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-sc-bullwhipgit 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-sc-bullwhip)<a href="https://agentmods.dev/skills/charlieviettq/awesome-agent-skill/algo-sc-bullwhip"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/algo-sc-bullwhip/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-sc-bullwhip"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/algo-sc-bullwhip.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 | $0.00079 | $0.01043 |
| Opus 5 | $0.00039 | $0.00522 |
| Sonnet 5 | $0.00016 | $0.00209 |
| Haiku 4.5 | $0.00008 | $0.00104 |
Grade A, and why
"algo-sc-bullwhip" 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
95% identical to algo-sc-bullwhip — 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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Bullwhip Effect Analysis
Overview
The bullwhip effect describes how small fluctuations in consumer demand amplify progressively at each upstream stage of the supply chain. A 5% retail demand increase can become a 40% order spike at the manufacturer. Caused by demand signal processing, order batching, price fluctuations, and rationing/shortage gaming.
When to Use
Trigger conditions:
- Diagnosing why supplier orders are far more volatile than end-consumer demand
- Quantifying demand amplification across supply chain tiers
- Designing strategies to reduce order variability
When NOT to use:
- When demand is genuinely volatile (not amplified) — the issue is demand forecasting
- For single-echelon inventory optimization (use EOQ or safety stock)
Algorithm
IRON LAW: Demand Variability Amplifies at EACH Upstream Stage
Bullwhip ratio = Var(orders) / Var(demand). A ratio > 1 at any stage
confirms the bullwhip effect. The four root causes (Lee et al., 1997):
1. Demand signal processing (forecasting with moving averages)
2. Order batching (periodic review, MOQs)
3. Price fluctuations (forward buying during promotions)
4. Rationing and shortage gaming (inflating orders during scarcity)
Phase 1: Input Validation
Collect: end-consumer demand time series AND order time series at each supply chain stage (retailer → distributor → manufacturer → supplier). Gate: At least 2 tiers of order data, minimum 26 periods.
Phase 2: Core Algorithm
- Compute variance of demand at each tier
- Compute bullwhip ratio per tier: BWR_i = Var(orders_i) / Var(orders_{i-1})
- Identify contribution of each cause: batch size analysis, promotion calendar overlap, forecast method evaluation
- Quantify cost: excess inventory carrying cost, expediting cost, capacity misallocation
Phase 3: Verification
Check: BWR > 1 at upstream stages (confirms bullwhip). Correlate order spikes with identifiable causes (promotions, forecast updates, batch cycles). Gate: Bullwhip quantified and root causes identified.
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 · 88 lines · 79 tokens per session scan A 8bcfc6f43071
"algo-sc-bullwhip" is a skill published in the GitHub repository charlieviettq/awesome-agent-skill (25 stars, last pushed 1mo ago), licensed MIT. It adds 79 tokens to every session and 1,043 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to algo-sc-bullwhip, differing in 8 lines, and is treated as a copy.
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