bugfix

pyproject.toml

@@ -0,0 +1,18 @@
+[build-system]
+requires = ["setuptools>=61.0"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "skillagent"
+version = "0.1.0"
+description = "Agent + Skillkit integration module with multi-skill, multi-script support"
+authors = [
+    { name="Your Name", email="you@example.com" }
+]
+readme = "README.md"
+requires-python = ">=3.10"
+dependencies = [
+    "pydantic>=1.10",
+	"skillkit",
+    "aiohttp"
+]

skillagent/agent.py

@@ -0,0 +1,43 @@
+import os, json
+from typing import Dict
+from pydantic import ValidationError
+
+class DAGNode:
+    def __init__(self, skill, script=None, params=None):
+        self.skill = skill
+        self.script = script
+        self.params = params or {}
+        self.state = "pending"
+        self.missing = []
+
+class SkillAgent:
+    def __init__(self, skill_loader, skillkit_wrapper, llm):
+        self.skill_loader = skill_loader
+        self.skillkit = skillkit_wrapper
+        self.llm = llm
+        self.dag = []
+
+    async def plan(self, user_text: str):
+        skills = self.skill_loader.list_skills()
+        skill_docs = "\n".join([open(os.path.join(self.skill_loader.skillspath, s, "skill.md"), encoding="utf-8").read() for s in skills])
+        prompt = f"""
+User request: {user_text}
+Available Skills:
+{skill_docs}
+
+Task: choose the most suitable skill and script. Output JSON: {{ "skill": "<skill_name>", "script": "<script_name>" }}
+"""
+        raw = await self.llm.complete(prompt)
+        data = json.loads(raw)
+        node = DAGNode(skill=data["skill"], script=data["script"])
+        self.dag.append(node)
+        return node
+
+    async def resume(self, node: DAGNode, user_params: dict = None, schema=None):
+        try:
+            validated = schema(**(user_params or node.params))
+            node.params = validated.dict()
+        except ValidationError as e:
+            node.missing = [err['loc'][0] for err in e.errors()]
+            return node.missing
+        return self.skillkit.invoke_skill(node.skill, node.script, node.params)

skillagent/schemas/generic_schemas.py

@@ -0,0 +1,12 @@
+from pydantic import BaseModel
+from typing import Optional
+
+class SliderParams(BaseModel):
+    min: int
+    max: int
+    step: Optional[int] = 1
+    default: Optional[int] = 0
+
+class ButtonParams(BaseModel):
+    label: str
+    color: Optional[str] = "blue"

skillagent/skill_loader.py

@@ -0,0 +1,25 @@
+import os, re
+from typing import List, Dict
+
+class SkillLoader:
+    def __init__(self, skillspath: str):
+        self.skillspath = skillspath
+
+    def list_skills(self) -> List[str]:
+        return [name for name in os.listdir(self.skillspath)
+                if os.path.isdir(os.path.join(self.skillspath, name))]
+
+    def parse_skill_md(self, skill_name: str) -> List[Dict]:
+        md_path = os.path.join(self.skillspath, skill_name, "skill.md")
+        if not os.path.exists(md_path):
+            return []
+        scripts = []
+        md_text = open(md_path, encoding="utf-8").read()
+        script_blocks = re.findall(r"### (.+?)\n- 功能: (.+?)\n- 参数: (.+)", md_text)
+        for name, desc, params in script_blocks:
+            scripts.append({
+                "name": name.strip(),
+                "description": desc.strip(),
+                "params": [p.strip() for p in params.split(",")]
+            })
+        return scripts

skillagent/skillkit_wrapper.py

@@ -0,0 +1,7 @@
+class SkillkitWrapper:
+    def __init__(self, skillkit_client):
+        self.client = skillkit_client
+
+    def invoke_skill(self, skill: str, script: str, params: dict):
+        print(f"Invoking skill={skill}, script={script}, params={params}")
+        return self.client.invoke_skill(skill, params)

skillagent/utils.py

@@ -0,0 +1,10 @@
+import os
+
+def feed_doc_layer(skillspath: str, skill_name: str, layer: str, script_name: str = None):
+    base_path = os.path.join(skillspath, skill_name)
+    if layer == "skill":
+        return open(os.path.join(base_path, "skill.md"), encoding="utf-8").read()
+    elif layer == "script" and script_name:
+        return open(os.path.join(base_path, "scripts", f"{script_name}.md"), encoding="utf-8").read()
+    elif layer == "example" and script_name:
+        return open(os.path.join(base_path, "examples", f"{script_name}_example.md"), encoding="utf-8").read()