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>
- 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>
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>
- 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>
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>
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>
