linkedin-writer
LinkedIn-native long-form article and newsletter writing workflow for LinkedIn and Google-to-LinkedIn topic discovery, business-depth research, professional thought leadership, evidence-led drafting, final humanization, discussion design, SEO settings, auditing, and publish-ready
供给资产档案
研究与知识工作
Deep research, source comparison, literature review, RAG, knowledge search, and reports.
场景
研究 Agent
I need my agent to research a topic, compare sources, and produce a concise report.
适配 Agent
Claude Code + CLI + Codex
适用于 Codex、Claude Code、Cursor、CLI 或自定义 Agent。
安装
就绪
npx skills add flaqai/backlink_skills --skill linkedin-writer
维护状态
新鲜
距上次推送 1 天
风险
需审查
Financial research output is not financial advice; require human review before any live investment decision
GitHub 质量
483
73/100 质量 · 74/100 信任
覆盖标签
审查说明
Financial research output is not financial advice; require human review before any live investment decision · The provided excerpt of the audit script (audit-linkedin-markdown.mjs) is incomplete; full review of its code could not be performed, but no obvious security risks were observed in the visible portion.
Agent 采用评分卡
一眼查看信任、审计与安装准备度
这些分数综合公开仓库元数据、OpenAgentSkill 审查信号、维护新鲜度与安装准备度。它用于候选筛选,不替代人工审查。
质量
强可靠的选择,值得加入生产工作流候选列表。
信任
仅限沙盒有用但信任信号不足或混杂的候选项。在结果闭环证明任务匹配前,请保持在隔离工作区内使用。
审计
需审查对安装准备度、安全元数据、维护情况与采用风险的机器可读审查。
OpenAgentSkill 信任评分 v5
安装前需人工审查
仅在沙盒中运行,并在用于真实工作前比较接近的替代方案。
Stars
483 个 GitHub Stars
仓库活跃度
483 个 Star,175 个 Fork
维护状态
距上次推送 1 天
许可证
MIT
安装
npx skills add flaqai/backlink_skills --skill linkedin-writer
安装安全性
标准软件包或运行时安装路径
权限范围
文件系统或文档访问
Agent 结果
暂未有 Agent 结果数据
文档
README/SKILL.md 上下文充分
风险摘要
生产前审查
- The provided excerpt of the audit script (audit-linkedin-markdown.mjs) is incomplete; full review of its code could not be performed, but no obvious security risks were observed in the visible portion.
- Financial research output is not financial advice; require human review before any live investment decision.
- Quality score needs review
安装准备度
安装路径可用
- 安装路径可用
- 仓库证据可用
- 已声明许可证
- 暂无 Agent 验证结果证据
Agent 可读元数据
这个 Skill 的机器可读决策数据。
使用此区块或内嵌 JSON 判断 Agent 是否应安装该 Skill、选择替代方案,或先请求人工审查。
适用任务
- 研究 Agent 工作流
- Claude Code 团队
- builders willing to evaluate younger projects
- 检索来源
适用 Agent
安装决策
- 命令
- npx skills add flaqai/backlink_skills --skill linkedin-writer
- 策略
- 审查
- 人工审查
- 是
信任与风险
- 信任
- 66/100
- 审计
- 81/100
- 风险级别
- 需审查
结果闭环
- 端点
- /api/agent/outcome
- 事件 ID
- resolve
- 结果
- 5
不适用场景
- 需要厂商支持 SLA 的团队
- production agents without a repository review
- The provided excerpt of the audit script (audit-linkedin-markdown.mjs) is incomplete; full review of its code could not be performed, but no obvious security risks were observed in the visible portion.
- Financial research output is not financial advice; require human review before any live investment decision
- The skill references external parent skill files (e.g., fact-check, humanization, R2 upload) that are not included in this submission; proper integration depends on those files being present.
Agent 安全 v2
57/100 · 安装前审查
Sparse or mixed signals. Useful for discovery, but not for autonomous installation.
Test manually in an isolated workspace and compare against safer alternatives.
中
Browser automation
Skill may drive a browser or interact with web pages.
中
网络访问
Skill 可能访问远程页面、API、仓库或外部服务。
中
文件系统访问
Skill 可能读取或写入项目文件、文档、生成产物或本地工作区状态。
中
数据库访问
Skill 可能检查 Schema、查询数据库或处理持久化存储。
- Financial research output is not financial advice; require human review before any live investment decision
安装目标
在你的 Agent 工作流中安装此 Skill
通过公开安装端点获取命令、安全清单、目标提示词和该 Skill 的规范链接。
OpenAgentSkill CLI
Resolve policy, run the source installer safely, and report a verified install receipt.
$ npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.2.1/openagentskill-0.2.1.tgz install flaqai-linkedin-writerAgent 解析计划
让 Agent 在安装前验证匹配度。
Resolve API 返回首选 Skill、替代方案、安全策略、审计说明、安装目标和可直接执行的提示词,无需抓取此页面。
打开 JSON
/api/agent/resolve?task=Use%20linkedin-writer%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Resolve 文本
/api/agent/resolve?task=Use%20linkedin-writer%20for%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text
安装交接
/api/skills/flaqai-linkedin-writer/install
Agent 应检查
- 从 Resolve API 检查任务匹配与替代方案。
- 检查审计评分、信任评分和安全策略警告。
- 检查 Codex、Claude Code、Cursor 或 CLI 的安装目标兼容性。
复制提示词
Task: Use linkedin-writer in this workspace.
Resolve first: https://www.openagentskill.com/api/agent/resolve?task=Use%20linkedin-writer%20for%20an%20agent%20workflow&agent=codex&max_risk=medium
Review install handoff: https://www.openagentskill.com/api/skills/flaqai-linkedin-writer/install
Install command: npx skills add flaqai/backlink_skills --skill linkedin-writer
Before running it, summarize audit warnings, required permissions, and the fallback skill if install is risky.Agent 交接
把安装路径交给 Agent,而不是再给一个目录页。
通过公开安装端点获取命令、安全清单、目标提示词和该 Skill 的规范链接。
安装交接
/api/skills/flaqai-linkedin-writer/install
LLM 文本格式
/api/skills/flaqai-linkedin-writer/install?format=text
寻找替代方案
/api/skills/search?q=linkedin-writer&limit=3
Agent 提示词
Use linkedin-writer for this task. Review https://www.openagentskill.com/api/skills/flaqai-linkedin-writer/install, then install with: npx skills add flaqai/backlink_skills --skill linkedin-writerRegistry 元数据
用于自动选择 Skill 的 Agent 可读档案。
本页通过 Registry API 提供相同的决策、信任、审计、场景和安装信号,让 Agent 无需抓取界面即可排序。
Agent 决策面板
Companion skill for Research agents
将此 Skill 加入候选列表,并在生产使用前与相近替代方案比较。
栈中角色
辅助 Skill
主要匹配
研究 Agent
信任标签
强候选
安装路径
命令已就绪
适用场景
- 研究 Agent 工作流
- Claude Code 团队
- builders willing to evaluate younger projects
证据
- 仓库近期活跃
- 已提供安装命令或 GitHub 仓库
- 73/100 质量档案
- 12 个 OpenAgentSkill 交互事件
先审查
- The provided excerpt of the audit script (audit-linkedin-markdown.mjs) is incomplete; full review of its code could not be performed, but no obvious security risks were observed in the visible portion.
实施路径
- 1在沙盒 Agent 中安装它,并端到端完成一次研究 Agent任务。
- 2Compare output quality, latency, and failure behavior against at least one alternative.
- 3Promote it into production only after reviewing repository permissions, license, and maintenance signals.
信任档案
仅限沙盒
有用但信任信号不足或混杂的候选项。在结果闭环证明任务匹配前,请保持在隔离工作区内使用。
GitHub 采用度
信息483 个 GitHub Stars
Star/Fork 活跃度
信息483 个 Star,175 个 Fork; 当前元数据中没有议题活跃度信息
近期维护
通过距上次推送 1 天
许可证清晰度
通过MIT
积极信号
- AI 审查已通过
- 安装路径可用
- 仓库证据可用
- 近期维护的仓库
- 安装命令未发现明显高风险模式
- 结果闭环已就绪,但需要首次真实 Agent 运行
安装前审查
- The provided excerpt of the audit script (audit-linkedin-markdown.mjs) is incomplete; full review of its code could not be performed, but no obvious security risks were observed in the visible portion.
- Financial research output is not financial advice; require human review before any live investment decision.
- Quality score needs review
- 暂未有真实 Agent 结果报告
- 无人值守安装前需要人工审查
建议操作
仅在沙盒中运行,并在用于真实工作前比较接近的替代方案。
质量档案
强 适用于 Agent 工作流的候选
可靠的选择,值得加入生产工作流候选列表。
工作流匹配
在这些场景使用此 Skill
Investigate faster
Research agents
I need my agent to research a topic, compare sources, and produce a concise report.
Automate repeated work
Workflow automation
I need my agent to automate a repeated workflow across tools and files.
Verify behavior
Testing and QA
I need my agent to test a web app, reproduce bugs, and verify fixes.
工作流匹配
加入完整工作流
Find, compare, and synthesize
Research report agent
A workflow for agents that gather sources, compare claims, summarize long material, and draft useful research briefs.
Turn skills into distribution
Content growth agent
A workflow for turning newly indexed skills into SEO briefs, social drafts, comparison pages, and reusable publishing workflows.
Ingest, retrieve, and cite
RAG knowledge base
A workflow for document-heavy agents that ingest files, create searchable knowledge, retrieve relevant context, and answer with grounded sources.
替代方案短名单
安装前对比
可能适合该任务的相近 Skill。
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Maigret
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Infisical
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概览
--- name: linkedin-writer description: LinkedIn-native long-form article and newsletter writing workflow for LinkedIn and Google-to-LinkedIn topic discovery, business-depth research, professional thought leadership, evidence-led drafting, final humanization, discussion design, SEO settings, auditing, and publish-ready packaging. Use when creating, outlining, researching, enriching, rewriting, humanizing, auditing, or packaging LinkedIn Articles, LinkedIn newsletters, LinkedIn long-form posts, LinkedIn thought leadership, LinkedIn B2B articles, LinkedIn 长文, LinkedIn 专栏, LinkedIn 话题调研, LinkedIn 商务内容, 去 AI 化编辑, or LinkedIn 发布包. ---
# LinkedIn Writer
## Goal
Turn a topic, product, argument, report, source bundle, or existing draft into a credible LinkedIn-native long-form article that helps a defined professional audience make a decision, understand a change, or improve how they work.
This skill reuses the parent `writer` workflow for fact checking, humanization, image packaging, and optional Cloudflare R2 delivery, but it does not treat LinkedIn as a generic SEO blog host or as Medium with a different publishing button.
LinkedIn-native writing prioritizes:
- a specific professional reader and work context; - a defensible point of view or useful decision framework; - current LinkedIn search and conversation signals; - expertise demonstrated through evidence, examples, and boundaries; - short, skimmable sections for busy readers; - a discussion-worthy close rather than a generic sales conclusion; - a complete native publishing pack, including LinkedIn SEO settings.
Default language follows the user's request. If the user gives no language, use the language of their source material or target audience.
## Format Routing
Use the requested format, not a blended default:
| Destination | Workflow | |---|---| | LinkedIn Article or LinkedIn newsletter edition | Use this skill in full | | LinkedIn short feed post only | Use the short-post rules and publishing pack in this skill; do not force a long article | | Google-first website article | Use `../SKILL.md` | | Medium article or third-party editorial essay | Use `../medium-writer/SKILL.md` | | Chinese WeChat Official Account article | Use `../wechat-writer/SKILL.md` |
If the user says only “LinkedIn article” or “LinkedIn long-form,” default to a native LinkedIn Article. If they already run a newsletter and provide its name or theme, package the piece as a newsletter edition. Do not claim a newsletter was created or published without direct evidence.
## Required References
Read each relevant file completely before acting:
- Topic discovery, LinkedIn search, trend expansion, and current seed topics: `references/linkedin-topic-research.md` - Google-to-LinkedIn discovery, business-depth enrichment, and final LinkedIn humanization: `references/linkedin-business-depth-and-humanization.md` - New article, rewrite, or reusable output format: `references/linkedin-article-template.md` - Article review, scoring, and revision gate: `references/linkedin-review-rubric.md` - Fact-heavy claims, comparisons, current products, or statistics: `../references/fact-check-and-style.md` - Final natural-language edit after factual and structural fixes: `../references/humanization.md` - Images, local paths, file packaging, and optional R2 delivery: `../references/output-packaging.md` - R2 upload tasks only: `../references/r2-image-upload.md` and `../references/r2-security.md`
Do not load unrelated references merely because they exist.
## Non-Negotiable Boundaries
1. Do not invent professional experience, product testing, customers, interviews, internal data, quotes, results, credentials, or events. 2. First-person events may appear only when the user supplied them for this task or they exist in an approved, attributable source package. 3. Do not turn LinkedIn search result counts, reactions, comments, or repeated phrases into search-volume claims. 4. Do not call a topic “trending,” “viral,” or “hot” without dated evidence. Use “recurring conversation,” “current topic seed,” or similarly bounded language when evidence is directional. 5. Do not mention or tag people and Pages merely to trigger notifications. Every suggested mention must have a content reason. 6. Do not convert a product announcement into disguised thought leadership. State affiliations, recommendations, and commercial relationships when they materially affect trust. 7. Do not use engagement bait such as “Agree?”, forced polls, empty controversy, or unrelated hashtags. Invite a concrete professional response. 8. Do not copy another LinkedIn creator's hook, framework, story, examples, distinctive phrases, or conclusion. Extract only topic signals and questions, then synthesize an original angle. 9. Do not claim publication, indexing, newsletter delivery, reach, or engagement from a completed local package. 10. Writing, generating images, uploading assets, and publishing externally are separate permission levels.
## LinkedIn Article Task Card
Before research or writing, create or infer a task card in 14 lines or fewer and save it as `linkedin-brief.md`:
- Publish as: personal profile / Company Page / unknown. - Format: standalone Article / newsletter edition / short feed post. - Professional audience: role, seniority, industry, and work situation. - Reader decision: what they should understand, compare, decide, or do. - Core thesis: one sentence the article must establish. - Expertise basis: supplied experience, verified sources, product knowledge, or editorial analysis. - Primary topic phrase: one natural phrase for LinkedIn and external search. - Related topic cluster: 4-8 entities, skills, problems, roles, or outcomes. - Conversation tension: trade-off, change, misconception, or unresolved question. - Business context: stakeholders, buying/approval path, economics, implementation, risk, and measurement dimensions that matter. - Evidence requirement: 3-6 claims that must be checked. - Target length: normally 900-1,800 words; adjust to the subject, not a platform myth. - CTA: discussion question, practical next step, subscription prompt, or disclosed product action.
Make conservative assumptions when details are missing. Ask only when audience, thesis, or authority to use personal experience is materially ambiguous.
## Working Modes
### Continuous mode (default)
Run `brief -> LinkedIn and Google-to-LinkedIn research -> business insight map -> evidence -> outline -> draft -> audit -> rewrite -> final humanization -> integrity recheck -> package` without pausing at every step. A request to “write an article” means deliver the reviewed local package.
### Topic-radar mode
When the user asks for hot topics, search ideas, or content planning, stop after the ranked topic map unless they also ask for an article. Do not draft ten shallow articles.
### Interactive mode
Pause at the topic shortlist or outline only when the user explicitly asks to choose first.
### Audit mode
If the user asks only for review, produce findings without overwriting the draft. If they ask to improve, preserve the original and apply fixes before re-auditing.
## End-to-End Workflow
### 1. Create an isolated article directory
Use:
```text writer/linkedin-writer/output/<article-slug>/ ```
This LinkedIn-specific output directory overrides the parent writer's default `writer/output/<article-slug>/` location. Prefer a short ASCII, hyphen-separated slug under 80 characters. Do not mix multiple campaigns in one directory.
Recommended working files:
```text linkedin-brief.md linkedin-topic-map.md linkedin-insight-map.md source-ledger.md outline.md draft.md article-linkedin.md linkedin-audit.md linkedin-publishing-pack.md image-plan.md ```
Create only the files the task needs. Keep `draft.md` separate from `article-linkedin.md` so an unreviewed draft cannot be mistaken for final copy.
### 2. Research LinkedIn search demand and conversation context
Read `references/linkedin-topic-research.md` completely.
Do not begin with a static list of broad trends. Build a query grid around:
```text core entity or skill × audience or role × work outcome × tension or decision × current change or timeframe ```
Use LinkedIn search suggestions and Posts results when available. Filter by recent date, content type, author industry/company, or source type when useful. Triangulate recurring questions with primary reports, official product or policy sources, credible industry research, customer questions, and the user's own content goals.
Then use Google to discover publicly indexed LinkedIn material with exact phrases, date operators, exclusions, and scoped queries such as:
```text site:linkedin.com/posts "<topic>" "<role or objection>" after:YYYY-MM-DD site:linkedin.com/pulse "<topic>" "<implementation, ROI, risk, or governance>" site:linkedin.com/company "<topic>" "<official case or report>" ```
Record Google results separately. Search snippets and LinkedIn creator claims are conversation signals, not automatically verified facts. Open the original page when possible, verify material claims elsewhere, and never copy a creator's hook, framework, structure, anecdote, or conclusion.
Record the exact query, date, filter, observed signal, and interpretation in `linkedin-topic-map.md`. Separate:
- **Observed:** directly visible search suggestion, repeated topic, question, format, or source. - **Inferred:** a possible reader need or angle derived from the observations. - **Verified demand:** use this label only when reliable demand data actually supports it.
Never imply that a topic is popular merely because it appears in one post or one search result.
### 3. Expand the topic before outlining
For the selected topic, create a useful professional topic cluster:
- Core concept: the named tool, skill, market shift, or decision. - Business outcome: time, quality, growth, cost, risk, hiring, retention, or customer value. - Role impact: what changes for practitioners, managers, executives, buyers, or candidates. - Implementation: workflow, prerequisites, governance, measurement, and failure modes. - Trade-off: what the popular framing misses or where the approach breaks. - Evidence: current data, official documentation, case material, or observable examples. - Adjacent conversation: 3-5 related topics that deepen the article without causing drift. - Discussion gap: a question qualified readers can answer from experience.
Reject adjacent topics that do not strengthen the thesis or reader decision. “More keywords” is not the same as more depth.
Read `references/linkedin-business-depth-and-humanization.md` and create `linkedin-insight-map.md`. Enrich the selected topic across the dimensions that materially affect the business decision:
- decision trigger and cost of waiting; - sponsors, users, approvers, blockers, buyers, and owners; - cost, budget, ROI, revenue, margin, or option value; - workflow, data, integration, adoption, and change management; - baselines, leading indicators, outcome metrics, and guardrails; - risk, strongest objection, failure mode, and reversibility; - one attributable or explicitly hypothetical scenario; - the next artifact, meeting, pilot, or decision the reader should initiate.
For a substantial business article, normally develop at least five relevant dimensions. Do not force irrelevant finance or governance sections into a career essay, but do not omit a material stakeholder, cost, or risk merely to keep the article simple.
### 4. Build the claim-source ledger
Create `source-ledger.md` for any current, factual, comparative, or decision-shaping article.
For each material claim, record:
- claim ID and exact claim; - claim type: fact / inference / editorial judgment / user-provided experience; - source title, publisher, date, and URL; - status: `verified`, `user_provided`, `needs_verification`, `softened`, `removed`, or `unsupported`; - where it will appear; - caveat or expiry risk. - rese
技术详情
- 版本
- 1.0.0
- 许可证
- MIT
- 最近更新
- 2026年8月21日
- 发布时间
- 2026年8月21日
决策摘要
辅助 Skill
仓库近期活跃
Agent 验证证据
Agent 验证证据
来自解析、审查、安装和一次小范围运行后的结果报告。
- 成功率
- —
- 近期失败
- —
- 结果
- 0
- 输出质量
- —
- 失败
- 0
- 不相关
- 0
- 安装次数
- 0
- 风险拦截
- 0
- 需要配置
- 0
- 生产环境
- 0
暂时没有 Agent 结果数据。首次 Agent 执行可以通过 /api/agent/outcome 报告成功、需要设置、风险拦截、失败或不相关。
增长闭环
分享工具包
为 linkedin-writer 准备的场景化草稿,可手动发布到 X。
linkedin-writer: LinkedIn-native long-form article and newsletter writing workflow for LinkedIn and Google-to-... 483 stars https://www.openagentskill.com/skills/flaqai-linkedin-writer?ref=x
可选:带安装命令的回复
Listing + install path for linkedin-writer: https://www.openagentskill.com/skills/flaqai-linkedin-writer?ref=x Install: npx skills add flaqai/backlink_skills --skill linkedin-writer
收录来源
Registry 收录
此列表来自公开来源,维护者认领获批前不会标记为官方。
- 创作者
- flaqai
- 收录方
- OpenAgentSkill 社区索引
归属链接指向公开仓库或创作者主页。创作者可认领列表以更新所有权信号。
认领此 Skill所有者认领
认领此 Skill 页面
这条 Registry 收录 列表归属于 flaqai,但尚未标记为官方。认领后可增加已验证所有者信号,使后续发布、安装和审计更新更值得信赖。
创作者外链工具包
将证据徽章加入你的 README
在开发者评估仓库的位置展示规范页面、当前信任与审计信号,以及真实的 Agent 验证证据。
[](https://www.openagentskill.com/skills/flaqai-linkedin-writer)
[](https://www.openagentskill.com/skills/flaqai-linkedin-writer)
[](https://www.openagentskill.com/skills/flaqai-linkedin-writer/audit)
[](https://www.openagentskill.com/skills/flaqai-linkedin-writer)作者
flaqai
@flaqai
平台适配
健康信号
- GitHub Stars
- 483
- 质量评分
- 42/100
- 最近 GitHub 推送
- 2026年8月21日
- 框架提示
- 未知
- OpenAgentSkill 浏览量
- 12
- 复制安装命令
- 0
- 跳转点击
- 0
社区信号
告诉我们这个 Skill 是否对你的 Agent 工作流有帮助。汇总反馈会持续改善排序。
信任与安全
仅限沙盒
- GitHub 采用度483 个 GitHub Stars信息
- Star/Fork 活跃度483 个 Star,175 个 Fork; 当前元数据中没有议题活跃度信息信息
- 近期维护距上次推送 1 天通过
- 许可证清晰度MIT通过
- README/SKILL.md 完整度元数据包含足够的用法与工作流上下文通过
- 依赖与运行时风险公开元数据中未发现主要依赖风险提示通过
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