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-price-dynamicgit 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-price-dynamic)<a href="https://agentmods.dev/skills/charlieviettq/awesome-agent-skill/algo-price-dynamic"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/algo-price-dynamic/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-price-dynamic"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/algo-price-dynamic.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 | $0.00075 | $0.00950 |
| Opus 5 | $0.00037 | $0.00475 |
| Sonnet 5 | $0.00015 | $0.00190 |
| Haiku 4.5 | $0.00007 | $0.00095 |
Grade A, and why
"algo-price-dynamic" 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-price-dynamic — 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 — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dynamic Pricing
Overview
Dynamic pricing adjusts prices in real-time based on demand signals, time, inventory, and competitive conditions. Common in airlines, hotels, ride-sharing, and e-commerce. Objective: maximize revenue (or profit) subject to capacity/inventory constraints.
When to Use
Trigger conditions:
- Pricing perishable inventory (hotel rooms, airline seats, event tickets)
- Implementing demand-responsive pricing for e-commerce
- Building surge pricing or time-based pricing systems
When NOT to use:
- For one-time pricing decisions (use Van Westendorp or conjoint)
- When price changes are impractical (regulated markets, long-term contracts)
Algorithm
IRON LAW: Dynamic Pricing Requires REAL-TIME Data
Stale data produces prices optimal for PAST conditions, not current ones.
Three data streams must be current:
1. Demand signal (bookings, searches, cart additions)
2. Inventory/capacity status
3. Competitive prices (where applicable)
Update frequency: minutes for ride-sharing, hours for hotels, daily for retail.
Phase 1: Input Validation
Collect: current demand indicators, remaining inventory/capacity, time until expiration/event, competitor prices, price floor/ceiling constraints. Gate: Real-time data feeds connected, business rules defined.
Phase 2: Core Algorithm
Rule-based: If demand > threshold, increase price by X%. Tiered rules by inventory level.
Demand-curve based: 1. Estimate demand curve at current conditions. 2. Find price that maximizes revenue = P × Q(P). 3. Apply inventory constraint: if capacity is scarce, price up; if excess, price down.
ML-based: Train model to predict demand at each price point given context features. Optimize over predicted demand curve.
Phase 3: Verification
Monitor: revenue per unit, booking pace, customer complaints, competitive position. A/B test new pricing rules. Gate: Revenue improved without significant volume loss or customer backlash.
Phase 4: Output
Return recommended price with reasoning and expected impact.
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 · 91 lines · 75 tokens per session scan A bf33c3fd34f0
"algo-price-dynamic" is a skill published in the GitHub repository charlieviettq/awesome-agent-skill (25 stars, last pushed 1mo ago), licensed MIT. It adds 75 tokens to every session and 950 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-price-dynamic, differing in 8 lines, and is treated as a copy.
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