init the project
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evyzacq
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---
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name: ir_generation_skill
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description: 通过自然语言描述，向JSON文件中添加以IR表示的功能点。
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---
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# add_ir
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## 概述
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通过自然语言描述由大语言模型自动转换为标准IR格式并写入到JSON文件或追加到JSON文件中。
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## 用法
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- **命令行传入**：直接传入功能的文字描述
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- **文件传入**：通过 `-f` 参数从文本文件读取描述
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- **追加模式**：新生成的功能点追加到已有 `_ir.json` 末尾，不影响已有条目
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## 输入要求
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- 功能的自然语言描述（命令行参数或 `-f` 文件）
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- 目标IR JSON文件路径（`--ir`，默认 `output/ir.json`）
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## 输出
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在目标IR JSON文件中追加新的功能点条目，每个条目包含：
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- `function`：功能名称
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- `source.section`：所属章节
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- `source.location`：位置描述
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- `trigger`：触发条件（可选）
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- `actions`：执行动作（可选）
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## 使用提示
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描述中应尽量包含功能的触发条件、执行动作和所属章节信息，以提高转换质量。
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import logging
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import os
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import time
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from typing import Optional
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from openai import OpenAI
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logger = logging.getLogger(__name__)
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class LLMClient:
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"""Low-level OpenAI-compatible LLM client with retry and token tracking.
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Usage::
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llm = LLMClient()
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content = llm.chat("qwen3.5-flash", [{"role": "user", "content": "Hello"}])
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print(llm.usage)
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"""
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IMAGE_MODEL = "qwen3-vl-plus"
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TEXT_MODEL = "qwen3.5-flash-2026-02-23"
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TIMEOUT = 120
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MAX_RETRIES = 3
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def __init__(
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self,
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*,
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base_url: str = "https://dashscope.aliyuncs.com/compatible-mode/v1",
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timeout: int | None = None,
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):
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key = os.environ.get("DASHSCOPE_API_KEY", "")
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if not key:
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raise ValueError("DASHSCOPE_API_KEY environment variable is not set.")
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self._client = OpenAI(api_key=key, base_url=base_url)
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self._timeout = timeout or self.TIMEOUT
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self._prompt_tokens = 0
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self._completion_tokens = 0
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@property
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def usage(self) -> dict:
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"""Return accumulated token counts as ``{prompt, completion, total}``."""
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return {
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"prompt_tokens": self._prompt_tokens,
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"completion_tokens": self._completion_tokens,
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"total_tokens": self._prompt_tokens + self._completion_tokens,
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}
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@staticmethod
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def estimate_tokens(text: str) -> int:
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"""Quick token estimate. CJK ≈1.7/token, others ≈3.0/token."""
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cjk = sum(1 for c in text if '一' <= c <= '鿿' or ' ' <= c <= '〿')
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other = len(text) - cjk
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return max(1, int(cjk / 1.7 + other / 3.0))
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@staticmethod
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def estimate_image_tokens() -> int:
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"""Fixed estimate for one vision-model image (~500 tokens)."""
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return 500
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def chat(
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self, model: str, messages: list[dict], *, timeout: int | None = None,
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response_format: dict | None = None,
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) -> str:
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"""Send a chat completion request and return the response content.
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Automatically retries on failure and accumulates token usage.
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"""
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label = f"chat({model})"
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def _call():
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t0 = time.time()
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kwargs = dict(model=model, messages=messages, timeout=timeout or self._timeout)
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if response_format is not None:
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kwargs["response_format"] = response_format
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kwargs["temperature"] = 0
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resp = self._client.chat.completions.create(**kwargs)
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content = resp.choices[0].message.content
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usg = resp.usage
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if usg:
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self._prompt_tokens += usg.prompt_tokens
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self._completion_tokens += usg.completion_tokens
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elapsed = time.time() - t0
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logger.info("%s: %d chars in %.1fs", label, len(content) if content else 0, elapsed)
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if not content:
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raise RuntimeError("Empty response from LLM")
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return content
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return self._retry(_call, label)
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def _retry(self, fn, label: str) -> str:
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"""Call *fn()* with exponential-backoff retry."""
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last_error: Optional[Exception] = None
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for attempt in range(self.MAX_RETRIES):
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try:
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return fn()
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except Exception as e:
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last_error = e
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logger.warning(
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"%s error (attempt %d/%d): %s",
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label, attempt + 1, self.MAX_RETRIES, e,
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)
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if attempt < self.MAX_RETRIES - 1:
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time.sleep(2 ** attempt)
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raise RuntimeError(f"{label}: all retries exhausted") from last_error
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+191
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#!/usr/bin/env python3
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"""Add missing features to an existing ``_ir.json``.
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Converts a natural-language description of missing features into structured
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IR entries via LLM, then appends them to the IR file.
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Usage::
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python scripts/add_missing.py "遗漏功能描述" --ir output/<basename>_ir.json
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python scripts/add_missing.py -f missing.txt --ir output/<basename>_ir.json
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"""
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import argparse
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import json
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import logging
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import os
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import sys
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sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
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from LLM import LLMClient
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s [%(levelname)s] %(message)s",
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datefmt="%Y-%m-%d %H:%M:%S",
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)
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logger = logging.getLogger(__name__)
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# ---------------------------------------------------------------------------
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# Prompt
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# ---------------------------------------------------------------------------
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PROMPT = """你是一个需求文档分析助手。用户发现已生成的功能点列表中有遗漏，请将以下描述中提到的遗漏功能点转换为标准JSON格式。
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## 遗漏功能描述
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{description}
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## JSON格式要求
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每个功能点输出为：
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{
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"function": "功能名称",
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"source": {
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"section": "章节名（如果描述中提到了章节信息则填写，否则填"未知"）",
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"location": "原文位置描述（如果描述中提供了则填写，否则填"用户补充"）"
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},
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"trigger": {
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"type": "AND或者OR",
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"conditions": [
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"触发条件1",
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"触发条件2"
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]
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},
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"actions": {
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"场景/角色": [
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"动作1",
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"动作2"
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]
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}
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}
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## 输出原则
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1. 将描述中的每个独立功能点拆分为一个独立的JSON对象
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2. 如果描述中包含多个功能点，输出一个JSON数组
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3. 没有 trigger 或 actions 的字段直接**省略**，不要写 null 或空列表/空对象
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4. 尽量从描述中推断 source.section 和 source.location
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5. 功能名称应简洁明确，概括该功能的核心行为
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6. 直接输出纯JSON，不要用 ```json 代码块包裹
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"""
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# ---------------------------------------------------------------------------
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# Helpers
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# ---------------------------------------------------------------------------
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def _parse_llm_response(raw: str) -> list | dict | str | None:
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"""Parse JSON from LLM response, handling markdown code fences."""
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if raw is None:
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return None
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stripped = raw.strip()
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if stripped.startswith("```"):
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nl = stripped.find("\n")
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stripped = stripped[nl + 1:] if nl != -1 else stripped[3:]
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if stripped.endswith("```"):
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stripped = stripped[:-3]
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try:
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return json.loads(stripped)
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except json.JSONDecodeError:
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logger.warning("Failed to parse JSON, returning raw text")
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return raw
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# ---------------------------------------------------------------------------
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# Core
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# ---------------------------------------------------------------------------
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def add_missing(description: str, ir_path: str) -> list[dict]:
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"""Convert missing feature description to IR entries and append to IR file.
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Returns the newly added entries.
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"""
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llm = LLMClient()
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prompt = PROMPT.replace("{description}", description)
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logger.info("Sending missing features to LLM...")
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try:
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raw = llm.chat(
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model=LLMClient.TEXT_MODEL,
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messages=[{"role": "user", "content": prompt}],
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response_format={"type": "json_object"},
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)
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logger.info("Response: %d chars", len(raw))
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except RuntimeError as e:
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logger.error("LLM call failed: %s", e)
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sys.exit(1)
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parsed = _parse_llm_response(raw)
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if isinstance(parsed, list):
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new_entries = parsed
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elif isinstance(parsed, dict):
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new_entries = [parsed]
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else:
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logger.error("Unparseable response")
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sys.exit(1)
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# Load existing IR or start fresh
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if os.path.exists(ir_path):
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with open(ir_path, "r", encoding="utf-8") as f:
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existing = json.load(f)
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logger.info("Loaded existing IR: %d entries", len(existing))
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else:
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existing = []
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logger.info("No existing IR file, creating new")
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existing.extend(new_entries)
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# Write back
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os.makedirs(os.path.dirname(ir_path) or ".", exist_ok=True)
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with open(ir_path, "w", encoding="utf-8") as f:
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json.dump(existing, f, ensure_ascii=False, indent=2)
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logger.info("Saved: %s (%d entries, +%d new)", ir_path, len(existing), len(new_entries))
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usg = llm.usage
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logger.info("Tokens: %d prompt + %d completion = %d total",
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usg["prompt_tokens"], usg["completion_tokens"], usg["total_tokens"])
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return new_entries
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# ---------------------------------------------------------------------------
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# CLI
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# ---------------------------------------------------------------------------
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description="Add missing features to an IR JSON file via LLM.",
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)
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parser.add_argument(
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"description", nargs="?", metavar="description",
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help="Natural-language description of missing features",
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)
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parser.add_argument(
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"-f", "--file", metavar="file.txt",
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help="Read description from a file instead of command line",
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)
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parser.add_argument(
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"--ir", default="output/ir.json", metavar="ir.json",
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help="Path to the IR JSON file (default: output/ir.json)",
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)
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args = parser.parse_args()
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if args.file:
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with open(args.file, "r", encoding="utf-8") as f:
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description = f.read().strip()
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elif args.description:
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description = args.description
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else:
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parser.error("Either provide a description string or use -f to read from a file")
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if not description:
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parser.error("Description is empty")
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add_missing(description, args.ir)
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