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 charlieviettq/awesome-agent-skill --skill algo-ad-biddinggit clone --depth 1 https://github.com/charlieviettq/awesome-agent-skillWrote 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/charlieviettq/awesome-agent-skill/algo-ad-bidding)<a href="https://agentmods.dev/skills/charlieviettq/awesome-agent-skill/algo-ad-bidding"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/algo-ad-bidding/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/charlieviettq/awesome-agent-skill/algo-ad-bidding"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/algo-ad-bidding.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 | $0.00975 |
| Opus 5 | $0.00037 | $0.00487 |
| Sonnet 5 | $0.00015 | $0.00195 |
| Haiku 4.5 | $0.00007 | $0.00097 |
Grade A, and why
"algo-ad-bidding" 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.
This is a copy
94% identical to algo-ad-bidding — 8 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ad Bidding Strategies
Overview
Bidding strategies determine how much an advertiser pays per auction. Range from manual CPC (full control) to automated strategies (Target CPA, Target ROAS, Maximize Conversions) that use ML to optimize bids in real-time based on contextual signals.
When to Use
Trigger conditions:
- Choosing between manual and automated bidding strategies
- Setting up or troubleshooting Target CPA / Target ROAS campaigns
- Analyzing bid strategy performance and making adjustments
When NOT to use:
- When designing the auction mechanism itself (use GSP/VCG)
- When building a CTR prediction model (use CTR prediction skill)
Algorithm
IRON LAW: Automated Bidding Requires SUFFICIENT Conversion Data
Below ~30 conversions/month, the algorithm lacks signal and performs
WORSE than manual bidding. Strategy selection depends on data volume:
- < 30 conv/month: Manual CPC or Maximize Clicks
- 30-50 conv/month: Maximize Conversions
- 50+ conv/month: Target CPA
- 50+ conv/month + revenue data: Target ROAS
Phase 1: Input Validation
Assess: monthly conversion volume, conversion tracking accuracy, campaign budget, business goal (volume vs efficiency vs revenue). Gate: Conversion tracking verified, sufficient data for chosen strategy.
Phase 2: Core Algorithm
Manual CPC: Set bid per keyword. Adjust based on: device, time, location, audience performance data.
Target CPA: 1. Set target cost-per-acquisition. 2. Algorithm predicts conversion probability per auction using contextual signals. 3. Bids up for high-probability conversions, down for low. 4. Aims to average at target CPA over time.
Target ROAS: Same as CPA but optimizes for return on ad spend = conversion_value / cost.
Phase 3: Verification
Monitor: actual CPA vs target, conversion volume stability, impression share changes, budget utilization. Gate: Actual CPA within 20% of target after learning period (2-4 weeks).
Phase 4: Output
Return strategy recommendation with expected performance ranges.
What ships with it
3 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.
- 12d ago First seen · 89 lines · 74 tokens per session scan A 7a5685c2a48a
"algo-ad-bidding" is a skill published in the GitHub repository charlieviettq/awesome-agent-skill (25 stars, last pushed 1mo ago), licensed MIT. It adds 74 tokens to every session and 975 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to algo-ad-bidding, differing in 8 lines, and is treated as a copy.
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