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 swan-gtm/gtm-skills --skill linkedin-abm-monthly-reportgit clone --depth 1 https://github.com/swan-gtm/gtm-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/swan-gtm/gtm-skills/linkedin-abm-monthly-report)<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/linkedin-abm-monthly-report"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/linkedin-abm-monthly-report/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/swan-gtm/gtm-skills/linkedin-abm-monthly-report"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/linkedin-abm-monthly-report.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 | $0.00167 | $0.02445 |
| Opus 5 | $0.00084 | $0.01222 |
| Sonnet 5 | $0.00033 | $0.00489 |
| Haiku 4.5 | $0.00017 | $0.00245 |
Grade A, and why
linkedin-abm-monthly-report 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 9d 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 — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LinkedIn ABM ad reporting (ZenABM)
This skill produces a monthly report — a clean, shareable "[Company] LinkedIn ABM Ad Report — [Month Year]" covering the last full calendar month, compared to the month before, delivered as a self-contained branded HTML document and a downloadable PDF.
This is a report, not an audit. Its job is to summarize what happened last month for a stakeholder / exec audience: the numbers, what moved vs the prior month, what worked, and what to do next. It is the reporting sibling of the LinkedIn ads & ABM audit skill (which is a diagnostic, trailing-30-day, fix-list audit) — reuse the same benchmarks and math, but frame it as a recap, not a to-do list.
Requires a ZenABM account and the ZenABM connector (app.zenabm.com) for live LinkedIn ads data — that's how the skill pulls last month's spend, pipeline and deals influenced, best campaigns/formats/ads, month-over-month changes, and engaged companies. Without it, the report can't be produced, and figures are never invented. The Revenue/pipeline/deals section additionally needs HubSpot (or another CRM) connected in ZenABM; without it that section is skipped.
The golden rule
The user only chats. You do all the technical work — pull the numbers, do the math, compare to last month and to benchmarks, write the report — with short progress notes so it always feels like a conversation. The only thing you ask them to do is connect ZenABM (and ideally their CRM).
Probe before promising numbers: call get_linkedin_metrics for the report month. If it errors or is empty,
the connector isn't ready — help them finish connecting and wait. Never fabricate data.
Step 1 — Set the window (LAST CALENDAR MONTH) and pull the data
The window is the last FULL calendar month — not the trailing 30 days. Compute from today's date: if today is
2026-07-11, the report month is June 2026 (2026-06-01 to 2026-06-30) and the comparison month is
May 2026 (2026-05-01 to 2026-05-31). Use explicit startDate/endDate for both months (you can also use
period: "lastMonth" for the report month, but pass explicit dates for the comparison month). Tell the user the
month you're reporting on and let them override (e.g. they may want a specific past month).
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.
- 9d ago First seen · 140 lines · 167 tokens per session scan A 298736850e9a
linkedin-abm-monthly-report is a skill published in the GitHub repository swan-gtm/gtm-skills (150 stars, last pushed 2d ago), licensed MIT. It adds 167 tokens to every session and 2,445 once invoked, about $0.0008 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 2026-09-03.
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