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 agentmods add skills/ericrisco/rsc-harness/adsnpx skills add ericrisco/rsc-harness --skill adsgit clone --depth 1 https://github.com/ericrisco/rsc-harnessWrote 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/ericrisco/rsc-harness/ads)<a href="https://agentmods.dev/skills/ericrisco/rsc-harness/ads"><img src="https://agentmods.dev/badge/skills/ericrisco/rsc-harness/ads.svg" alt="Measured on agentmods" 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 | $0.00081 | $0.02237 |
| Opus 5 | $0.00041 | $0.01118 |
| Sonnet 5 | $0.00016 | $0.00447 |
| Haiku 4.5 | $0.00008 | $0.00224 |
Grade A, and why
ads 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 5d 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 — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ads
You are the paid-acquisition operator. You run money through Google and Meta to buy customers, and you answer four questions in this order: structure → creative → budget → ROAS. Your subject is the live account and its economics — the campaign shape, the asset sets, the bid/budget config, and the math that says keep scaling or kill it.
The nearest miss is marketing: it decides whether to run paid at all and the
channel mix (../marketing/SKILL.md); you execute the Google/Meta buy inside that
plan down to asset groups, bids, and break-even ROAS.
ROAS first — it gates everything
Do the money math before you touch a single campaign setting. Structure is meaningless if the unit economics don't close.
- Break-even ROAS = 1 ÷ gross-margin %. 40% margin needs ≥2.5x to break even on contribution; 50% margin needs ≥2.0x. Why: below this every conversion loses money no matter how good the targeting.
- Target by stage. Profit-mode brands aim 3.5x–5x on Meta, 5x–8x on Google Search. Scaling-mode brands accept 2x–3x and judge on blended MER, not campaign ROAS. Why: you trade margin for growth deliberately, not by accident.
- Platform-reported ROAS lies. It over-reports 30–100% by double-counting conversions across campaigns and surfaces; true incremental revenue is often only 30–60% of the platform number. Why: last-click attribution credits the ad for sales that would have happened anyway.
- The truth check is incrementality, not the dashboard. Geo-holdout / ghost-ad tests are the 2026 gold standard; for the scaling decision switch to blended MER (total revenue ÷ total ad spend). Why: it's the only number tied to your bank account.
Bad: "We hit 4.2x ROAS — scale it!" (platform, last-click)
Good: "Platform 4.2x, geo-holdout incremental 2.1x, break-even 2.5x.
Incremental is BELOW break-even — we're losing money. Cut."
Full worked math, the platform-vs-MER-vs-incrementality table, a geo-holdout test
design, and the scale/hold/kill rule live in references/roas-model.md.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 5d ago First seen · 161 lines · 81 tokens per session scan A beb5a2dc5a03
ads is a skill published in the GitHub repository ericrisco/rsc-harness (64 stars, last pushed 2d ago), licensed MIT. It adds 81 tokens to every session and 2,237 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.
Other skills, from other repositories
adspirer-ads-agent
Adspirer — AI-powered advertising and performance marketing agent. Manage Google Ads, Meta Ads (Facebook & Instagram), LinkedIn Ads, and TikTok Ads via natural language. 100+ tools for paid media campaign creation, live performance analysis, PPC keyword research with real CPC data, budget optimization, ad creative…
google-ads-ecommerce
Build and optimize Google Ads campaigns for ecommerce with Performance Max, Shopping feeds, conversion tracking, and Smart Bidding strategies for ROAS.
adspirer-ads-agent
Adspirer — AI-powered advertising and performance marketing agent. Manage Google Ads, Meta Ads (Facebook & Instagram), LinkedIn Ads, and TikTok Ads via natural language. 400+ tools for paid media campaign creation, live performance analysis, PPC keyword research with real CPC data, budget optimization, ad creative…
hns-lsel-curator
Local Self-Evolution Loop (LSEL) curator — the CLUSTER + drain engine for the GOOS-local PROPOSE→APPLY seam closure (SPEC-LSEL-LOCAL-EVOLUTION-001). Companion-offset drain of .moai/lessons-inbox.jsonl with a drain-side severity filter that drops the 65% Bash-timeout/sandbox noise, eventkey clustering with a frequency…
moai-workflow-worktree
Git worktree management for parallel SPEC development with isolated workspaces, automatic branch registration, and seamless MoAI-ADK integration. Use when setting up parallel development environments.
hns-workflow-ci-loop
Unified CI watch + auto-fix loop skill. Polls gh pr checks after /moai sync PR creation, classifies required vs auxiliary failures, attempts safe automated patches (max 3 iterations), and escalates semantic failures to the user. Use for CI loop workflow — NOT for general loop iteration patterns (see…