liam-launch

liam-launch is a skill for Claude Code, Codex from stan-rym/liam-linkedin-ads-MCP. It costs 92 tokens per session (968 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.

Compare 6 skills from other repositories ↓
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 968 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 $0.00968
Opus 5 $0.00046 $0.00484
Sonnet 5 $0.00018 $0.00194
Haiku 4.5 $0.00009 $0.00097

Measured 10d ago against content hash 9b7230c30334, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, 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 10d 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 · 79 lines

How it starts

The opening of the file, as written. The whole thing — 79 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.

Read the full file on GitHub · 79 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. 10d 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 (22 stars, last pushed 1mo ago), licensed MIT. It adds 92 tokens to every session and 968 once invoked, about $0.0005 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 2026-08-30.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

insight-error-page

Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…

vercel/next.js · 83 tokens

next-cache-components-optimizer

Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…

vercel/next.js · 170 tokens

next-partial-prefetching-adoption

Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…

vercel/next.js · 103 tokens