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Some LLM providers (e.g. qwen3.7-max via DashScope OpenAI-compatible endpoint) serialize array-typed function call parameters as JSON-encoded strings instead of native JSON arrays. This causes Pydantic validation to reject the skills parameter before the function body executes, making every create_agent and load_skill call fail. Changes: - Widen skills type annotation: list[str] -> str | list[str] - Add strict_mode=False to @function_tool decorator - Parse string inputs in function body (json.loads with comma-split fallback) Tested with qwen3.7-max: create_agent successfully spawns child agents that execute 23+ exec_command calls for SQL injection testing. Closes #770
44 lines
1.7 KiB
Python
44 lines
1.7 KiB
Python
"""``load_skill`` — fetch skill reference material into the conversation."""
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from __future__ import annotations
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import json
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from agents import RunContextWrapper, function_tool
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from strix.skills import load_skills, validate_requested_skills
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@function_tool(timeout=10, strict_mode=False)
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async def load_skill(ctx: RunContextWrapper, skills: str | list[str]) -> str:
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"""Return the markdown body of one or more skills as reference material.
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Use this when you need exact syntax / workflow / payload guidance
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right before acting on a technology that wasn't preloaded for your
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agent. The skill content lands inline as a tool result — no
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permanent prompt change, just in-conversation reference.
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For permanent skill assignment, pass ``skills=[…]`` to
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``create_agent`` when spawning a specialist child instead.
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Args:
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skills: List of skill names (e.g. ``["xss", "sql_injection"]``).
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Max 5. Names match the bare files under
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``strix/skills/<category>/<name>.md``.
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"""
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del ctx
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# Tolerate LLM providers that pass array params as JSON-encoded strings
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if isinstance(skills, str):
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try:
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skills = json.loads(skills)
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except json.JSONDecodeError:
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skills = [s.strip() for s in skills.split(",") if s.strip()]
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requested = list(skills or [])
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err = validate_requested_skills(requested)
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if err:
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return f"load_skill: {err}"
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contents = load_skills(requested)
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if not contents:
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return "load_skill: no content loaded for requested skills."
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sections = [f"## Skill: {name}\n\n{body}" for name, body in contents.items()]
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return "\n\n---\n\n".join(sections)
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