跳到正文
cccskills
免费在 GitHub 公开

create-linkedin-content

Draft voice-tuned LinkedIn post variants from a free-form brief. Reads a personal voice guide (generated via generate-voice-guide), produces 2–5 variants with distinct framings, applies LinkedIn-specific defaults (arrow bullets, "why this matters" beat, 150–500 words), and self-checks against the voice guide's banned phrases before returning.

中文简介正在准备中。当前显示原文说明。

集成工具LinkedIn

实测评分 未实测

查看安装方法

包含的文件(2)

  • SKILL.md6.4 KB
  • skill.meta.json282 B

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Create LinkedIn Content

Draft LinkedIn post variants that sound like a real human practitioner, not a LinkedIn thought-leader persona. Reads a voice guide the user has already generated (or prompts to create one), produces multiple framings of the same idea, and saves each variant as its own markdown file with frontmatter.

This is an agent-executed skill — the agent does the drafting and self-check inline. No Python script.

Mirrors create-x-content structurally; differences are marked ★.

Quick Start

/create-linkedin-content --brief "New open-source CLI that turns Figma files into React components. Called figma2react. Free, MIT licensed."

Or interactively:

/create-linkedin-content

Inputs

FlagRequiredDefault
--briefYes (asked interactively if missing)—
--variantsNoSkill decides based on brief richness (2–5)
--voice-guideNoResolved via chain below
--outputNo./content/YYYY-MM-DD-<topic-slug>/
--topicNoDerived from brief

Voice Guide Resolution

Resolve in this order, stop at first hit:

  1. --voice-guide <path> flag
  2. ~/.goose-skills/config.json → voice_guides.linkedin
  3. ~/.goose-skills/voice-guides/voice-linkedin.md (default path)
  4. Fallback prompt — no guide found. Three options:
    • (a) Paste a path to an existing guide
    • (b) Run /generate-voice-guide --platforms linkedin now to create one (recommended)
    • (c) Proceed with a neutral default (warn that variants will sound generic)

LinkedIn-Specific Defaults ★

Apply these unless the voice guide explicitly says otherwise:

  • Length: 150–500 words per variant (LinkedIn rewards longer than X but shorter than a blog post)
  • Bullets: arrow style (→) for workflow steps — this is LinkedIn-native
  • Numbered steps: emoji numbers 1️⃣ 2️⃣ 3️⃣ allowed sparingly for installation/quickstart type posts
  • "Why this matters" beat: every post should have one — LinkedIn audiences want the so what? more explicitly than X audiences
  • Hashtags: 0–2 max. Prefer zero.
  • CTAs: "Link in comments" pattern is LinkedIn-native; inline links are fine too. Avoid "Follow me for more."
  • Openings: more scene-setting is ok (1–2 lines of context before the hook). Unlike X, you don't have to hook-first.

Workflow

Phase 1 — Load the voice guide

Same as create-x-content Phase 1 — load voice guide, extract banned phrases, hook patterns, format rules, dos/don'ts, examples.

Phase 2 — Parse the brief and decide variant count

Same 2–5 decision rubric. LinkedIn briefs often warrant fewer variants than X because LinkedIn posts are longer and the audience expects more narrative coherence — 3 strong framings usually beats 5.

Phase 3 — Generate variants with LinkedIn-appropriate framings

Framings tuned for LinkedIn (subset of the X framings, plus a few LinkedIn-native ones):

FramingStructureWhen to use
builder-story ★"I built X. Here's why. Here's what I learned."LinkedIn-native — builder stories outperform tactical posts here
product-launch ★Clear hook + 2-step quickstart + linkNew tool/product announcements
problem-firstOpen with the pain, then solutionUniversal
ecosystem-mapCurated list of N related tools/companiesLandscape posts
contrarian-insight"Your X might be worth more than Y"Opinion pieces with a fresh take
workflow-tutorialArrow-bulleted step-by-step, with numbersTactical how-tos (denser than X version)

Phase 4 — Self-check against the voice guide

Same 4 checks as create-x-content:

  1. Banned phrase check
  2. Length check (150–500 for LinkedIn)
  3. "Meat" check — specific tools/numbers/builds
  4. Voice-match spot check

Plus LinkedIn-specific: 5. Arrow-bullet check — if the post uses lists, are they → style (or numbered emojis), matching the LinkedIn convention? 6. "Why this matters" check — does the post explain the stakes, or does it just state the facts? Add a "why this matters" beat if missing. 7. No corporate tone — does it sound like a press release? If yes, rewrite to sound like a human practitioner.

Phase 5 — Save outputs

File naming (the linkedin- prefix distinguishes from X variants when they live in the same folder):

linkedin-<letter>-<framing-slug>.md

Examples: linkedin-a-builder-story.md, linkedin-b-product-launch.md.

Frontmatter:

---
id: <topic-slug>-li-<letter>
platform: linkedin
format: standard | long
topic: <slug>
framing: <framing-slug>
status: draft
---

Phase 6 — Deliver

Print output directory, file list, framing summary, suggested next step.

Outputs

  • <output>/linkedin-<letter>-<framing>.md per variant

Examples

Builder story brief:

/create-linkedin-content --brief "Built a CRM for my AI agents using just markdown files and Claude Code. No Salesforce, no HubSpot. Works better than both."

→ 3 variants (builder-story, contrarian-insight, workflow-tutorial)

Launch brief:

/create-linkedin-content --brief "Launching goose-aeo — open-source CLI that measures your brand's visibility on AI search engines like ChatGPT, Perplexity, Gemini. npm install, three commands to run."

→ 3 variants (product-launch, problem-first, workflow-tutorial)

Dependencies

  • A voice guide at the resolved path
  • generate-voice-guide skill (for creating one when missing)

Tips

  • Arrow bullets are a LinkedIn signal. When in doubt, use them. They make the post scan-friendly and land as "LinkedIn-native."
  • Never open with "I'm humbled" or "Grateful for..." — LinkedIn performative cringe. The voice guide should flag these; honor it.
  • Cross-reference ecosystems. LinkedIn readers like knowing the landscape. If you mention a tool, it's fine to note 1–2 comparable ones.
  • End with a forward-looking line. "Fast-forward 3 model generations" or "This will change X over the next 12 months" — beats a soft "thoughts?"

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

Research supported advertising promises from a specific buyer's context, current product facts, customer language and observed ads. Turn them into static, video or copy ideas; match video ideas to actual recipes and save a product-scoped angle bank for write-video-ad-script. Product-led discovery can use verified facts without reviews. Reference persistence and reach are observations, not proof of performance.

中文简介正在准备中。当前显示原文说明。

gooseworks-ai/goose-skills12642026年10月23日 更新

Analyze ad campaign performance data (Google, Meta, LinkedIn) to identify what's working, what's wasting budget, and specific cut/scale/test recommendations. Runs statistical analysis, funnel diagnostics, and multi-channel budget reallocation with specific dollar-amount shift recommendations and scenario modeling.

中文简介正在准备中。当前显示原文说明。

gooseworks-ai/goose-skills12642026年10月23日 更新

For paid lead-gen and participant-recruitment ads, replaces vanity CPA with true CAC per qualified lead by joining ad-platform data with downstream funnel events, surfaces tracking gaps, and classifies every creative into Scale / Keep / Investigate / Cut.

中文简介正在准备中。当前显示原文说明。

gooseworks-ai/goose-skills12642026年10月23日 更新

The intake stage of a Meta ad harness. Researches the brand first, states what it assumes, and asks only what it cannot infer and what would change money, claims or legal exposure if guessed wrong — at most four questions a turn, in plain business language. Writes the brand's stable facts (ads/brand.md), the campaign brief, and a log of what was asked vs assumed. Use it when someone wants to run Meta ads and the brand's ad facts are not yet recorded.

中文简介正在准备中。当前显示原文说明。

gooseworks-ai/goose-skills12642026年10月23日 更新

Checks paid ads against the ad platform's rules (Meta, TikTok, Google, LinkedIn and others) and the brand's category rules: the copy, on-image or on-screen text, pictures and people. Rewrites what can be fixed honestly, adds the disclosures that are needed, and flags anything that needs a human or legal review, with one pass, fixed or flag line per ad and platform. Use alone when you already have the ad text or images and know where they will run; otherwise run the playbook for the job (e.g. make-static-ads, make-content, email-lifecycle).

中文简介正在准备中。当前显示原文说明。

gooseworks-ai/goose-skills12642026年10月23日 更新

Analyze the message match between your ads and landing pages. Checks if the promise in the ad copy carries through to the landing page headline, body, and CTA. Flags disconnects that kill conversion rates. Works with Google, Meta, and LinkedIn ads.

中文简介正在准备中。当前显示原文说明。

gooseworks-ai/goose-skills12642026年10月23日 更新

查看 gooseworks-ai 的全部技能

报告此技能的问题