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>
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>
- 11 checks across 2 tiers (6 statistical + 5 pattern), up from 6
- Continuous 0-1 scores instead of pass/fail booleans
- Each check maps to a writing-config parameter via param field
- New checks: negative emotion ratio, adverb density, vocabulary richness,
sentence length range, self-correction patterns
- New --tier3 flag for agent to pass LLM structural analysis score
- param_scores in JSON output: flat param→score map for optimization
- Standalone mode redistributes weights (T1=62.5%, T2=37.5%)
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>
- scripts/build_openclaw.py：SKILL.md 转换（{skill_dir}→{baseDir}、WebSearch→web_search、移除 allowed-tools）
- .github/workflows/build-openclaw.yml：push to main 时自动构建 dist/openclaw/
- dist/openclaw/：首次构建产物入库，OpenClaw 用户可直接使用
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Previously, hotspots were sorted by raw hot values directly, but different
platforms use vastly different scales (Toutiao ~10M, Weibo ~1M, Baidu ~100K),
causing Toutiao to dominate all results while Weibo and Baidu entries were
always truncated.
Now uses rank-based normalization (0-100) within each source before merging,
so cross-platform sorting gives equal weight to each platform's top stories.
Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
