linkedin-content

$npx mdskill add alirezarezvani/claude-skills/linkedin-content

The post is not the deliverable; the specific thing only this person can say is. The scripts handle format choice, mechanical faults, and de-duplication. The interesting part — what actually happened and what it cost — comes from the user, and cannot be generated.

SKILL.md

.github/skills/linkedin-contentView on GitHub ↗
---
name: linkedin-content
description: Use when someone wants to write, edit, or lint a LinkedIn post — a story, how-to, opinion piece, carousel script, video script, or poll — or wants an article, talk, or transcript repurposed into posts. Triggers on "write a LinkedIn post", "is this hook any good", "review my post", "turn this into LinkedIn posts", "carousel", "what format should this be". Lints posts 0-100 on mechanics, hook, integrity, and accessibility; picks the format the material actually supports; and splits long sources into standalone units with a reuse ledger.
license: MIT
metadata:
  version: 1.0.0
  author: Alireza Rezvani
  category: marketing
  updated: 2026-08-25
---

# LinkedIn Content — format, draft, lint

The post is not the deliverable; the specific thing only this person can say is. The scripts
handle format choice, mechanical faults, and de-duplication. The interesting part — what
actually happened and what it cost — comes from the user, and cannot be generated.

## Workflow

**1. Check the brief exists.** If there is no positioning brief, offer `linkedin-strategy`
first as a question. Posts without pillars are noise. Never chain silently.

**2. Pick the format from the material, not from fashion.**

```bash
python3 scripts/format_picker.py --goal authority --material data --material tutorial \
  --minutes 120 --output human
```

Exit 0 recommends / 2 asks when the top two are within a point (tie-break on which one they
would enjoy making — the one they repeat beats the one that scores higher once) / 3 no fit,
go get material. It refuses video with no camera and no footage, and a poll with no real
decision behind it.

**3. Draft.** Interview for specifics before writing a line: the number, the mistake, the
sentence someone said. Then write to the fold — **the first ~140 characters are the whole
post for most readers**, and a sentence must complete inside them.

**4. Lint before it ships.**

```bash
python3 scripts/post_linter.py --input draft.md --has-image --output human
```

Exit 0 SHIP / 2 REVISE (or any blocking finding) / 3 REWRITE. Blocking findings have named
consequences: over the 3,000-character cap; engagement bait, named as demoted content in the
Professional Community Policies; Unicode pseudo-bold, which screen readers announce as
mathematical symbols and search does not index as words. Two passes is normal; the stop
condition is exit 0 or a stated decision to accept a warning.

**5. Repurposing.** Split a source and keep the ledger — it prevents the specific recurring
failure of the same idea going out three times over eight months. Commit it alongside the
source; it is project state, not a cache. Every unit is source material, not a post: add the
sentence only the author can write — what it cost, or what they would do differently.

```bash
python3 scripts/repurpose_splitter.py --input talk.md --ledger .linkedin-ledger.json --output human
python3 scripts/repurpose_splitter.py --input talk.md --ledger .linkedin-ledger.json --record 2 --posted-on 2026-08-25
```

## Rules

- **Never fabricate a number, a client, a result, or a quote.** Not one, not as a placeholder.
- **Write to the mobile fold.** A sentence completes before character 140.
- **Links in the first comment**, and say so in the post.
- **No Unicode pseudo-bold, ever.** It is an accessibility failure, not a style choice.
- **Alt text on every image; captions on every video.** LinkedIn does not add them for you.
- **No engagement bait.** Ask the question the post actually earned.
- **The author reads every line before it ships.** They are the author of record.

## Scripts

| Script | Role |
|---|---|
| [`scripts/post_linter.py`](scripts/post_linter.py) | 0-100 across mechanics / hook / integrity / accessibility; blocking findings for the cap, bait, and pseudo-bold. |
| [`scripts/format_picker.py`](scripts/format_picker.py) | Ranks nine native formats against goal, material, and minutes; refuses camera-less video and decision-less polls. |
| [`scripts/repurpose_splitter.py`](scripts/repurpose_splitter.py) | Splits a source into standalone units, scores them, and skips anything already in the reuse ledger. |

## References and assets

- [`references/hook_and_fold_mechanics.md`](references/hook_and_fold_mechanics.md) — the first 140 characters, and the openers to delete on sight (7 sources)
- [`references/post_formats_canon.md`](references/post_formats_canon.md) — what each native format is good at, and the carousel trap (7 sources)
- [`references/repurposing_discipline.md`](references/repurposing_discipline.md) — standalone units, the reuse ledger, source types and their risks (7 sources)
- [`references/accessibility_and_inclusion.md`](references/accessibility_and_inclusion.md) — pseudo-bold, alt text, captions, and why they are blocking (7 sources)

- [`assets/post_templates.md`](assets/post_templates.md) — five worked post shapes with the hook already doing its job
- [`assets/example_post.md`](assets/example_post.md) — a post that passes the linter, annotated

## Distinct from

- **`linkedin-strategy`** — decides what the posts are about. This writes them.
- **`linkedin-engagement`** — comments and DMs. A comment is a different craft with a
  different budget; it lives there.
- **`marketing-skill/copywriting` / `content-humanizer`** — general copy and de-AI passes.
  Reach for those for tone; this one owns LinkedIn's mechanics and its accessibility floor.

---

**Version:** 1.0.0

More from alirezarezvani/claude-skills

SkillDescription
a11y-auditAccessibility audit skill for scanning, fixing, and verifying WCAG 2.2 Level A and AA compliance across React, Next.js, Vue, Angular, Svelte, and plain HTML codebases. Use when auditing accessibility, fixing a11y violations, checking color contrast, generating compliance reports, or integrating accessibility checks into CI/CD pipelines.
ab-test-setupWhen the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "conversion experiment," "statistical significance," or "test this." For tracking implementation, see analytics-tracking.
ad-creativeWhen the user needs to generate, iterate, or scale ad creative for paid advertising. Use when they say 'write ad copy,' 'generate headlines,' 'create ad variations,' 'bulk creative,' 'iterate on ads,' 'ad copy validation,' 'RSA headlines,' 'Meta ad copy,' 'LinkedIn ad,' or 'creative testing.' This is pure creative production — distinct from paid-ads (campaign strategy). Use ad-creative when you need the copy, not the campaign plan.
adversarial-reviewerAdversarial code review that breaks the self-review monoculture. Use when you want a genuinely critical review of recent changes, before merging a PR, or when you suspect Claude is being too agreeable about code quality. Forces perspective shifts through hostile reviewer personas that catch blind spots the author's mental model shares with the reviewer.
aeoAnswer Engine Optimization (AEO) skill — optimize content to be cited by AI language models (ChatGPT, Perplexity, Claude, Gemini, Mistral) as authoritative sources. Distinct from SEO — AEO optimizes for citation in LLM-generated responses, not search rankings. Use when planning content for AI-first search audiences, auditing existing content for E-E-A-T signals, tracking which pages get cited by which LLMs, or building a citation-friendly content strategy. Triggers — 'AEO audit', 'optimize for ChatGPT', 'get cited by Perplexity', 'LLM citation strategy', 'answer engine optimization', 'content for AI search', 'E-E-A-T audit'. Output is a markdown audit report (default) or JSON for pipeline integration. Stdlib-only Python tools.
agent-decision-receiptsMint a tamper-evident, post-quantum-signed receipt for a consequential agent action (deploy, delete, pay, grant-access, model decision) so it can be verified later from the certificate alone. Use when an autonomous agent takes a side-effecting action that may need to be proven later, or when satisfying EU AI Act Article 12 record-keeping. Three decisions: whether an action needs a receipt, minting it, verifying it. Signing is delegated to the open-source OpenAgentOntology package. Not after-the-fact log analysis; not a hosted notary; not a legal opinion.
agent-designerUse when the user asks to design a multi-agent system, pick an orchestration pattern (supervisor/swarm/pipeline), generate tool schemas for agents, or evaluate agent execution logs for cost, latency, and failure bottlenecks. Examples: 'design an agent architecture for research automation', 'generate Anthropic tool schemas from these tool descriptions', 'analyze these agent run logs for bottlenecks'. NOT for Claude Code workflow files (use workflow-builder) or single-agent prompt design (use agent-workflow-designer).
agent-harnessTurn any domain folder of skills into a bounded agentic loop: compile a goal into a verifiable task plan, execute tasks with the domain's own tools, verify every task with machine-run checks, retry with caps, escalate to a human when budgets exhaust, and refuse to close until everything is verified or explicitly waived. Use when you want an agent or subagent to pick up a goal and drive it to a verified close across one of this repo's 18 domains ('run this goal through the engineering harness', 'set up an agentic loop for marketing work', 'make the finance domain self-verifying'). NOT for authoring Claude Code Workflow-tool .js scripts (workflow-builder), N-agent tournaments on one task (agenthub), single-file metric optimization (autoresearch-agent), or discovering published loop recipes (loop-library).
agent-launcher-orchestratorUse when a user wants to build, launch, grade, or schedule a Claude Managed Agent (CMA) in their own Anthropic account — "build me an agent", "launch this as a managed agent", "run this on a schedule", "grade my agent against a rubric", "set up a nightly worker". Reads the per-session goal (./my-agent/goal.json), routes deterministically to one of five phase sub-skills (interview → stage-launch → grade-iterate → run-without-you → wrap-up) via goal_router.py, and compiles the goal+phase into an execution shape (single-pass workflow / bounded grade→iterate loop / recurring cron deployment loop) via loop_compiler.py. Forks context so heavy intake (build sheets, payloads, eval cases) stays out of the parent thread. All launches are emitted as BYOK curl the user runs with their own key; no tool makes API calls. Inspired by anthropics/launch-your-agent (Apache-2.0). Distinct from engineering/agent-harness (generic domain loop) and engineering/write-a-skill (authors Claude Code skills, not CMAs).
agent-memoryUse when a project's CLAUDE.md has grown past what anyone reads and you want the agent to learn durable facts from its own sessions instead — or when asking why the agent keeps re-learning the same correction, why a remembered rule is wrong, or where a memory line came from. Implements a four-tier store (L0 transcripts / L1 candidates / L2 project context / L3 stable persona) where promotion is earned by recurrence across sessions and days, never by one confident statement, and nothing reaches a committed file without a human adopting it.