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 naveedharri/benai-skills --skill ads-linkedingit clone --depth 1 https://github.com/naveedharri/benai-skillsWrote 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/naveedharri/benai-skills/ads-linkedin)<a href="https://agentmods.dev/skills/naveedharri/benai-skills/ads-linkedin"><img src="https://agentmods.dev/badge/skills/naveedharri/benai-skills/ads-linkedin/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/naveedharri/benai-skills/ads-linkedin"><img src="https://agentmods.dev/badge/skills/naveedharri/benai-skills/ads-linkedin.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00085 | $0.01267 |
| Opus 5 | $0.00043 | $0.00633 |
| Sonnet 5 | $0.00017 | $0.00253 |
| Haiku 4.5 | $0.00009 | $0.00127 |
Grade A, and why
ads-linkedin 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 12d 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- ads-linkedin — 100% identical, 0 lines differ
- ads-linkedin — 86% identical, 19 lines differ
How it starts
The opening of the file, as written. The whole thing — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn Ads Deep Analysis
Process
- Collect LinkedIn Ads data (Campaign Manager export, Insight Tag status)
- Read
ads/references/linkedin-audit.mdfor full 25-check audit - Read
ads/references/benchmarks.mdfor LinkedIn-specific benchmarks - Read
ads/references/scoring-system.mdfor weighted scoring - Evaluate all applicable checks as PASS, WARNING, or FAIL
- Calculate LinkedIn Ads Health Score (0-100)
- Generate findings report with action plan
What to Analyze
Technical Setup (25% weight)
- Insight Tag installed and firing on all pages (L01)
- Conversions API (CAPI) active — launched 2025 (L02)
- Conversion events configured for full funnel
- Revenue attribution tracking enabled
Audience Targeting (25% weight)
- Job title targeting uses specific titles, not just functions (L03)
- Company size filtering matches ICP (L04)
- Seniority level appropriate for offer (L05)
- Matched Audiences active: retargeting + contact lists (L06)
- ABM company lists uploaded (up to 300,000 companies) (L07)
- Audience expansion OFF for precision campaigns, ON for scale (L08)
- Predictive audiences tested — replaced Lookalikes Feb 2024 (L09)
Creative Quality (20% weight)
- Thought Leader Ads active, ≥30% budget allocation for B2B (L10)
- Ad format diversity: ≥2 formats tested (L11)
- Video ads tested (L12)
- Creative refresh every 4-6 weeks (L13)
Lead Gen & Performance (15% weight)
- Lead Gen Form ≤5 fields (13% CVR benchmark) (L14)
- Lead Gen Form synced to CRM in real-time (L15)
- Campaign objective matches funnel stage (L18)
- A/B testing active: creative or audience (L19)
- Message ad frequency ≤1 per 30-45 days (L20)
Bidding & Budget (15% weight)
- Bid strategy: CPS for Messages, Max Delivery for Content (L16)
- Daily budget ≥$50 for Sponsored Content (L17)
- CTR ≥0.44% for Sponsored Content (L21)
- CPC within benchmark: $5-7 average, senior $6.40+ (L22)
- Lead-to-opportunity rate tracked, not just CPL (L23)
- Attribution: 30-day click / 7-day view configured (L24)
- Demographics report reviewed monthly (L25)
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.
- 12d ago First seen · 124 lines · 85 tokens per session scan A 1b74e7f37724
ads-linkedin is a skill published in the GitHub repository naveedharri/benai-skills (61 stars, last pushed 8d ago), licensed MIT. It adds 85 tokens to every session and 1,267 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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