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 Arcadi4/nerdy --skill online-algorithmsgit clone --depth 1 https://github.com/Arcadi4/nerdyWrote 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/arcadi4/nerdy/online-algorithms)<a href="https://agentmods.dev/skills/arcadi4/nerdy/online-algorithms"><img src="https://agentmods.dev/badge/skills/arcadi4/nerdy/online-algorithms/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/arcadi4/nerdy/online-algorithms"><img src="https://agentmods.dev/badge/skills/arcadi4/nerdy/online-algorithms.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 | $0.00045 | $0.04038 |
| Opus 5.5 | $0.00018 | $0.01615 |
| Sonnet 5.5 | $0.00009 | $0.00808 |
| Haiku 4.5 | $0.00005 | $0.00404 |
Grade A, and why
online-algorithms 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 365 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Online Algorithms
Overview
Online-algorithm answers must compare decisions made without future knowledge against an offline optimum that sees the entire input.
Core principle: never judge an online algorithm by its standalone worst case; judge the worst-case ratio between its cost and the cost of a future-knowing optimum under the same input model and adversary.
Shared CLRS Conventions
Follow the parent clrs skill for mathematical formatting, direct polished answers, theorem preconditions, and formula-free headings.
Answer Formatting Guardrail
Online-algorithm exercises often invite comparison tables. Do not put costs, thresholds, ratios, request sequences with symbolic block names, or potential-function expressions in table cells or inline code spans.
Use prose labels for cases, then put the associated expression in a display block immediately underneath. A safe pattern is:
- Name the case in words.
- State in prose which quantity is being computed.
- Put the cost, ratio, bound, or sequence in a display block.
- Return to prose for the interpretation.
If a verification answer needs a summary table, keep the table verbal only, such as "early arrival" and "ratio one," and put the exact expressions outside the table.
For elevator or ski-rental threshold answers, do not compress the case split to only the two dangerous ratios. Always name the harmless early-arrival piece first, then the just-after-give-up piece, then the late-arrival piece. The dangerous pieces determine the maximum, but the early piece confirms that the case split is complete.
When to Use
Use this skill for:
- inputs revealed over time, including request sequences, releases, arrivals, or operation streams;
- competitive-ratio proofs for minimization problems;
- rent-or-buy or wait-then-switch strategies such as elevator, ski rental, and cow-path search;
- self-organizing lists, list update, move-to-front, and adjacent-swap cost models;
- online caching, paging, cache-eviction policies, marking algorithms, and adversarial lower bounds;
- randomized online algorithms where the adversary model determines whether randomization helps.
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.
- 1mo ago First seen · 365 lines · 45 tokens per session scan A 5ccd75c6f264
online-algorithms is a skill published in the GitHub repository Arcadi4/nerdy (6 stars, last pushed 6mo ago), licensed MIT. It adds 45 tokens to every session and 4,038 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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