Adapt Pinscope to DeepSeek, auto datasheets, and a shared library.
Based on manvalan/pinscope main. Default LLM is DeepSeek with local skills and PDF ingest. Datasheets are fetched from LCSC/TI, stored in the component library, and review extracts abs-max with a deeper checklist. Adds scripts/update-pinscope.sh for the production host.
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"""Provider-agnostic local skill runner.
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Anthropic Console Skills have no equivalent on DeepSeek (or Gemini). This
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module inlines ``skills/<name>/SKILL.md`` as the system prompt, drives a
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normal tool-calling session, and runs ``validate.py`` locally after each
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``output_tool`` call. Used by DeepSeek and Gemini; Anthropic falls back
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here when no Console skill id is configured.
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"""
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from __future__ import annotations
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import importlib.util
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import logging
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import re
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import time
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from pathlib import Path
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from backend.config import settings
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from backend.services.llm.base import LLMProvider
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from backend.services.llm.types import (
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Completion,
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Message,
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PdfBlock,
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TextBlock,
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ToolResultBlock,
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ToolSchema,
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Usage,
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)
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log = logging.getLogger(__name__)
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_SKILL_MAX_TURNS = 10
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_FRONTMATTER = re.compile(r"^---\n.*?\n---\n", re.DOTALL)
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_LOCAL_SKILL_TAIL = """
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You cannot run shell commands or Python. Do not try to execute validate.py.
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After extracting the data, call the `{tool}` tool with the structured result.
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The server validates the payload. If validation fails you will receive the
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errors and must call `{tool}` again with a corrected payload.
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Do NOT write files to disk.
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"""
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def skills_dir() -> Path:
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return Path(settings.skills_dir)
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def load_skill_markdown(skill_name: str) -> str:
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path = skills_dir() / skill_name / "SKILL.md"
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if not path.is_file():
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raise FileNotFoundError(
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f"Skill {skill_name!r} not found at {path}. "
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f"Expected skills/{skill_name}/SKILL.md in the repo."
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)
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raw = path.read_text(encoding="utf-8")
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return _FRONTMATTER.sub("", raw).strip()
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def load_skill_validator(skill_name: str):
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"""Import ``skills/<name>/validate.py`` and return its ``validate`` fn."""
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path = skills_dir() / skill_name / "validate.py"
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if not path.is_file():
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return None
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spec = importlib.util.spec_from_file_location(
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f"pinscope_skill_{skill_name.replace('-', '_')}_validate", path,
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)
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if spec is None or spec.loader is None:
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return None
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mod = importlib.util.module_from_spec(spec)
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spec.loader.exec_module(mod)
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fn = getattr(mod, "validate", None)
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return fn if callable(fn) else None
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def _sum_usage(total: Usage, piece: Usage) -> Usage:
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return Usage(
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input_tokens=total.input_tokens + piece.input_tokens,
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output_tokens=total.output_tokens + piece.output_tokens,
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cache_creation_tokens=total.cache_creation_tokens + piece.cache_creation_tokens,
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cache_read_tokens=total.cache_read_tokens + piece.cache_read_tokens,
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)
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async def run_skill_locally(
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provider: LLMProvider,
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*,
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skill_name: str,
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model: str,
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system: str,
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user_text: str,
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pdf_path: str | None,
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output_tool: ToolSchema,
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max_turns: int = _SKILL_MAX_TURNS,
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) -> tuple[dict, Completion]:
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"""Run ``skill_name`` as an in-process tool loop on ``provider``."""
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skill_md = load_skill_markdown(skill_name)
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validator = load_skill_validator(skill_name)
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full_system = (
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skill_md
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+ "\n\n"
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+ system.strip()
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+ "\n"
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+ _LOCAL_SKILL_TAIL.format(tool=output_tool.name)
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)
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user_blocks: list = []
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if pdf_path:
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user_blocks.append(PdfBlock(path=Path(pdf_path), cacheable=True))
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user_blocks.append(TextBlock(text=user_text, cacheable=True))
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messages: list[Message] = [Message(role="user", content=user_blocks)]
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total = Usage()
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t0 = time.monotonic()
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last_completion: Completion | None = None
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session = await provider.create_session(
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model=model, system=full_system, max_tokens=16384, temperature=0.0,
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)
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try:
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for turn in range(max_turns):
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force = turn >= max_turns - 2
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completion = await session.complete(
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messages=messages,
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tools=[output_tool],
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tool_choice={"name": output_tool.name} if force else "auto",
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)
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last_completion = completion
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total = _sum_usage(total, completion.usage)
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payload: dict | None = None
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for tc in completion.tool_calls:
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if tc.name == output_tool.name:
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payload = dict(tc.input)
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break
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messages.append(Message(
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role="assistant", content=completion.raw_assistant_blocks,
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))
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if payload is None:
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messages.append(Message(role="user", content=[TextBlock(
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text=(
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f"You did not call {output_tool.name}. "
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f"Call it now with the extracted data."
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),
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)]))
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continue
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errors: list[str] = []
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if validator is not None:
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try:
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errors = list(validator(payload) or [])
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except Exception as exc:
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log.warning(
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"Skill %s validate.py raised: %s", skill_name, exc,
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)
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errors = [f"validator crashed: {exc}"]
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if not errors:
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completion.usage = total
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completion.turns = turn + 1 # type: ignore[attr-defined]
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completion.duration_ms = int((time.monotonic() - t0) * 1000) # type: ignore[attr-defined]
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return payload, completion
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messages.append(Message(
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role="user",
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content=[
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ToolResultBlock(
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tool_use_id=completion.tool_calls[0].id,
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name=output_tool.name,
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content="VALIDATION FAILED:\n" + "\n".join(
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f"- {e}" for e in errors
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),
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),
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TextBlock(
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text=(
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"Fix the payload and call "
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f"{output_tool.name} again."
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),
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),
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],
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))
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finally:
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await session.close()
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raise RuntimeError(
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f"Skill {skill_name!r} did not produce a valid {output_tool.name} "
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f"in {max_turns} turns"
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+ (f" (last stop_reason={last_completion.stop_reason})"
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if last_completion else "")
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)
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