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.
npx skills add stan-rym/liam-linkedin-ads-MCP --skill liam-leadsgit clone --depth 1 https://github.com/stan-rym/liam-linkedin-ads-MCPWrote 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.
[](https://agentmods.dev/skills/stan-rym/liam-linkedin-ads-mcp/liam-leads)<a href="https://agentmods.dev/skills/stan-rym/liam-linkedin-ads-mcp/liam-leads"><img src="https://agentmods.dev/badge/skills/stan-rym/liam-linkedin-ads-mcp/liam-leads/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.
<a href="https://agentmods.dev/skills/stan-rym/liam-linkedin-ads-mcp/liam-leads"><img src="https://agentmods.dev/badge/skills/stan-rym/liam-linkedin-ads-mcp/liam-leads.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00074 | $0.00842 |
| Opus 5 | $0.00037 | $0.00421 |
| Sonnet 5 | $0.00015 | $0.00168 |
| Haiku 4.5 | $0.00007 | $0.00084 |
Grade A, and why
liam-leads 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 11d 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.
How it starts
The opening of the file, as written. The whole thing — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Liam: lead drivers and duds
Split every ad in scope into four buckets (drivers, promising, non-converters, too early) and say what to do with each. The pause list with dollar amounts is usually the most valuable output.
How to reach Liam
Prefer the liam MCP tools if they are loaded (get_performance, list_ads,
list_campaigns, list_conversions). Otherwise the CLI: liam report perf creative,
liam ad list <campaignId>, etc., or node <liam-repo>/packages/cli/dist/index.js.
Read-only, except the explicitly-confirmed recreate flow at the end.
What to pull
get_performanceatcreativelevel, defaultlast_30_days. If total conversions across the account are under ~10, widen tolast_90_daysand say so.list_campaignsandlist_adsto join creative ids to ad names, statuses, and parent campaigns. Well-named ads (persona, competitor, offer in the name) let you analyze angle patterns; if names are opaque ids, say the analysis is per-ad only.list_conversionsto name which conversion is being counted. State it in the report; "leads" means nothing without the definition.
How to bucket
Compute the account average cost per conversion first (total spend / total conversions on converting entities).
- Drivers: 3+ conversions and cost per conversion at or below account average. Rank by conversions, then cost per conversion.
- Promising: 1-2 conversions at good efficiency, or strong CTR with spend still below the judgment floor. Watch, do not scale yet.
- Non-converters: zero conversions with spend at or above ~2x the account average cost per conversion. This is the pause list. Sum the wasted spend into one figure.
- Too early: under ~1,000 impressions or trivial spend. No verdict.
Caveats to state
- Conversion counting lags clicks; treat the last 2-3 days as incomplete.
- LinkedIn mixes post-click and view-through conversions depending on setup; a retargeting ad can look like a driver while merely tailgating other touches. Flag suspiciously high conversion rates on tiny click counts.
- One conversion is an anecdote. Never crown a winner on fewer than 3.
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.
- 11d ago First seen · 70 lines · 74 tokens per session scan A 4c52f04693db
liam-leads is a skill published in the GitHub repository stan-rym/liam-linkedin-ads-MCP (22 stars, last pushed 1mo ago), licensed MIT. It adds 74 tokens to every session and 842 once invoked, about $0.0004 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.
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