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-gspgit 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-gsp)<a href="https://agentmods.dev/skills/charlieviettq/awesome-agent-skill/algo-ad-gsp"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/algo-ad-gsp/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-gsp"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/algo-ad-gsp.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 | $0.00072 | $0.00961 |
| Opus 5 | $0.00036 | $0.00481 |
| Sonnet 5 | $0.00014 | $0.00192 |
| Haiku 4.5 | $0.00007 | $0.00096 |
Grade A, and why
"algo-ad-gsp" 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
91% identical to algo-ad-gsp — 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Generalized Second Price Auction
Overview
GSP allocates K ad slots to N bidders, assigning the highest bidder the top slot, second-highest the second slot, etc. Each winner pays the bid of the advertiser ONE POSITION BELOW them (per-slot second price). Used by Google Ads and Bing Ads. Runs in O(N log N) for sorting bids.
When to Use
Trigger conditions:
- Understanding search engine ad auction mechanics
- Computing ad position and cost-per-click from bid and quality data
- Analyzing bidding strategy in sponsored search
When NOT to use:
- When you need incentive-compatible truthful bidding (use VCG mechanism)
- When analyzing display/programmatic ad auctions (typically use first-price)
Algorithm
IRON LAW: GSP Is NOT Incentive-Compatible
Unlike Vickrey (single-item second-price) auctions, truthful bidding
is NOT a dominant strategy in GSP. Bidders may strategically shade
bids below their true value. The equilibrium depends on competitor bids.
Ad Rank = Bid × Quality Score (Google's variant adds format/extensions).
Phase 1: Input Validation
Collect: bids, quality scores (or ad rank scores) for all competing advertisers. Define available slot positions and their click-through rate multipliers. Gate: All bids positive, quality scores in valid range.
Phase 2: Core Algorithm
- Compute Ad Rank for each advertiser: AdRank_i = Bid_i × QualityScore_i
- Sort advertisers by Ad Rank descending
- Assign top-K to slots 1 through K
- Compute payment: CPC_i = AdRank_{i+1} / QualityScore_i (price to maintain position)
- Last slot winner pays the minimum bid threshold
Phase 3: Verification
Check: all payments ≤ bids, positions ordered by Ad Rank, no advertiser pays more than their bid. Gate: Payment ≤ bid for all winners, positions consistent.
Phase 4: Output
Return slot assignments with positions, CPCs, and estimated clicks.
Output Format
{
"slots": [{"advertiser": "A", "position": 1, "ad_rank": 8.5, "cpc": 2.10, "est_clicks": 100}],
"metadata": {"total_bidders": 15, "slots_available": 4, "auction_type": "gsp"}
}
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 · 86 lines · 72 tokens per session scan A e9d84e6fa44c
"algo-ad-gsp" is a skill published in the GitHub repository charlieviettq/awesome-agent-skill (25 stars, last pushed 1mo ago), licensed MIT. It adds 72 tokens to every session and 961 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to algo-ad-gsp, differing in 8 lines, and is treated as a copy.
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