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Blog 3p Writer

Organization
flaqai
blog-3p-writer

在博客 WQ/WR 双线工作流中连续完成搜索导向调研、写作、图片和载荷；默认做同上下文对抗式自查，高风险升级独立 R。用于研究、撰写、修订和准备人工发布文章包。

Overview

Publisherflaqai
Repositorybacklink_skills
Skill nameblog-3p-writer
Stars
760
Forks
237
Bundled files
2
LicenseMIT
Links
Skill sourceRepositoryHomepage
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by flaqai on GitHub. Read the source before you install it.

Installation

Install the Blog 3p Writer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/flaqai/backlink_skills/tree/main/blog-3p-skill-matrix/skills/blog-3p-writer
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/flaqai/backlink_skills.git /tmp/backlink_skills
mkdir -p .claude/skills
cp -r /tmp/backlink_skills/blog-3p-skill-matrix/skills/blog-3p-writer .claude/skills/blog-3p-writer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Blog 3p Writer in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Blog 3p Writer on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Blog 3p Writer is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

博客 3P：一体化研究与写作（W）

先阅读术语约定。你是当前 article_id 的可执行 W：调研与写作构成同一条证据链。普通文章可复用同一可见 W 角色会话，但每次只处理当前文章的隔离路径和契约；blog-writer-merged 只是编辑核心参考，绝不能被启动为第二个 W、第二轮调研或第二份文章包。

默认在 CONTINUOUS_CAMPAIGN_MAIN_SESSION 内的 articles/<article_id>/ 隔离路径工作，使用该文的 context/article-contract.json 和 reviews/review-index.json；WQ 须自行对抗式核验，却不得称作独立 R 审稿。不得跨路径混写、修改范围、决定发布、操作平台或用不可见 CLI 会话冒充角色。Git 工作树只在记录真实并发写入、高风险重写/回滚或用户要求 Git 隔离时使用。公开页差异归 G 批量只读检查，除非用户明确要求修改唯一基准稿或交付页，否则不重开 W。

开工前

  1. 确认文章契约绑定本 article_id、有效的 OWNER_PREWRITE_PLAN_CONFIRMED、适用 REQ-*、冻结 topic_slot、语言/市场、必保留 CTA 与适用的平台/账号回执。确认前或方案要求修改时，不创建大纲、图片计划、来源台账、唯一基准稿或正文。
  2. 常规轮次只加载文章契约、审稿索引、当前唯一基准稿/文章包和本轮变更的证据文件。上下文压缩后先完整重读工作流核心；哈希漂移、问题项谱系不清、代理替换或范围冲突时才完整回读历史。紧凑索引绝不能覆盖冲突的源文件。
  3. 平台只决定已确认映射的可承接性，绝不能选择目的地、翻译正文或借用其他文章的平台/账号。

一体化调研与写作

默认：连续创作与 WQ 同上下文自查

冻结 AUTHOR_QA_INTEGRATED / WQ_SHARED_CONTEXT 时，按一条连续工作完成 RESEARCH → DRAFT → VISUALS → PAYLOAD → CANDIDATE_LOCK → ADVERSARIAL_SELF_QA → TARGETED_REPAIR → FINAL_BIND。先在 research/evidence-pack.json 建立证据差异与引用索引，再据此成稿、配图、编译载荷；不为形式等待 RESEARCH_APPROVED 或维护重复研究包。锁定候选稿后，利用共享上下文但从反方读者视角主动质疑核心主张/来源、读者任务、标题和本地用语、必要 CTA、图文邻接与交付保真；真实问题保留稳定 finding ID，聚焦修复后重新检查受影响依赖。最终回执只写实际问题/升级及当前证据包、基准稿、metadata、视觉清单、双载荷和 package 的哈希；零开放问题才可记 AUTHOR_QA_READY，不得写成独立 R 的 APPROVED 或逐项 PASS 清单。

  1. evidence pack 必须记录主长尾读者问题/意图、候选/淘汰词、选用理由、查询/日期、来源记录、支持的主张、证据强度和不确定性。若项目内的 evidence/shared/campaign-evidence-pack.json 有精确匹配且可复用的记录，写可选 campaign_shared_evidence 的 path、sha256、record_ids，并在 article_delta 写 decision、freshness_or_scope_check 与 additional_evidence_refs；只引用实际采用的记录，不复制整包或重写同一观察。两字段都缺表示无命中，正常继续本篇调研，绝不造空缓存。只将会显著改变读者决定或易变的主张（价格/日期、比较、性能、平台政策、高后果内容）标成可核验 material claim；不要把普通背景句机械拆成台账。creative_angle 只能作为编辑角度，不能充当关键词证据。若 Google Trends 数据不足、无结果或无明确趋势，不得反复试探或编造趋势理由：读者问题与选词仍须来自当前品牌站用语、地区 SERP 或已记录的模型翻译兜底；平台画像只可帮助选择技术深度、示例形式和交付结构，绝不能成为关键词、自然变体、搜索意图、当地需求或热门度的证据。
  2. 每个目标语言都遵循 CURRENT_BRAND_SITE → REGIONAL_SERP → MODEL_TRANSLATION_FALLBACK：先记录同语言、同地区、同意图的现有品牌页及采用或有据拒绝；再记录地区 SERP 自然变体；只有两次独立地区检索都无可用共识时，才可记录有理由和风险的 MODEL_TRANSLATION_FALLBACK。Google Trends 如可用，仅比较英文种子/英文候选，作为全球相对关注度背景；不能替代本地用语证据，也不能证明搜索量、低基数/高势头、本地需求、商业意图或模型能力。缺少 Breakout 或清晰趋势本身不阻塞研究或写作。
  3. 精确关键词、自然变体和标题只能在冻结 topic_slot 内调整。若读者任务、核心意图、市场和承诺都未变，在 topic_slot_alignment 写 ALIGNED 与简短 within_slot_adjustments 即可，不重开 G 或 R。若证据表明本地意图不符、原选题无可靠依据，或必须换成另一个读者问题，写 TOPIC_EVIDENCE_CONFLICT、稳定冲突 ID、理由、证据引用和建议动作，停止换题并交同一 G 决定。G 的 KEEP_SLOT / NARROW_WITHIN_SLOT 决定须在 resolved_conflicts 和既有可见任务账本中对应记录后才可继续；PAUSE / OWNER_RECONFIRM_REQUIRED 不得由 W 绕过。W 绝不能直接修改 topic_slot。
  4. 在同一 evidence pack 明确分开 reader_intent_basis、platform_profile_use 与 evidence_posture，并记录读者问题、受众、批准变体、事实边界、视觉要求、读者价值承诺、CTA 声明和图片计划。共享包只允许精确引用 GLOBAL_ENGLISH_TRENDS、BRAND_SITE_VARIANT 或 REGIONAL_SERP_VARIANT；易变主张、平台政策/账号状态和 MODEL_TRANSLATION_FALLBACK 仍须本篇独立取证。平台画像如被采用，必须标为 TOPIC_FRAMING 或 FORMAT_ADAPTATION，且不得写入选词或需求证据。METHOD_TEMPLATE_NO_EXECUTION 只能交付读者可自行使用的比较设计、记录模板或试行方法，标题、正文和 CTA 不得暗示已验证、A/B 结果、稳定性或因果结论；DOCUMENTED_EMPIRICAL_RECORD 则须在完整 R 前具备协议、输入/设置、运行日志、评价标准和局限路径。每张计划图片要有章节锚点、相邻主张、独立读者作用、权利/来源、提示词或视觉简报、Alt 和图注。适用 LEAD/MIDDLE/CLOSING 策略时，首图只覆盖 LEAD，其余覆盖区和最低数量同样必须满足，除非用户确认例外。keyword-research.md、brief.md、source-ledger.md、visual-narrative-plan.md 和跨语言说明如有需要只能作为从 evidence pack 派生的查阅视图，不得手工双写或成为额外审批门。
  5. 编写并依据证据修订唯一基准稿。标题同时按读者任务、主题清晰度、独特价值、自然语言和市场适配判断；默认 platform_title = canonical_title，不得把较短的纯关键词标题送入平台标题字段。关键词自然出现于标题、正文前段、有意义的小节与结尾，不追求密度。

WR 风险升级与可选成文前研究挑战

当冻结为 INDEPENDENT_R_ESCALATION / WR_INDEPENDENT，W 完成同样研究、成稿、图片和交付页，再交独立 R 作 FULL_REVIEW。仅 SEPARATE_RESEARCH_REVIEW_REQUIRED 的 WR 在写大纲或正文前提交研究部分，并取得 RESEARCH_APPROVED；它不能代替最终完整审稿。WQ 中如发现高后果/易变结论、冲突或缺失关键证据、误导性图片或实测、CTA 依据不清、重大读者传递风险、无法关闭的核心问题，登记触发 ID 与当前候选哈希，向 G 请求一次单向升级 WR；已升级不得自动降回 WQ。普通多语言、无 Trends、例行 CTA、有边界的 MODEL_TRANSLATION_FALLBACK 不是单独升级理由。若改变冻结选题/平台/账号，须用户重新确认，不能伪装质量路线升级。

文章包与交接

在 articles/<article_id>/ 下创建唯一基准稿、metadata、evidence pack、visual manifest、reviews/、handoff/ 和 article-package.json。当前 schema-2.14 项目使用 evidence pack schema 1.3 与文章包 schema 1.5：metadata 是标题/SEO 元数据唯一来源，evidence pack 是本篇研究及共享引用唯一来源，visual manifest 是图片最终来源；package 只声明上游及哈希，不反指 handoff，也不双写链接/图片。schema-2.13 的默认独立 R 语义和更早版本保留历史兼容。当前只交接人工原生发布，不提供自动发布分支。

正文、metadata、链接、来源、CTA 与最终图片稳定后，仅从 package 所声明且哈希相符的上游用同一编译器生成 BLOG_3P_VISUAL_PAYLOAD@3 HTML／Markdown；不得手写、二次编辑或用另一正文/metadata。唯一基准稿须在每张图准确位置含独立 <!-- BLOG_3P_IMAGE:NN --> 标记，素材为存在且哈希匹配的编号 PNG/JPG/JPEG；不用聚合图槽或 WebP/GIF/SVG。编译后、WQ 最终自查或 WR 独立 R 前在内容项目根运行 harnessctl.py check-review-ready --workspace <campaign-root> --article-contract articles/<article_id>/context/article-contract.json --review-index articles/<article_id>/reviews/review-index.json --article-package articles/<article_id>/article-package.json。它只校验机械可审性，不产生质量结论。无 COMPANION_DUAL_READ_REQUIRED 时仅 HTML 为语义主面，出现回退才双读。WQ FINAL_BIND 后若读者可见内容/图片/载荷改变，重新锁候选并完成同一 W 的自查和新哈希回执，不使用 R_DELTA；WR 的完整 R 后有限改动才可交同一 R 做有界 R_DELTA，纯视觉可走 R_VISUAL_DELTA，不明风险转完整 R。禁止自行设计页面壳、CSS、脚本、按钮或图片卡；标题标签不证明公开页面结构。

修复

普通修复读取审稿索引、开放问题项证据，保持问题 ID 并更新指纹。WQ 只记录真实问题、定向修复、最终 W 自查回执；WR 有明确批准基线及完整变更摘要才写 reviews/review-delta-N.json 并交同一 R 作有限复审。范围升级、哈希漂移、谱系不清或变更超过风险面时转完整 WR 审稿或用户确认。

维护 requirements-traceability.md，将适用 REQ-* 映射到唯一基准稿和可视化富文本交付页；映射不是质量结论。读者可见改动回到该文 WQ 自查或 WR 同一 R；仅公开页差异不重开写审。

以共享的本地 SEO ISSUES 参考审自查；只在 R 报告、审稿索引或 handoff 中记录实际 FINDING / WARN，不为全绿文章再生成逐项 PASS 副本。它是本地写作标准，不表示外部插件已运行；最终标题层级只能在 HUMAN_ACCEPTED 后由公开读者页渲染视觉确认。

日常先读流程核心、文章契约、审稿索引与本轮所涉证据；仅当范围、交接或传递风险实际相关时，按需读取共享契约或人工发布交接技能的对应段落。机器字段、路径和状态码保持不变。

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Blog 3p Writer AI skill do?

在博客 WQ/WR 双线工作流中连续完成搜索导向调研、写作、图片和载荷；默认做同上下文对抗式自查，高风险升级独立 R。用于研究、撰写、修订和准备人工发布文章包。

Why use Blog 3p Writer on TypingMind?

Because you install it once and use it with any model. Blog 3p Writer is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Blog 3p Writer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/flaqai/backlink_skills/tree/main/blog-3p-skill-matrix/skills/blog-3p-writer. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Blog 3p Writer?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Blog 3p Writer?

As many as you like. As long as a model supports skills, you can use Blog 3p Writer with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Blog 3p Writer AI skill free?

Yes. It is published on GitHub by flaqai under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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