refactor: remove optimize_loop.py, replace with agent-driven optimization
optimize_loop.py was framework-only (needed external LLM API). The optimization is now an auxiliary function in SKILL.md driven by the already-running agent. All references updated across README, CLAUDE.md, diagnose.py, and writing-config.example.yaml. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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75
CLAUDE.md
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75
CLAUDE.md
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# CLAUDE.md
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This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
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## Project Overview
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WeWrite is a WeChat public account (公众号) content generation AI skill. It automates the full workflow from trending topic discovery to draft box publishing. It works as both a Claude Code skill (via SKILL.md) and an OpenClaw-compatible skill (via `dist/openclaw/`).
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The core pipeline is defined in SKILL.md (Steps 1-8): environment check → topic selection → framework + material collection → writing → SEO/anti-AI verification → visual AI → formatting/publishing → wrap-up.
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## Commands
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```bash
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# Install dependencies
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pip install -r requirements.txt
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# Toolkit CLI
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python3 toolkit/cli.py preview article.md --theme sspai # Preview as HTML
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python3 toolkit/cli.py publish article.md --cover cover.png --title "标题" # Publish to WeChat
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python3 toolkit/cli.py gallery # Browse all 16 themes
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python3 toolkit/cli.py themes # List theme names
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python3 toolkit/cli.py image-post img1.jpg img2.jpg -t "标题" # Image post (carousel)
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# Data collection scripts
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python3 scripts/fetch_hotspots.py --limit 20 # Trending topics
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python3 scripts/seo_keywords.py --json "关键词1" "关键词2" # SEO keyword analysis
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python3 scripts/fetch_stats.py <article_id> # WeChat article stats
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python3 scripts/humanness_score.py article.md --verbose # AI detection scoring (11 checks)
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# Build OpenClaw-compatible skill (also runs in CI on push to main)
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python3 scripts/build_openclaw.py
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```
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No formal test suite exists. CI only rebuilds the OpenClaw version on push to main.
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## Architecture
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### Dual Nature: Skill + Toolkit
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- **As a skill** (SKILL.md): An agent-orchestrated 8-step pipeline. The LLM reads SKILL.md and executes steps, calling Python scripts as tools. Reference docs in `references/` are loaded on-demand by the agent at specific steps.
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- **As a standalone toolkit** (`toolkit/cli.py`): A Python CLI for Markdown→WeChat HTML conversion and publishing, usable independently of the skill.
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### Key Directories
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- `scripts/` — Data collection utilities (hotspots, SEO, stats) and build tools. Called by the agent during pipeline execution.
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- `toolkit/` — Markdown→WeChat HTML converter, theme engine, WeChat API client, image generation. The CLI entry point is `toolkit/cli.py`.
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- `personas/` — 5 YAML writing personality presets controlling tone, data presentation, emotional arc. Loaded in Step 4b.
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- `references/` — Agent-loaded docs (writing rules, frameworks, SEO, topic scoring). These are NOT code — they are instruction sets the LLM reads and follows.
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- `toolkit/themes/` — 16 YAML theme definitions. Parsed by `toolkit/theme.py`, applied as inline CSS by `toolkit/converter.py`.
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### Formatting Pipeline (toolkit)
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`converter.py` is the core: Markdown → HTML with inline styles + WeChat compatibility fixes (CJK spacing, bold punctuation, list→section conversion, external links→footnotes, dark mode attributes). WeChat strips `<style>` tags, so all CSS must be inlined. Themes are YAML files defining colors and base CSS; `theme.py` parses them, `converter.py` applies them.
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### OpenClaw Compatibility
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`scripts/build_openclaw.py` transforms SKILL.md for OpenClaw: replaces `{skill_dir}` with `{baseDir}`, renames tool references (WebSearch→web_search, etc.), copies referenced files. CI runs this on push to main and commits to `dist/openclaw/`.
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### Configuration Files
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- `config.yaml` (from `config.example.yaml`) — WeChat API credentials + image API key. Missing → graceful degradation (skip_publish, skip_image_gen).
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- `style.yaml` (from `style.example.yaml`) — User's writing profile (name, topics, tone, persona, theme). Auto-created via onboard flow on first run.
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- `writing-config.yaml` (from `writing-config.example.yaml`) — Writing parameters (sentence variance, idiom density, etc.). Optimized per-user via the "优化参数" auxiliary function in SKILL.md.
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All three are .gitignored — each user generates their own.
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### Graceful Degradation
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The pipeline never hard-fails. Missing config → skip_publish/skip_image_gen flags. Script failures → WebSearch or LLM fallback. Image gen fails → output prompts only. These flags are set in Step 1 and automatically respected by later steps.
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## Language & Conventions
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- All code is Python 3.11+. No type checking or linter configured.
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- Commit messages are in Chinese, format: `type: description` (e.g., `fix: ...`, `chore: ...`).
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- The project language (README, SKILL.md, comments, references) is Chinese.
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23
README.md
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README.md
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@ -168,7 +168,7 @@ wewrite/
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├── SKILL.md # 主管道（273行，Step 1-8）
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├── config.example.yaml # API 配置模板
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├── style.example.yaml # 风格配置模板
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├── writing-config.example.yaml # 写作参数模板（可用 optimize loop 调优）
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├── writing-config.example.yaml # 写作参数模板（说"优化参数"自动调优）
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├── requirements.txt
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│
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├── dist/openclaw/ # OpenClaw 兼容版（CI 自动构建）
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@ -179,8 +179,7 @@ wewrite/
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│ ├── fetch_stats.py # 微信文章数据回填
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│ ├── build_playbook.py # 从历史文章生成 Playbook
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│ ├── learn_edits.py # 学习人工修改
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│ ├── humanness_score.py # 文章"人味"打分器（客观 checklist + LLM 判官）
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│ ├── optimize_loop.py # autoresearch 风格迭代优化框架
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│ ├── humanness_score.py # 文章"人味"打分器（11 项检测 + 参数映射）
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│ └── build_openclaw.py # SKILL.md → OpenClaw 格式转换
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│
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├── toolkit/ # Markdown → 微信工具链
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@ -235,19 +234,25 @@ Step 8 写入历史 → 回复用户（含编辑建议 + 飞轮提示）
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默认全自动。说"交互模式"可在选题/框架/配图处暂停确认。
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## 优化循环（实验性）
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## 写作参数优化
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借鉴 [autoresearch](https://github.com/karpathy/autoresearch) 的 change→score→keep/rollback 模式，WeWrite 提供写作参数自动调优框架：
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在对话中说「优化写作参数」或「优化参数」，Agent 会自动迭代调优你的 `writing-config.yaml`：
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1. 用当前参数写测试短文
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2. 用 `humanness_score.py` 打分（11 项检测，连续 0-1 分数）
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3. 找到最低分维度，调整对应参数
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4. 重复 N 轮（默认 3 轮）
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5. 保留得分最好的参数组合
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```bash
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# 对一篇文章打分（客观 checklist + 主观 LLM 判官）
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# 独立打分（不需要 Agent）
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python3 scripts/humanness_score.py article.md --verbose
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# 迭代优化写作参数
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python3 scripts/optimize_loop.py --topic "AI Agent" --iterations 10
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# JSON 输出（含每项分数 + 参数映射）
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python3 scripts/humanness_score.py article.md --json
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```
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框架开源，但优化后的 `writing-config.yaml` 不入 git——每个用户跑出自己的最优参数。
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优化后的 `writing-config.yaml` 不入 git——每个用户跑出自己的最优参数。
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## Toolkit 独立使用
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@ -157,7 +157,7 @@ def check_enhancements():
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else:
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checks.append(make_check(
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"enhancement", "writing_config", "warn",
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"not found → using defaults (run optimize_loop.py to tune)",
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"not found → using defaults (say '优化参数' to tune)",
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))
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# playbook.md
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@ -240,7 +240,7 @@ def compute_summary(checks):
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elif name == "playbook":
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recs.append('Edit a generated article, then say "学习我的修改" to build playbook.md')
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elif name == "writing_config":
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recs.append('Run: python3 scripts/optimize_loop.py --topic "your topic" --iterations 10')
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recs.append('Say "优化参数" to run the optimization loop')
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elif name == "history_articles":
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recs.append("Generate your first article to start building history")
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elif name == "dimension_variance":
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#!/usr/bin/env python3
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"""
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WeWrite Optimization Loop — autoresearch-style iterative improvement.
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Inspired by Karpathy's autoresearch: change → score → keep/rollback → repeat.
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But instead of optimizing ML training code, we optimize WRITING RULES to
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produce articles that pass AI detection while maintaining quality.
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The mutable surface: writing-config.yaml (style parameters + prompt rules)
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The fixed evaluation: humanness_score.py (objective checklist + subjective feel)
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The metric: composite_score (lower = more human, like val_bpb)
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Usage:
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python3 optimize_loop.py --topic "AI Agent" --iterations 10
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python3 optimize_loop.py --topic "AI Agent" --iterations 5 --verbose
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Architecture:
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1. Load current writing-config.yaml
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2. Generate article with current config
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3. Score with humanness_score.py
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4. LLM proposes a change to writing-config.yaml
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5. Generate article with new config
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6. Score again
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7. If improved → keep (commit). If not → rollback.
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8. Log to results.tsv
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9. Repeat.
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Requirements:
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- ANTHROPIC_API_KEY in environment (for article generation + LLM judge)
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- writing-config.yaml in skill root (created on first run with defaults)
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"""
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import argparse
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import json
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import os
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import subprocess
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import sys
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from datetime import datetime
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from pathlib import Path
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import yaml
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SKILL_DIR = Path(__file__).parent.parent
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CONFIG_PATH = SKILL_DIR / "writing-config.yaml"
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RESULTS_PATH = SKILL_DIR / "optimization-results.tsv"
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DEFAULT_CONFIG = {
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"persona": "科技媒体资深编辑，写了八年公众号，对AI行业有深度认知",
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"sentence_variance": 0.7,
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"broken_sentence_rate": 0.04,
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"idiom_density": 0.15,
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"filler_style": "mixed", # literary / casual / mixed / minimal
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"paragraph_rhythm": "chaotic", # structured / chaotic / wave
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"self_correction_rate": 0.02,
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"tangent_frequency": "every_800_chars", # never / every_500 / every_800 / every_1200
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"real_data_density": "high", # low / medium / high
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"word_temperature_bias": "warm", # cold / warm / hot / balanced
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"emotional_arc": "restrained_to_burst", # flat / gradual / restrained_to_burst / volatile
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"opening_style": "scene", # scene / data / question / anecdote / cold_open
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"closing_style": "open_question", # summary / open_question / image / abrupt
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"structure_linearity": 0.3, # 0=fully non-linear, 1=fully linear
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}
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def ensure_config():
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"""Create default writing-config.yaml if it doesn't exist."""
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if not CONFIG_PATH.exists():
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with open(CONFIG_PATH, "w", encoding="utf-8") as f:
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yaml.dump(DEFAULT_CONFIG, f, allow_unicode=True, default_flow_style=False)
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print(f"Created default config: {CONFIG_PATH}")
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return yaml.safe_load(CONFIG_PATH.read_text(encoding="utf-8"))
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def score_article(article_path: str) -> dict:
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"""Run humanness_score.py on an article. Returns parsed result."""
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result = subprocess.run(
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["python3", str(SKILL_DIR / "scripts" / "humanness_score.py"), article_path, "--json"],
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capture_output=True, text=True
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)
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if result.returncode != 0:
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print(f"Scoring failed: {result.stderr}", file=sys.stderr)
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return {"composite_score": 100.0, "error": result.stderr}
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return json.loads(result.stdout)
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def log_result(iteration: int, composite: float, config_summary: str, status: str, description: str):
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"""Append result to TSV log."""
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header_needed = not RESULTS_PATH.exists()
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with open(RESULTS_PATH, "a", encoding="utf-8") as f:
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if header_needed:
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f.write("iteration\ttimestamp\tcomposite\tstatus\tdescription\tconfig_change\n")
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ts = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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f.write(f"{iteration}\t{ts}\t{composite:.2f}\t{status}\t{description}\t{config_summary}\n")
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def print_banner(iteration: int, total: int):
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print(f"\n{'='*60}")
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print(f" OPTIMIZATION LOOP — Iteration {iteration}/{total}")
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print(f"{'='*60}")
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def main():
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parser = argparse.ArgumentParser(description="WeWrite optimization loop")
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parser.add_argument("--topic", required=True, help="Article topic for testing")
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parser.add_argument("--iterations", type=int, default=10, help="Number of iterations")
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parser.add_argument("--verbose", "-v", action="store_true")
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args = parser.parse_args()
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print(f"""
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╔══════════════════════════════════════════════════════╗
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║ WeWrite Optimization Loop ║
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║ Topic: {args.topic:<44s}║
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║ Iterations: {args.iterations:<39d}║
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║ ║
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║ Pattern: change config → generate → score → ║
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║ keep if better, rollback if worse ║
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╚══════════════════════════════════════════════════════╝
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""")
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config = ensure_config()
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print("This script provides the FRAMEWORK for optimization.")
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print("To run the full loop, you need:")
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print(" 1. An article generation function (Claude API)")
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print(" 2. A scoring function (humanness_score.py — included)")
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print(" 3. An LLM to propose config changes (Claude API)")
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print()
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print("Current config:")
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print(yaml.dump(config, allow_unicode=True, default_flow_style=False))
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print()
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print("Run this loop via Claude Code / OpenClaw agent:")
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print()
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print(" Agent reads writing-config.yaml")
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print(" → generates article with those rules")
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print(" → scores with: python3 scripts/humanness_score.py article.md --json")
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print(" → proposes a config change")
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print(" → generates new article")
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print(" → scores again")
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print(" → if composite_score decreased → commit config change")
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print(" → if composite_score same/worse → rollback")
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print(" → logs to optimization-results.tsv")
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print(" → repeats")
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print()
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print("To test scoring on an existing article:")
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print(f" python3 scripts/humanness_score.py <article.md> --verbose")
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if __name__ == "__main__":
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main()
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# WeWrite 写作参数（可优化）
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# 复制为 writing-config.yaml，然后用 optimize loop 迭代调优
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# 或手动调整后观察朱雀检测结果
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# 复制为 writing-config.yaml，在对话中说"优化参数"让 Agent 迭代调优
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# 或手动调整后用 humanness_score.py 评估
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#
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# 这个文件是起点，不是最优解。
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# 运行: python3 scripts/optimize_loop.py --topic "你的主题" --iterations 10
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# 每次迭代会修改 writing-config.yaml 中的参数，保留得分更好的版本。
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# 在对话中说"优化参数"即可自动调优，每轮调整得分最低的参数。
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#
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# 参数分三层，对应 writing-guide.md 的反检测结构。
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