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 meta-reportinggit 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/meta-reporting)<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/meta-reporting"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/meta-reporting/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/meta-reporting"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/meta-reporting.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 | $0.00077 | $0.00902 |
| Opus 5.5 | $0.00031 | $0.00361 |
| Sonnet 5.5 | $0.00015 | $0.00180 |
| Haiku 4.5 | $0.00008 | $0.00090 |
Grade A, and why
meta-reporting 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.
How it starts
The opening of the file, as written. The whole thing — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Meta Ads Reporting and Dashboards
Turn raw Meta numbers into a decision. This skill pulls live performance, analyzes it against the operating system in the meta-ads skill, and renders a clean, client-ready dashboard.
What this covers
- Performance analysis - pull live spend, leads, CPL, CTR, CPM, reach at account and campaign level, then read it like an operator (leading vs vanity signals).
- Reporting - weekly or period-over-period rollups: what changed, what is working, what to fix first.
- Dashboards - a self-contained HTML dashboard branded with the Frontal logo, that you open in a browser or send to a client.
Scripts
Run from .claude/skills/meta-ads/scripts/ (shared client + .env) unless noted.
| Task | Command |
|---|---|
| Account snapshot (all KPIs, one call) | python account_overview.py |
| Campaign performance table | python get_campaign_performance.py --date-preset last_30d |
| Pull active ad copy (for a creative/audit read) | python get_active_ads_copy.py |
| Branded HTML dashboard | cd ../../meta-reporting/scripts && python generate_dashboard.py --date-preset last_30d |
The dashboard (with your logo)
generate_dashboard.py writes a shareable HTML file: KPI tiles (spend, leads, cost per lead, CTR, CPM, reach) plus a per-campaign table, sorted by spend.
cd .claude/skills/meta-reporting/scripts
python generate_dashboard.py # last 30 days -> meta-dashboard.html
python generate_dashboard.py --date-preset last_7d --out weekly.html
It ships with the Frontal logo by default. It's your dashboard - rebrand it:
- Set
DASHBOARD_LOGO_URLandDASHBOARD_BRAND_NAMEin.env, or - Pass
--logo https://yourbrand.com/logo.png --brand "Your Brand".
No image API, no external service - just the Meta API and Python. Open it with open meta-dashboard.html or attach the file to an email.
How to analyze (not just report)
Reporting is describing the numbers. Analysis is deciding what to do. Always:
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.
- 1mo ago First seen · 63 lines · 77 tokens per session scan A d26491af9415
meta-reporting is a skill published in the GitHub repository swan-gtm/gtm-skills (221 stars, last pushed 8d ago), licensed MIT. It adds 77 tokens to every session and 902 once invoked, about $0.0003 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 2026-09-03.
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