SKILL.md Step 6 restructured:
- 6.1: extract 3-5 concrete entities from article before prompting
- 6.2: generate cover only (1 API call, test direction early)
- 6.3: validate cover (interactive: ask user; auto: self-check entities)
- 6.4: batch inline images using cover's style for consistency
visual-prompts.md:
- Add "entity anchoring" hard rule: every prompt must include ≥2 article
entities; ban vague terms as sole subject ("科技感", "未来感")
- Add anti-pattern → good-pattern examples
- Inline images must reuse cover's style description for consistency
Addresses #9
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- learn_edits.py: prioritize output_file field from history.yaml,
fall back to title slug matching, then largest file
- SKILL.md: add output_file field to history.yaml schema
- Fixes wrong file match when multiple articles share the same date
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- publisher.py: add get_draft() to fetch draft content by media_id,
add html_to_plaintext() for HTML→text conversion
- learn_edits.py: add --from-wechat flag that auto-fetches latest draft
from WeChat, converts both sides to plaintext, and diffs
- learn_edits.py: add markdown_to_plaintext() for local file conversion
- SKILL.md: update edit workflow — both local and WeChat edits supported
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Add Step 8.2 guidance: edits must be in output/ markdown, not WeChat
draft box, for learn-edits to work
- Update 8.3 "学习我的修改" entry with same note
- Renumber 8.2→8.2 (edit advice) + 8.3→8.3 (reply) for clarity
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Rename "Tier 3 评估" to "综合评估", describe dimensions directly
(tone variance, density rhythm, pacing, readability) without
referencing anti-detection framework
- Reframe composite_score from "0=human, 100=AI" to "0=high quality,
100=issues found"
- Change 5.3 role from "gate control" to "supplementary verification"
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Add article self-check ("检查一下"): generation report + quality advice
- Record enhance_strategy in history.yaml
- Replace Zhuque test data with persona style descriptions in README
- Update descriptions: anti-AI focus → content quality focus
- Remove stale parameter optimization references
- Sync all trigger words across README, auxiliary functions, and Step 8.3
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Step 4.6: add quick self-check after writing (banned words, sentence
variance, negative emotion) to fix obvious issues before Step 5
- Step 5.2: tighten rewrite scope to specific sentences only, max 3
fixes per round, reduce max rounds from 3 to 2
- Step 5.3: reduce scoring rewrite from 3 rounds to 2, mark
DONE_WITH_CONCERNS instead of infinite loops when score stays >50
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Rename closing_style → closing_tendency in all 5 personas, making it
a soft preference rather than a hard constraint
- Add closing variation rule + 6 closing patterns table to writing-guide.md
- Step 4.5: LLM judges best closing from content; checks history.yaml
last 3 articles to avoid repeating the same closing_type
- Step 8.1: record closing_type in history.yaml for dedup
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Add transition segment to user exemplar injection (was 3 segments,
now 4 to match seeds path)
- Clarify priority chain: playbook > persona > exemplar > writing-guide
- Add exemplar fallback row to error handling table
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Seeds demonstrate anti-AI structural patterns (sentence variance, real
negative emotion, self-correction, abrupt closings) without imposing a
specific writing style. Step 4.4 falls back to seeds when the user's
exemplar library is empty.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Prompts users to import articles when exemplar library is empty,
without blocking the pipeline.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- New script: scripts/extract_exemplar.py
Extracts style fingerprints from human-written articles (opening hook,
emotional peak, transition/self-correction, closing) with statistical
analysis (sentence stddev, vocab temperature, negative ratio, paragraph CV).
Auto-detects category, supports batch import.
- SKILL.md: Add Step 4.4 exemplar injection
Loads matching exemplars by category before writing, injects segments
as few-shot style examples in the prompt.
- learn_edits.py: Auto-grow exemplar library
After user edits, auto-extracts the final version into the exemplar
library if humanness_score <= 50.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Renumber all sub-steps to consistent X.Y format (1a-2→1.2, 4a-0→4.1, 5b-2→5.3)
- Add TaskCreate directive: create 8 tasks at pipeline start, update status per step
- Clean up internal references (Step 3b→3.2, Step 4b→4.3, etc.)
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
learn_edits.py: patterns now have type/key/description/rule fields,
confidence auto-computed from occurrences + recency with 30-day decay.
--summarize --json outputs aggregated patterns sorted by confidence.
learn-edits.md: playbook.md format changed from free text to structured
YAML rules with confidence levels. Rules with confidence ≥ 5 become
hard constraints in Step 4, < 5 are soft references, < 2 get pruned.
SKILL.md Step 4: playbook priority now confidence-gated.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Step 8a: write composite_score + writing_config_snapshot to history.yaml,
recording which parameters produced which anti-AI score.
Step 4a-0: before writing, read history for the best-scoring article's
parameter combination and use it as reference for the current article.
This closes the feedback loop: write → score → record → learn → write better.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Reorganize anti-AI rules into 3 tiers mapped to detector signals:
- Tier 1 (Statistical): sentence variance, vocab temperature, paragraph
rhythm, emotion polarity, adverb density, style drift
- Tier 2 (Linguistic): banned words, broken sentences, unexpected words,
coherence breaking
- Tier 3 (Content): real data anchoring, specificity, density waves,
dimension randomization
New rules added: emotion polarity distribution (1.4), adverb density
control (1.5), inter-paragraph style drift (1.6), unexpected word
usage (2.3). Each rule now references the detection signal it counters.
writing-config.example.yaml updated with corresponding new parameters.
SKILL.md Step 5 checklist aligned to new structure.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Add VERSION file (1.2.0)
- SKILL.md Step 1: auto-check for updates on each run
- SKILL.md: add "更新" auxiliary function (git pull)
- README: install via git clone instead of cp/ln
- build_openclaw.py: include VERSION in dist
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
