# Daily Briefing > Generate a source-linked Chinese AI/Web3 daily briefing with official model releases, infrastructure, application adoption, funding, and cross-industry signals directly in Codex, then save the editorial result as Markdown plus a standalone interactive HTML reader. Use when the user asks for 今日早报, AI 简报, 交互版简报, HTML 简报, 最新模型发布, AI 产品更新, 应用层趋势, AI 投融资, 产业链, official AI updates, daily briefing, recent AI hotspots, GitHub trends, content ideas, briefing feedback, or a weekly briefing review. - Skill: `rion-wu-tech/daily-briefing-2` (Agent Skill, multi-file: 5 files) - Install (CLI): `npx skillmds@latest add rion-wu-tech/daily-briefing-2` - Raw SKILL.md: https://api.skillmd.com/api/skills/rion-wu-tech/daily-briefing-2/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Web & Frontend - Author: rion-wu-tech (https://skillmd.com/u/rion-wu-tech) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/rion-wu-tech/daily-briefing-2 --- # Daily Briefing Use this skill directly in Codex. Do not require Hermes, Telegram, or a separate chat bot. ## Generate Today's Briefing 1. Run the bundled helper to fetch, normalize, deduplicate, and score the source pool: ```bash skills/daily-briefing/scripts/run-daily-briefing.sh \ --editorial-packet \ --output-file "outputs/editorial_packet_$(date +%F).json" ``` Translate explicit user constraints into helper flags without asking them to learn CLI syntax: - `最近 48 小时模型发布与更新` → `--mode models --hours 48` - `最近 7 天 AI 视频产品更新` → `--mode products --focus "AI视频" --hours 168` - `最近 24 小时应用层趋势` → `--mode applications --hours 24` - `最近 7 天 AI 投融资` → `--mode funding --hours 168` - `今日 AI 全产业链简报` → `--mode industry --hours 24` - `过去 24 小时医疗 AI 新闻` → `--mode hotspots --focus "医疗" --hours 24` - `今日完整早报` → keep the default `--mode all`; do not narrow the existing source pool. Append the selected flags to the helper command before generating the editorial packet. When invoked from an installed skill, use the absolute path to this skill's `scripts/run-daily-briefing.sh`. 2. Read `sections.industry_chain`, `sections.cross_layer_connections`, `sections.model_releases`, `sections.product_updates`, `sections.application_trends`, `sections.ai_funding`, `sections.viral_ai_news`, `sections.official_social_updates`, and `sections.editorial_queue`. Treat infrastructure, model/platform, product/function, application adoption, capital/commercial events, and potentially viral news as parallel intelligence tracks. Use the current Codex model to select the strongest items, write concrete Chinese explanations, organize sections, and produce varied X drafts. `raw_summary` contains source material for factual rewriting; do not discard it when the deterministic Chinese hint is generic. 3. When `metadata.official_x_monitor.mode` is `codex_web_search`, execute every query in `sections.official_x_search_groups` for the requested window. The groups are country-aware and put China first. Retain direct `https://x.com//status/` links, and add only posts published by the configured account. Also check `sections.official_model_watchlist` and `sections.official_product_watchlist` for vendors and products that do not expose a stable feed. 4. Execute the relevant batches in `sections.industry_search_groups`, starting with China. Use `sections.industry_source_watchlist` to return to company newsrooms, customer announcements, investors, exchanges, or regulatory filings. Discovery-only media and community sources may surface a lead, but they cannot confirm funding amounts, adoption figures, revenue, valuation, or infrastructure spending on their own. 5. Before writing, complete a freshness audit: confirm that every relevant X and industry search group was checked or name the failed group, and separately record first-party findings as `model release`, `product/function update`, `application/adoption`, `funding/commercial event`, `ecosystem activity`, or `potential viral story`. An account or page appearing in a watchlist is not evidence that it was searched. Do not finalize a briefing with an empty China official-source result until the CN groups have been checked explicitly. 6. Do not require a model launch for inclusion. Track major product releases, new features, application adoption, customer cases, user growth, pricing, revenue, partnerships, infrastructure, open-source projects, research breakthroughs, financing, policy or safety events, industry disputes, and fast-rising community discussions when they have clear AI relevance and credible links. Treat popularity as a distribution signal, not proof of truth. 7. Classify each item on two separate axes: `industry_layer` is only `upstream`, `midstream`, or `downstream`; `event_types` describes what happened, such as `model_release`, `product_update`, `adoption`, `funding`, `acquisition`, `earnings`, or `policy`. Capital is a cross-cutting event, never a fourth industry layer. A downstream application company remains downstream after raising money. 8. Save the final document as `outputs/briefing_YYYY-MM-DD.md` in the project directory. Then run the bundled validator and return a clickable local file link only after it passes: ```bash python skills/daily-briefing/scripts/validate-briefing.py \ "outputs/briefing_$(date +%F).md" ``` When invoked from an installed skill, use the absolute path to `validate-briefing.py`. If validation fails, rewrite the named entries and run it again; do not silently return a partial draft. After validation passes, generate the companion interactive HTML by default: ```bash python skills/daily-briefing/scripts/render-briefing-html.py \ "outputs/briefing_$(date +%F).md" ``` The renderer preserves the final Codex-edited Markdown and creates `outputs/briefing_YYYY-MM-DD.html`. It is a standalone file with local search, section navigation, confidence and industry-layer filters, browser-local bookmarks, Markdown download, print/PDF support, theme switching, and copy buttons for code or X drafts. It must not require a web server or external CDN. Return clickable links to both Markdown and HTML. If the user explicitly asks for only one format, honor that request. 9. Keep every factual claim within the candidate packet or the first-party pages/posts checked above. Preserve original URLs and numbers. Do not invent missing dates, scores, funding amounts, customer counts, revenue, benchmark results, or source agreement. 10. Use clickable Markdown titles. Immediately below every retained news, model release, product update, funding event, official X post, and GitHub project, write a specific one- or two-sentence Chinese explanation before metadata or scores. It must identify who did what and include the most useful result, number, availability change, or impact supported by the source. A reader should understand the event without opening the link. Do not use generic filler such as `值得关注`, `可作为行业观察`, or `核心看点是工具链还不成熟` as the explanation. Do not add labels such as `中文总结`, `摘要`, or `一句话介绍` before explanations. 11. Use these sections in order when relevant: ```text 相比昨天的新变化 AI 产业链全景 产业链联动 最新模型发布与更新 产品与工具更新 应用层趋势 可能爆火的 AI 新闻 AI 投融资与商业化 模型公司官方账号动态 今日必须看 适合发 X B端/商业机会 持续跟踪 X 草稿 来源明细 ``` 12. Distinguish source confidence as `官方确认`, `多源印证`, or `单源信号`. Funding and commercial figures marked `待官方复核` must not be written as confirmed. If a source fails, retain successful sections and name the failed source briefly. 13. Make X drafts structurally different: include at least a single post, a thread, and a visual or video script when the candidate pool supports them. 14. End every final Markdown briefing with a concise bilingual support section. Use the heading `支持这个项目 / Support the Project`, invite readers in natural Chinese and English to Star the project if the briefing was useful, and include the canonical repository link exactly once: `https://github.com/Rion-Wu-tech/ai-daily-briefing`. Keep this section brief and place it after source notes so it does not compete with the briefing. 15. Before saving, audit every clickable item in the final Markdown. Reject the draft if any item is followed only by a source, tag, score, confidence label, or link. Also reject repeated template explanations that could describe several unrelated stories. Rewrite those entries from `raw_summary` and the original source facts. The bundled validator enforces the minimum structural and anti-filler checks; editorial factuality still requires Codex review. ## Official Model Release And Update Radar Treat model launches and material model updates as a separate first-party intelligence track, not as ordinary hot news. 1. Check `sections.model_releases` before general headlines on every run, including when the user only asks for an AI briefing. This section includes launches plus material version, capability, availability, pricing, deprecation, and open-weight updates. 2. Confirm a launch with a vendor website, official RSS/sitemap, official changelog, official model card, or official GitHub Release. Media coverage may explain impact but cannot replace the release source. 3. For each model release, preserve the original title, exact source URL, `published_at`, `release_kind`, and any concrete model ID supplied by the packet. 4. Explain what changed, who can use it, how it is available (API, product, or open weights), and which important details remain unverified. Never infer pricing, benchmarks, context length, license, or availability. 5. Keep release announcements separate from Hugging Face Trending and social heat. A trending model is a popularity signal unless it comes from a configured first-party organization. 6. If no official model release appears in the requested time window, say so plainly instead of filling the section with rumors or older launches. ## Official X Account Radar Treat official X posts as a fast first-party signal layer. They are not a substitute for product documentation when exact API details matter. 1. Cover both US and China account groups from `sections.official_x_watchlist`; do not silently search only English-language labs. 2. Prefer original posts over reposts, quote-post commentary, screenshots, aggregators, or employee accounts. 3. For a model launch announced on X, preserve the direct post URL and then look for the matching vendor page, model card, changelog, or repository. Link both when available. 4. Include launch previews, availability changes, pricing/API notices, open-weight releases, benchmark reports, safety notices, and major product integrations when they are material. 5. Keep X engagement as a heat signal only. Likes and reposts do not increase factual confidence. 6. If X blocks or rate-limits retrieval, name that limitation and continue with official websites and model cards. 7. Cover three groups separately: foundation-model companies, AI coding/agent products (Claude Code, Codex, Cursor, Copilot, Devin, Replit), and multimodal model products (Runway, Stability, FLUX, Midjourney, Luma, Pika, Kling, Vidu). Do not call every product a foundation-model vendor. ## Targeted Briefings Preserve the full source pool and default complete briefing. Apply targeting only when the user asks for it. - `--mode models`: model launches and model updates, including version upgrades, capability changes, API availability, pricing, deprecations, and open-weight changes. - `--mode products`: AI products, features, APIs, workflows, integrations, and pricing changes. - `--mode applications`: downstream products, customer adoption, user growth, pricing, revenue, and industry deployment. - `--mode funding`: AI financing, acquisitions, IPOs, and earnings across all three industry layers, with primary-source verification status. - `--mode industry`: the upstream/midstream/downstream view, cross-layer topic connections, application trends, and capital/commercial events. - `--mode hotspots`: potentially viral AI stories from the requested period, while retaining confidence labels and original links. - `--focus "AI视频"`: limit the editorial result to a named track or industry. Accept natural topics such as Agent, AI coding, image/video, healthcare, finance, education, robotics, chips, search, or open source. - `--hours 48`: retain verifiable items from the requested time window. For date-only sources, state that the exact boundary is uncertain instead of inventing a timestamp. These options can be combined. Example: `--mode products --focus "医疗" --hours 168`. ## Deterministic Fallback If the Codex editorial pass cannot be completed, generate a complete linked Markdown file directly: ```bash skills/daily-briefing/scripts/run-daily-briefing.sh \ --format markdown \ --output-file "outputs/briefing_$(date +%F).md" ``` ## Validate The Workflow For installation or regression checks, run: ```bash skills/daily-briefing/scripts/run-daily-briefing.sh --dry-run --no-save ``` ## Record Feedback Resolve the item URL from the current Markdown or editorial packet, then run one action without asking the user to repeat the link: ```bash skills/daily-briefing/scripts/run-daily-briefing.sh --feedback "" --feedback-action opened skills/daily-briefing/scripts/run-daily-briefing.sh --feedback "" --feedback-action saved skills/daily-briefing/scripts/run-daily-briefing.sh --feedback "" --feedback-action drafted skills/daily-briefing/scripts/run-daily-briefing.sh --feedback "" --feedback-action published skills/daily-briefing/scripts/run-daily-briefing.sh --feedback "" --feedback-action dismissed ``` ## Generate A Weekly Review ```bash skills/daily-briefing/scripts/run-daily-briefing.sh --weekly-review ``` The default file is `outputs/weekly_review_YYYY-MM-DD.md`.