diff --git a/CLAUDE.md b/CLAUDE.md deleted file mode 100644 index c47a33a..0000000 --- a/CLAUDE.md +++ /dev/null @@ -1,97 +0,0 @@ -# CLAUDE.md - -This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository. - -## Project Overview - -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/`). - -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. - -## Commands - -```bash -# Install dependencies -pip install -r requirements.txt - -# Toolkit CLI -python3 toolkit/cli.py preview article.md --theme sspai # Preview as HTML -python3 toolkit/cli.py publish article.md --cover cover.png --title "标题" # Publish to WeChat -python3 toolkit/cli.py gallery # Browse all 16 themes -python3 toolkit/cli.py themes # List theme names -python3 toolkit/cli.py image-post img1.jpg img2.jpg -t "标题" # Image post (carousel) -python3 toolkit/cli.py learn-theme --name # Learn theme from WeChat article - -# Scoring and diagnostics -python3 scripts/humanness_score.py article.md --verbose # AI detection scoring (11 checks, 0-1 continuous) -python3 scripts/humanness_score.py article.md --json --tier3 0.7 # With agent Tier 3 score -python3 scripts/diagnose.py # Anti-AI config diagnostic -python3 scripts/diagnose.py --json # JSON output for agent - -# Exemplar library (SICO-style few-shot) -python3 scripts/extract_exemplar.py article.md # Extract exemplar from article -python3 scripts/extract_exemplar.py article.md -c tech-opinion -s "账号名" # With category + source -python3 scripts/extract_exemplar.py article1.md article2.md # Batch import -python3 scripts/extract_exemplar.py --list # List exemplar library - -# Data collection scripts -python3 scripts/fetch_hotspots.py --limit 20 # Trending topics -python3 scripts/seo_keywords.py --json "关键词1" "关键词2" # SEO keyword analysis -python3 scripts/fetch_stats.py # WeChat article stats - -# Build OpenClaw-compatible skill (also runs in CI on push to main) -python3 scripts/build_openclaw.py -``` - -No formal test suite exists. CI only rebuilds the OpenClaw version on push to main. - -## Architecture - -### Dual Nature: Skill + Toolkit - -- **As a skill** (SKILL.md): An agent-orchestrated 8-step pipeline with TaskCreate progress tracking. 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. -- **As a standalone toolkit** (`toolkit/cli.py`): A Python CLI for Markdown→WeChat HTML conversion and publishing, usable independently of the skill. - -### Anti-AI Detection System - -Three-tier approach aligned with how detectors work (defined in `references/writing-guide.md`): -- **Tier 1 Statistical** (rules 1.1-1.6): Sentence variance, vocabulary richness, paragraph rhythm, emotion polarity, adverb density, style drift. Counters perplexity/burstiness detection. -- **Tier 2 Linguistic** (rules 2.1-2.4): Banned words, broken sentences, unexpected words, coherence breaking. Counters syntax/vocabulary fingerprinting. -- **Tier 3 Content** (rules 3.1-3.4): Real data anchoring, specificity, density waves, dimension randomization. Counters semantic analysis. - -`scripts/humanness_score.py` implements Tier 1+2 programmatically (11 checks, continuous 0-1 scores). Tier 3 is done by the agent in SKILL.md Step 5.3. Each check maps to a `writing-config.yaml` parameter via the `param` field in JSON output. - -### Self-Learning Flywheel - -- **Scoring feedback**: Step 5.3 scores each article → Step 8.1 records `composite_score` + `writing_config_snapshot` to `history.yaml` → Step 4.1 reads historical best params for next article. -- **Edit learning**: `scripts/learn_edits.py` captures typed patterns (key/type/description/rule) with confidence scoring and 30-day decay → `playbook.md` stores rules as structured YAML → Step 4.3 applies rules gated by confidence (≥5 hard constraint, <5 soft reference, <2 pruned). -- **Parameter optimization**: "优化参数" auxiliary function in SKILL.md runs agent-driven iterative loop (write test article → score → adjust lowest params → repeat). - -### Key Directories - -- `scripts/` — Scoring, diagnostics, data collection, and build tools. -- `toolkit/` — Markdown→WeChat HTML converter, theme engine, WeChat API client, image generation. CLI entry point: `toolkit/cli.py`. -- `personas/` — 5 YAML writing personality presets controlling tone, data presentation, emotional arc. -- `references/` — Agent-loaded instruction docs (writing rules, frameworks, SEO, topic scoring). NOT code. -- `toolkit/themes/` — 16 YAML theme definitions, applied as inline CSS. - -### Configuration Files - -- `config.yaml` (from `config.example.yaml`) — WeChat API credentials + image API key. Missing → graceful degradation (skip_publish, skip_image_gen). -- `style.yaml` (from `style.example.yaml`) — User's writing profile (name, topics, tone, persona, theme). Auto-created via onboard flow on first run. -- `writing-config.yaml` (from `writing-config.example.yaml`) — Writing parameters mapped to anti-AI rules. Optimized per-user via "优化参数" auxiliary function. -- `playbook.md` — Structured YAML rules learned from user edits, with confidence scores and decay. - -All four are .gitignored — each user generates their own. - -### Graceful Degradation - -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. - -## Language & Conventions - -- All code is Python 3.11+. No type checking or linter configured. -- Commit messages use format: `type: description` (e.g., `fix: ...`, `feat: ...`, `chore: ...`). -- The project language (README, SKILL.md, comments, references) is Chinese. -- SKILL.md sub-steps use `X.Y` numbering (e.g., 1.1, 4.3, 5.2). -- VERSION file tracks releases. Bump on user-facing changes.