liam-launch

liam-launch is a skill for Claude Code, Codex from stan-rym/liam-linkedin-ads-MCP. It costs 92 tokens per session (1,075 once invoked), scanned A, original, MIT.

A guarded workflow for creating draft LinkedIn advertising campaigns from a plain-language request. It gathers the campaign brief, estimates the audience, applies specified defaults, asks for confirmation, creates the draft, and verifies it.

In plain words
What is it for?
Use it to prepare LinkedIn campaign groups, campaigns, and ads with budgets, bids, landing pages, audiences, conversion settings, and ad copy.
Why use it?
It reduces missing details and setup mistakes before a campaign is created. The workflow creates drafts only, so campaigns are not activated by this add-on.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to prepare LinkedIn campaign groups, campaigns, and ads with budgets, bids, landing pages, audiences, conversion settings, and ad copy.

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Install with agentmods
npx agentmods add skills/stan-rym/liam-linkedin-ads-mcp/liam-launch
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.

Any agent
npx skills add stan-rym/liam-linkedin-ads-MCP --skill liam-launch
Clone the repo
git clone --depth 1 https://github.com/stan-rym/liam-linkedin-ads-MCP

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for liam-launch

README.md
[![agentmods](https://agentmods.dev/badge/skills/stan-rym/liam-linkedin-ads-mcp/liam-launch/github.svg)](https://agentmods.dev/skills/stan-rym/liam-linkedin-ads-mcp/liam-launch)
Your own site
<a href="https://agentmods.dev/skills/stan-rym/liam-linkedin-ads-mcp/liam-launch"><img src="https://agentmods.dev/badge/skills/stan-rym/liam-linkedin-ads-mcp/liam-launch/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for liam-launch

Your own site · 80×15
<a href="https://agentmods.dev/skills/stan-rym/liam-linkedin-ads-mcp/liam-launch"><img src="https://agentmods.dev/badge/skills/stan-rym/liam-linkedin-ads-mcp/liam-launch.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 92 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,075 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00092 $0.01075
Opus 5.5 $0.00037 $0.00430
Sonnet 5.5 $0.00018 $0.00215
Haiku 4.5 $0.00009 $0.00108

Measured 1mo ago against content hash c74d1755d661, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

liam-launch 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 1mo 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/liam-launch/SKILL.md · 84 lines

How it starts

The opening of the file, as written. The whole thing — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Liam: guarded campaign launch

Everything Liam creates is a DRAFT and there is deliberately no activate tool, so the cost of a mistake is rework, not money. This skill's job is making rework rare: gather a complete brief, estimate before creating, apply the house rules, confirm, create, verify.

How to reach Liam

Prefer the liam MCP tools if loaded: launch_from_brief, create_campaign_group, create_campaign, create_image_ad, search_targeting, list_targeting_facets, estimate_audience, list_conversions, upload_audience_csv, audience_from_salesforce. CLI fallback: liam launch --brief brief.json (see examples/brief.json in the Liam repo). Naming map: campaignGroupName is LinkedIn's "campaign", campaignName is the "ad group", creatives are the "ads".

Step 1: gather the brief

Required before anything is created: group and campaign names, daily budget and bid (with currency), landing URL, run start (epoch ms, must be in the future), the audience (a matched-list CSV, a Salesforce SOQL query, or targeting facets), and the ad copy or enough intent to draft it. Name entities so the angle is legible (persona, offer, competitor, audience in the name); the analysis skills mine angles from names later.

Step 2: resolve and estimate before creating

  • Resolve targeting facets with search_targeting; verify facet names with list_targeting_facets (they are exact; job functions is jobFunctions, not functions).
  • estimate_audience on the resolved spec and quote the reach in the confirmation.
  • Matched audiences need ~300 members to serve at all; warn below that, and note matching takes up to 48h, so the run start should allow for it.
  • CSV audiences: always --dry-run first and show the cleaned columns and row count before uploading.

Step 3: apply the house rules

Ask once for the account's standing defaults if you do not know them, then apply on every campaign:

  • Audience Expansion off and LinkedIn Audience Network off. Where Liam does not expose a toggle at creation, list it as a Campaign Manager check in the handoff rather than assuming.
  • Standing exclusion audiences (customers, competitors, employees are the usual set) on every campaign.
  • Conversion tracking wired: use the user's named conversion or the config default (conversionName/conversionIds; list_conversions to look up). Never create a campaign that tracks nothing.
  • Format is create-only. A video campaign must be created with format SINGLE_VIDEO; format cannot be patched afterwards. Getting this wrong means recreating the campaign.
  • Sponsoring somebody else's post takes the ENGAGEMENT objective.
  • Ad rotation is the campaign field creativeSelection: OPTIMIZED (LinkedIn's default, favors the predicted winner) or ROUND_ROBIN (Campaign Manager's "Rotate ads evenly"). Small audiences with several ads per ad set usually want ROUND_ROBIN so every ad gets a fair read. Set it on create_campaign / update_campaign, in a brief, or with liam campaigns update <id> --rotation even --apply.

Read the full file on GitHub · 84 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. 1mo ago Changed · +5 lines c74d1755d661
  2. 1mo ago First seen · 79 lines · 92 tokens per session scan A 9b7230c30334

Subscribe to this mod's changes

liam-launch is a skill published in the GitHub repository stan-rym/liam-linkedin-ads-MCP (23 stars, last pushed 1mo ago), licensed MIT. It adds 92 tokens to every session and 1,075 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-08-30.

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