call(
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self,
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context: ContextWrapper[AstrAgentContext],
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payload: dict[str, Any] | list[Any],
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kind: str = "astrbot_skill_v1",
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) -> ToolExecResult:
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return await self._run(
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context,
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lambda client, _sandbox: client.skills.create_payload(
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payload=payload,
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kind=kind,
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),
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error_action="creating skill payload",
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)
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@dataclass
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class GetSkillPayloadTool(NeoSkillToolBase):
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name: str = "astrbot_get_skill_payload"
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description: str = "Get one skill payload by payload_ref."
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parameters: dict = field(
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default_factory=lambda: {
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"type": "object",
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"properties": {
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"payload_ref": {"type": "string"},
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},
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"required": ["payload_ref"],
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}
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)
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async def call(
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self,
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context: ContextWrapper[AstrAgentContext],
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payload_ref: str,
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) -> ToolExecResult:
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return await self._run(
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context,
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lambda client, _sandbox: client.skills.get_payload(payload_ref),
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error_action="getting skill payload",
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)
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@dataclass
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class CreateSkillCandidateTool(NeoSkillToolBase):
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name: str = "astrbot_create_skill_candidate"
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description: str = (
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"Step 2/3 for Neo skill authoring: create a candidate by binding execution evidence "
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"(source_execution_ids) with skill identity (skill_key) and optional payload_ref."
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)
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parameters: dict = field(
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default_factory=lambda: {
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"type": "object",
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"properties": {
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"skill_key": {
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"type": "string",
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"description": "Stable logical identifier, e.g. image-collage-9grid.",
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},
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"source_execution_ids": {
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"type": "array",
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"items": {"type": "string"},
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"description": "Execution evidence IDs captured from sandbox history.",
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},
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"scenario_key": {
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"type": "string",
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"description": "Optional scenario namespace for grouping candidates.",
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},
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"payload_ref": {
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"type": "string",
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"description": "Optional payload reference created by astrbot_create_skill_payload.",
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},
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},
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"required": ["skill_key", "source_execution_ids"],
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}
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)
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async def call(
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self,
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context: ContextWrapper[AstrAgentContext],
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skill_key: str,
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source_execution_ids: list[str],
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scenario_key: str | None = None,
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payload_ref: str | None = None,
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) -> ToolExecResult:
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return await self._run(
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context,
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lambda client, _sandbox: client.skills.create_candidate(
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skill_key=skill_key,
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source_execution_ids=source_execution_ids,
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scenario_key=scenario_key,
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payload_ref=payload_ref,
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),
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error_action="creating skill candidate",
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)
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@dataclass
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class ListSkillCandidatesTool(NeoSkillToolBase):
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name: str = "astrbot_list_skill_candidates"
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description: str = "List skill candidates."
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parameters: dict = field(
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default_factory=lambda: {
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"type": "object",
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"properties": {
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"status": {"type": "string"},
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"skill_key": {"type": "string"},
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"limit": {"type": "integer", "default": 100},
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"offset": {"type": "integer", "default": 0},
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},
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"required": [],
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}
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)
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async def call(
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self,
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context: ContextWrapper[AstrAgentContext],
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status: str | None = None,
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skill_key: str | None = None,
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limit: int = 100,
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offset: int = 0,
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) -> ToolExecResult:
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return await self._run(
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context,
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lambda client, _sandbox: client.skills.list_candidates(
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status=status,
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skill_key=skill_key,
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limit=limit,
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offset=offset,
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),
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error_action="listing skill candidates",
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)
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@dataclass
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class EvaluateSkillCandidateTool(NeoSkillToolBase):
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name: str = "astrbot_evaluate_skill_candidate"
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description: str = "Evaluate a skill candidate."
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parameters: dict = field(
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default_factory=lambda: {
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"type": "object",
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"properties": {
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"candidate_id": {"type": "string"},
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"passed": {"type": "boolean"},
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"score": {"type": "number"},
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"benchmark_id": {"type": "string"},
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"report": {"type": "string"},
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},
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"required": ["candidate_id", "passed"],
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}
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)
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async def call(
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self,
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context: ContextWrapper[AstrAgentContext],
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candidate_id: str,
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passed: bool,
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score: float | None = None,
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benchmark_id: str | None = None,
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report: str | None = None,
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) -> ToolExecResult:
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return await self._run(
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context,
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lambda client, _sandbox: client.skills.evaluate_candidate(
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candidate_id,
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passed=passed,
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score=score,
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benchmark_id=benchmark_id,
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report=report,
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),
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error_action="evaluating skill candidate",
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)
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@dataclass
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class PromoteSkillCandidateTool(NeoSkillToolBase):
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name: str = "astrbot_promote_skill_candidate"
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description: str = (
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"Step 3/3 for Neo skill authoring: promote candidate to canary/stable release. "
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"If stage=stable and sync_to_local=true, payload.skill_markdown is synced to local SKILL.md automatically."
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)
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parameters: dict = field(
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default_factory=lambda: {
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"type": "object",
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"properties": {
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"candidate_id": {"type": "string"},
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"stage": {
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"type": "string",
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"description": "Release stage: canary/stable",
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"default": "canary",
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},
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"sync_to_local": {
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"type": "boolean",
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"description": (
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"Only used with stage=stable. true means sync payload.skill_markdown to local SKILL.md; "
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"false means release remains Neo-side only."
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),
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"default": True,
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},
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},
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"required": ["candidate_id"],
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}
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)
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async def call(
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self,
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context: ContextWrapper[AstrAgentContext],
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candidate_id: str,
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stage: str = "canary",
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sync_to_local: bool = True,
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) -> ToolExecResult:
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if err := _ensure_admin(context):
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return err
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if stage not in {"canary", "stable"}:
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return "Error promoting skill candidate: stage must be canary or stable."
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try:
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client, _sandbox = await _get_neo_context(context)
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sync_mgr = NeoSkillSyncManager()
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result = await sync_mgr.promote_with_optional_sync(
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client,
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candidate_id=candidate_id,
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stage=stage,
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sync_to_local=sync_to_local,
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)
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if result.get("sync_error"):
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rollback_json = result.get("rollback")
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if rollback_json:
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return (
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"Error promoting skill candidate: stable release synced failed; "
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f"auto rollback succeeded. sync_error={result['sync_error']}; "
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f"rollback={_to_json_text(rollback_json)}"
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)
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return _to_json_text(
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{
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"release": result.get("release"),
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"sync": result.get("sync"),
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"rollback": result.get("rollback"),
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}
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)
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except Exception as e:
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return f"Error promoting skill candidate: {str(e)}"
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@dataclass
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class ListSkillReleasesTool(NeoSkillToolBase):
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name: str = "astrbot_list_skill_releases"
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description: str = "List skill releases."
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parameters: dict = field(
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default_factory=lambda: {
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"type": "object",
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"properties": {
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"skill_key": {"type": "string"},
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"active_only": {"type": "boolean", "default": False},
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"stage": {"type": "string"},
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"limit": {"type": "integer", "default": 100},
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"offset": {"type": "integer", "default": 0},
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},
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"required": [],
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}
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)
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async def call(
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self,
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context: ContextWrapper[AstrAgentContext],
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skill_key: str | None = None,
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active_only: bool = False,
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stage: str | None = None,
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limit: int = 100,
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offset: int = 0,
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) -> ToolExecResult:
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return await self._run(
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context,
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lambda client, _sandbox: client.skills.list_releases(
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skill_key=skill_key,
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active_only=active_only,
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stage=stage,
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limit=limit,
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offset=offset,
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),
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error_action="listing skill releases",
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)
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@dataclass
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class RollbackSkillReleaseTool(NeoSkillToolBase):
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name: str = "astrbot_rollback_skill_release"
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description: str = "Rollback one skill release."
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parameters: dict = field(
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default_factory=lambda: {
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"type": "object",
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"properties": {
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"release_id": {"type": "string"},
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},
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"required": ["release_id"],
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}
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)
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async def call(
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self,
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context: ContextWrapper[AstrAgentContext],
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release_id: str,
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) -> ToolExecResult:
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return await self._run(
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context,
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lambda client, _sandbox: client.skills.rollback_release(release_id),
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error_action="rolling back skill release",
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)
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@dataclass
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class SyncSkillReleaseTool(NeoSkillToolBase):
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name: str = "astrbot_sync_skill_release"
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description: str = (
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"Sync stable Neo release payload to local SKILL.md and update mapping metadata."
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)
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parameters: dict = field(
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default_factory=lambda: {
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"type": "object",
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"properties": {
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"release_id": {"type": "string"},
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"skill_key": {"type": "string"},
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"require_stable": {"type": "boolean", "default": True},
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},
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"required": [],
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}
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)
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async def call(
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self,
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context: ContextWrapper[AstrAgentContext],
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release_id: str | None = None,
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skill_key: str | None = None,
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require_stable: bool = True,
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) -> ToolExecResult:
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return await self._run(
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context,
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lambda client, _sandbox: _sync_release_to_dict(
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client,
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release_id=release_id,
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skill_key=skill_key,
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require_stable=require_stable,
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),
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error_action="syncing skill release",
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)
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async def _sync_release_to_dict(
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client: Any,
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*,
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release_id: str | None,
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skill_key: str | None,
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require_stable: bool,
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) -> dict[str, str]:
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sync_mgr = NeoSkillSyncManager()
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result = await sync_mgr.sync_release(
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client,
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release_id=release_id,
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skill_key=skill_key,
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require_stable=require_stable,
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)
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return sync_mgr.sync_result_to_dict(result)
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