linkedin-onboard

A setup workflow that creates a file describing your LinkedIn writing style, target buyers, business offer, evidence, and customer problems. LinkedIn is a professional networking platform where this information guides later writing tasks.

In plain words
What is it for?
Collecting business details, defining an ideal customer profile, analyzing past posts and direct messages, and saving the result in linkedin-brief.md.
Why use it?
It gives related writing tools a shared reference for producing posts and messages that match your voice and market.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/styfinity/linkedin-engine/linkedin-onboard
Any agent
npx skills add styfinity/linkedin-engine --skill linkedin-onboard
Clone the repo
git clone --depth 1 https://github.com/styfinity/linkedin-engine

Made for: Claude Code, Codex.

Per session 35 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 533 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00035 $0.00533
Opus 5 $0.00017 $0.00267
Sonnet 5 $0.00007 $0.00107
Haiku 4.5 $0.00003 $0.00053

Measured 2d ago against content hash 9fccda8d7c6e, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

linkedin-onboard scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/linkedin-onboard/SKILL.md · 30 lines

How it starts

The opening of the file, as written. The whole thing — 30 lines — stays where its author put it; the contents beside it link to each section on GitHub.

LinkedIn Onboard

The setup pass. This skill assembles the one file every other skill reads: linkedin-brief.md. Run it once, in the user's own words and evidence, and the whole engine knows the voice, the market, and the offer.

Inputs

  • The user's business, who they sell to, their offer, and a paste of their past posts and DMs (the more real text, the better): $ARGUMENTS
  • Nothing loads automatically yet. This skill is what creates the brief that loads from now on.

Do this

  1. Run /linkedin-icp-definer on the business and buyer details. Capture the ICP it returns: who, role, company shape, the trigger that makes them a fit.
  2. Run /linkedin-voice-profiler on the pasted posts and DMs. Capture the voice profile: cadence, sentence length, words they reach for, words they never use.
  3. Capture the offer in the user's language: one sentence (what it is), the outcome it produces, the proof behind it, and the risk reversal (the guarantee or the safety net).
  4. Capture the top three to five buyer pains in the buyer's words, pulled from the paste where possible, not paraphrased into jargon.
  5. Assemble all of it into one file and write it to linkedin-brief.md in the project root, with clear sections: ICP, Voice profile, Offer, Pains.

Output

The assembled linkedin-brief.md (ICP, voice profile, offer, pains), shown in full for the user to confirm. End with a one-line note: the SessionStart hook will load this file into every session from now on, so no other skill will ask for this context again.

Rules

  • Build only from the user's real evidence and real words. If a section is thin because the paste was thin, say so and ask for more text rather than inventing it.
  • The offer positions the user as a Revenue Partner who uses AI to do the heavy lifting. Never "AI consultant" or "AI agency".
  • This is the file every other skill reads. Get it right before running anything else.
  • No em-dashes. Draft first: show the assembled brief, the user confirms, then it is saved.

Read the full file on GitHub · 30 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 30 lines · 35 tokens per session scan A 9fccda8d7c6e

Subscribe to this mod's changes

linkedin-onboard is a skill published in the GitHub repository styfinity/linkedin-engine (7 stars, last pushed 2mo ago), licensed MIT. It adds 35 tokens to every session and 533 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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