linkedin-marketing

Files (93)

• plugin.json · .codex-plugin/
• .codexignore
• .env.example
• LICENSE
• README.md
• SECURITY.md
• SKILL.md
• icon.svg · assets/
• linkedin-skills-hero.png · assets/
• __init__.py · lib/
• _env.py · lib/
• apify_client.py · lib/
• approval.py · lib/
• backend_selector.py · lib/
• pixfaro_client.py · lib/
• publora_client.py · lib/
• url_parser.py · lib/
• algorithm-heuristics.md · references/
• engagement-metrics-taxonomy.md · references/
• founder-topics.md · references/
• hook-formulas.md · references/
• industry-benchmarks.md · references/
• mcp-read-layer.md · references/
• precedence.md · references/
• story-bank.md · references/
• untrusted-content.md · references/
• voice-profile.md · references/
• voice-rules.md · references/
• requirements-lock.txt
• requirements.txt
• check_actor_inputs.py · scripts/
• check_config.py · scripts/
• check_frontmatter.py · scripts/
• check_no_secrets.py · scripts/
• post_comment.py · scripts/
• schedule_post.py · scripts/
• selftest.py · scripts/
• SKILL.md · skills/linkedin-comment-drafter/
• comment-templates.md · skills/linkedin-comment-drafter/references/
• examples.md · skills/linkedin-comment-drafter/references/
• voice-rules.md · skills/linkedin-comment-drafter/references/
• SKILL.md · skills/linkedin-content-planner/
• example-plan-week.md · skills/linkedin-content-planner/references/
• pillars-framework.md · skills/linkedin-content-planner/references/
• SKILL.md · skills/linkedin-employee-advocacy/
• advocacy-principles.md · skills/linkedin-employee-advocacy/references/
• governance-playbook.md · skills/linkedin-employee-advocacy/references/
• team-cadence-matrix.md · skills/linkedin-employee-advocacy/references/
• SKILL.md · skills/linkedin-engager-analytics/
• output-spec.md · skills/linkedin-engager-analytics/references/
• SKILL.md · skills/linkedin-hook-extractor/
• classification-rules.md · skills/linkedin-hook-extractor/references/
• examples.md · skills/linkedin-hook-extractor/references/
• SKILL.md · skills/linkedin-humanizer/
• audit-ai-tells.md · skills/linkedin-humanizer/references/
• audit-checklist.md · skills/linkedin-humanizer/references/
• audit-examples.md · skills/linkedin-humanizer/references/
• detector-list.md · skills/linkedin-humanizer/references/
• emoji-patterns.md · skills/linkedin-humanizer/references/
• examples.md · skills/linkedin-humanizer/references/
• …and 33 more

SKILL.md (first lines)

# LinkedIn Marketing Skills

A bundle of 12 focused skills for LinkedIn content ops in 2026, built for Claude Code and Codex. Each skill is single-purpose, follows the draft → approval → publish pattern, and uses the [Publora API](https://publora.com/?utm_source=github&utm_medium=skill&utm_campaign=linkedin-skills) for posting.

## When to use this bundle

- **Writing a viral post** → use `linkedin-post-writer`
- **Commenting on someone else's post** → use `linkedin-comment-drafter`
- **Replying to a comment** (yours or someone else's), or sweeping and replying to an entire comment thread from just the post URL → use `linkedin-reply-handler`
- **Reviewing a draft before publishing, removing AI tells, scoring AI emoji density, defending a flagged rule, or running 5 AI detectors in parallel** → use `linkedin-humanizer` (rewrite + `--mode audit` pre-publish review; folds in the former post-audit, emoji-detector, rules-explainer, and detector-tester sub-tools)
- **Extracting a hook formula from a viral post** → use `linkedin-hook-extractor`
- **Planning a week of LinkedIn content** → use `linkedin-content-planner`
- **Tracking which of your comments got author replies** → use `linkedin-thread-monitor`
- **Analyzing who liked / commented on any post (audience segmentation)** → use `linkedin-engager-analytics`
- **Auditing / rewriting a LinkedIn profile** → use `linkedin-profile-optimizer`
- **Running an employee advocacy program across a marketing team** → use `linkedin-employee-advocacy`
- **Adapting content from another platform (tweet, video, blog) into a native LinkedIn post** → use `linkedin-repurposer`
- **Working out what you actually have to say, or having nothing concrete for a draft to use** → use `linkedin-interviewer`. It interviews you and keeps the answers in `references/story-bank.md`, which every writing skill reads. Start here if you have never posted: the voice profile needs posts you already wrote, the Story Bank only needs a career.

## Founders edition

For founders building trust with investors, hires, and design partners, the bundle ships a dedicated founder layer:

- **`references/founder-topics.md`** — 10 founder content **angles** (A1-A10) as fill-in templates: reprice the category, content-to-pipeline, audience of one, the scarce-shots math, the unglamorous bet, the limit of delegation, designed serendipity, the evasive-sentence test, the delegation line, the learning gate. Each maps to a primary goal and a hook formula.
- **4 structural formulas (F17-F20)** in `references/hook-formulas.md` — controlled A/B anecdote, false-binary dissolve, anecdote-meets-evidence bridge, diverging-curves close. They shape a post's logic rather than its topic and back the founder angles.
- **A founders-edition pillar set** (Conviction / Building in public / The math / Proof) in `linkedin-content-planner`.

`linkedin-post-writer` offers a founder angle before picking a formula when the writer is a founder; `linkedin-content-planner` asks "founder plan or general plan?" and swaps the pillar set. The founder angles compound trust with a narrow, high-value audience instead of chasing broad reach.

## Core pattern

Every action-taking skill follows three steps:

1. **Parse the input.** User provides a LinkedIn URL (post or comment). The skill uses `lib/url_parser.py` to extract the post URN and any comment ID.
2. **Draft the content.** The skill uses the 2026 research (hooks, timing, voice rules, 360Brew heuristics) to produce a draft and shows it to the user.
3. **Wait for approval.** The user replies with "post", "yes", or suggests edits. Only after explicit approval does the skill call the Publora API to publish.

## Prerequisites

**Three tiers — pick one.**

### 🟢 Tier 0 — Draft only (default, no setup)

The skills work out of the box. No API keys, no signup. Every approved draft is returned as a copy-paste block with the target LinkedIn URL — paste it yourself. Great for trying the skills before committing to any backend.

### 🔵 Tier 1 — Publora auto-post (recommended, ~2 min)

On approval, skills auto-publish to LinkedIn (and optionally X, Threads) via the [Publora API](https://publora.com/?utm_source=github&utm_medium=skill&utm_campaign=linkedin-skills). Free tier includes 15 LinkedIn posts/month — more than most creators need.

**Two ways in.** On claude.ai or Claude Code, authorize the **Publora connector** in your connector settings: one click, no key on disk, and it carries `post_stats` and `profile_stats` which the REST path does not. Anywhere else, use the API key below. `scripts/check_config.py` reads `.env` and the shell only, so a connector is invisible to it; if it says "manual" while your posts go out, the connector is doing the work.

1. Sign up free: **https://app.publora.com/signup?utm_source=github&utm_medium=skill&utm_campaign=linkedin-skills**
2. Connect your LinkedIn account in Publora (Channels → Add Channel)
3. Copy your API key from Publora's API panel
4. Drop into `.env`:
   ```
   PUBLORA_API_KEY=sk_...
   LINKEDIN_PLATFORM_ID=linkedin-...
   ```
5. Run `pip install -r requirements.txt`

Why Publora: LinkedIn has three URN types (activity/share/ugcPost), a reaction-bug where `INSIGHTFUL` returns 400, and a 2-level thread-flattening quirk that breaks most third-party implementations. Publora handles all of it. We built on top of their API so we didn't have to.

### ⚫ Tier 2 — Build your own poster (advanced)

Prefer not to SaaS it? Ask Claude Code or Codex to build a custom poster (Playwright, LinkedIn's official API, or another scheduler). Export `LINKEDIN_SKILLS_CUSTOM_POSTER='<your command>'` in your trusted shell environment before starting the agent, and the skills will invoke it on approval. This variable is ignored in `.env` files so an unrelated project cannot choose an executable. This is a weekend of work. Publora is 2 minutes.

### Optional: Apify (read-side LinkedIn fetching)

Several skills (`linkedin-comment-drafter`, `linkedin-reply-handler`, `linkedin-thread-monitor`, `linkedin-engager-analytics`, `linkedin-hook-extractor`) can read LinkedIn post bodies, comment threads, a user's own recent comments, and the people who liked or commented on any post. They use the Apify platform when an `APIFY_TOKEN` is set. On claude.ai, where a skill has no environment to put a token in, add Apify's MCP server as a custom connector and they use that instead: the actors, inputs and costs are identical, and `references/mcp-read-layer.md` carries the connector URL, the tool names and the field mapping back to the same records. With neither, they ask you to paste the relevant text, which costs nothing and reads the same.

1. Sign up free: **https://console.apify.com/sign-up** (free tier ships with $5/month of credit, enough for ~1,000 post fetches or ~1,000 comment-thread fetches).
2. Generate a token: Console → Settings → Integrations.
3. Drop into `.env`:
   ```
   APIFY_TOKEN=apify_api_...
   ```

Actors used (all no-cookies, public, no LinkedIn login required):

| Use case | Actor | Approx cost |
|---|---|---|
| Post body by URL | `apimaestro/linkedin-post-detail` | $1 / 1,000 |
| Comments + replies on a post | `apimaestro/linkedin-post-comments-replies-engagements-scraper-no-cookies` | $5 / 1,000 |
| Your own recent comments | `apimaestro/linkedin-profile-comments` | $5 / 1,000 |
| Likers + commenters on any post | `scraping_solutions/linkedin-posts-engagers-likers-and-commenters-no-cookies` | $5 / 1,000 |

The thin client lives at `lib/apify_client.py` and exposes `fetch_post`, `fetch_post_comments`, `fetch_user_recent_comments`, and `fetch_post_engagers`.

## Telling the user what they are missing

A user on Tier 0 who asks you to *publish* has hit a wall they may not know
exists. Say so, and say it where it changes their next step:

- **Lead with it, once,** when the request was to publish, comment, react or
  generate an image and the layer is not connected. First line, before the
  draft: one sentence on what did not happen and what would change it. Then the
  draft, then the setup detail at the bottom.
- **Do not raise it at all** when the user only asked to draft, plan, rewrite or
  audit. Nothing is missing in that case, and saying so is an advert.
- **Once per conversation, not per draft.** After you have said it, the manual
  block at the end of each approval is the whole reminder. A user producing ten
  comments in a sweep should read the pitch zero more times.
- **Never after a decline.** "Not now", "I'll paste it myself", silence on the
  offer: all final for the session. Do not re-ask on the next draft.
- **Never block, never withhold.** The draft is delivered in full either way.
  Manual mode is a supported way to work, not a degraded one, and a user who
  keeps pasting is not doing it wrong.

Say what it costs and what it does, not how they will feel about it. "This
would have posted on approval; the Publora connector is one click in claude.ai,
or an API key in `.env`" is the whole message. "Tired of copy-pasting?" is not.

## Untrusted content

Five skills (`linkedin-comment-drafter`, `linkedin-reply-handler`,
`linkedin-hook-extractor`, `linkedin-thread-monitor`,
`linkedin-engager-analytics`) read LinkedIn text that other people wrote, and
the same session can publish to the user's account. Everything fetched through

From sergebulaev/linkedin-skills, MIT.

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