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-vcggit 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-vcg)<a href="https://agentmods.dev/skills/charlieviettq/awesome-agent-skill/algo-ad-vcg"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/algo-ad-vcg/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-vcg"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/algo-ad-vcg.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 | $0.00077 | $0.00973 |
| Opus 5 | $0.00039 | $0.00487 |
| Sonnet 5 | $0.00015 | $0.00195 |
| Haiku 4.5 | $0.00008 | $0.00097 |
Grade A, and why
"algo-ad-vcg" 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
89% identical to algo-ad-vcg — 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
VCG Mechanism (Vickrey-Clarke-Groves)
Overview
VCG allocates slots to maximize total social welfare and charges each winner the externality they impose on others. Truthful bidding is a dominant strategy. Runs in O(N log N + K × N) where N=bidders, K=slots.
When to Use
Trigger conditions:
- Designing an incentive-compatible (truthful) multi-slot auction
- Computing welfare-maximizing allocations with externality pricing
- Academic analysis comparing VCG to GSP auctions
When NOT to use:
- When revenue maximization matters more than truthfulness (GSP often generates more revenue)
- For single-item auctions (standard Vickrey suffices)
Algorithm
IRON LAW: VCG Guarantees Truthful Bidding BUT May Not Maximize Revenue
VCG payments are based on externality (harm to others), not competition.
This makes VCG payments often LOWER than GSP payments. Platforms
choose GSP because it typically generates higher revenue despite
strategic bidding. Truthfulness has a revenue cost.
Phase 1: Input Validation
Collect true valuations per click for each advertiser and CTR for each slot position. Valuations must be non-negative. Gate: All valuations non-negative, slot CTRs decreasing by position.
Phase 2: Core Algorithm
- Compute welfare-maximizing allocation: assign advertisers to slots to maximize Σ(value_i × CTR_slot_i)
- For each winner i in slot s: compute total welfare WITHOUT advertiser i (re-optimize remaining bidders)
- VCG payment_i = (welfare of others without i) - (welfare of others with i present)
- This equals: Σ over lower positions j of (value_{j+1} × (CTR_j - CTR_{j+1}))
Phase 3: Verification
Check: all payments ≤ valuations (individual rationality), truthful bidding is dominant strategy, allocation maximizes welfare. Gate: IR satisfied, welfare is optimal.
Phase 4: Output
Return allocation with VCG payments and welfare metrics.
Output Format
{
"allocation": [{"advertiser": "A", "slot": 1, "vcg_payment_per_click": 1.80, "total_welfare_contribution": 500}],
"metadata": {"total_welfare": 1500, "total_revenue": 420, "mechanism": "vcg"}
}
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 · 85 lines · 77 tokens per session scan A e6cff6900d0b
"algo-ad-vcg" is a skill published in the GitHub repository charlieviettq/awesome-agent-skill (25 stars, last pushed 1mo ago), licensed MIT. It adds 77 tokens to every session and 973 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to algo-ad-vcg, differing in 8 lines, and is treated as a copy.
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