Firsthand deepdive
专题深度线——对一个值得深挖的功能点(如 Claude Tag)生成深度文章草稿。从内参索引列「值得深挖」候选→用户选→抓官方素材生成五段骨架 draft→交 article-harness 打磨。用户说「深挖 X / 做个 X 的专题 / 专题深度」时使用。From its SKILL.md
npx -y skills add yinjialu/ai-frontier-daily --skill firsthand-deepdiveAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
2 things to look at
- no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
- 3 stars3 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
SKILL.md
3.6 KB, ~1.1k tokens by cl100k_base, as published. Nobody here has run it
专题深度线
内参的第三条内容线:把官方一手的重要功能点(模型发布/重大功能)做成面向普通用户的深度文章。
流程
1. 选题初筛(AI 列候选,用户选)
python3 -c "import json,datetime; from scripts.firsthand.query import load_index; from scripts.firsthand.deepdive import candidates; \
today=datetime.datetime.now(datetime.timezone(datetime.timedelta(hours=8))).date().isoformat(); \
[print(i+1, '|', g[0]['title'], '|', ', '.join(a['source'] for a in g)) for i,g in enumerate(candidates(load_index('data/firsthand'), 7, today))]"
把候选清单(每个 = 一个事件,可能多源)报给用户,用户选一个。
也可用户直接指定(不在索引里时,手工构造 cluster:每篇 {title, source, resource, summary, detected})。
2. 生成骨架 draft
判断该功能当前能否体验(如 Claude Tag 暂不可体验 → feature_available=False):
python3 -c "from scripts.firsthand.query import load_index; from scripts.firsthand.deepdive import candidates, write_draft; \
import datetime; today=datetime.datetime.now(datetime.timezone(datetime.timedelta(hours=8))).date().isoformat(); \
cands=candidates(load_index('data/firsthand'), 7, today); \
p=write_draft('content', cands[<选中序号-1>], today, feature_available=False); print('draft:', p)"
产出 content/deepdive/<date>_<slug>/draft.md:五段骨架 + frontmatter sources(官方 URL)+ 官方原文素材。
3. article-harness 迭代润色
article-harness 已适配多宿主,同一个 Writer/Reviewer 协议按当前 Agent 环境选择执行方式:
Codex 内使用时(优先):
- 读取并遵循
~/.codex/skills/article-harness/SKILL.md。 - 不从 shell 直接跑
~/.claude/.../harness.sh;由当前 Codex 主 Agent 按 article-harness 的 Codex subagent 流程编排 Writer/Reviewer 迭代。 - 输入草稿为
content/deepdive/<date>_<slug>/draft.md,产物契约保持不变:content/deepdive/<date>_<slug>/.harness-workspace/finished.md与feedback.md。
Claude Code 内使用时(暂时保持原链路不变):
bash ~/.claude/skills/article-harness/harness.sh content/deepdive/<date>_<slug>/draft.md
Writer 整理润色 ↔ Reviewer 迭代,PASS 后自动进入步骤4。
4. 自动推送微信草稿箱
harness PASS 后,立即用 wechat-official-draft skill 将 <draft_dir>/.harness-workspace/finished.md 推送到微信公众号草稿箱,无需用户介入。
用户在微信草稿箱完成最终验收。
5. 反馈沉淀(推送后主动询问)
草稿推送成功后,立即询问用户:"草稿已推送,有没有想沉淀的审稿反馈?"
用户说完后,将可泛化的反馈条目追加写入 ~/Documents/context-harness-data/article-harness/reviewer.md,并告知已写入。
没有反馈则跳过。
与其他线的关系
- 选题来源:内参
data/firsthand/index.json(官方一手)。 - 不重造 Writer-Reviewer(复用 article-harness);article-harness 已收敛进公开仓
yinjialu/bianliang-skills(skills.sh,npx skills add),全局安装路径按宿主区分:Codex 使用~/.codex/skills/article-harness/的 Codex 模式,Claude Code 暂继续使用~/.claude/skills/article-harness/的 CLI runner。
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.