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 hash-tablesgit 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/hash-tables)<a href="https://agentmods.dev/skills/arcadi4/nerdy/hash-tables"><img src="https://agentmods.dev/badge/skills/arcadi4/nerdy/hash-tables/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/hash-tables"><img src="https://agentmods.dev/badge/skills/arcadi4/nerdy/hash-tables.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 | $0.03502 |
| Opus 5.5 | $0.00018 | $0.01401 |
| Sonnet 5.5 | $0.00009 | $0.00700 |
| Haiku 4.5 | $0.00005 | $0.00350 |
Grade A, and why
hash-tables 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 — 226 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hash Tables
Overview
Hash tables are not magic constant-time dictionaries. The core move is to separate the address calculation, collision policy, load-factor budget, and adversarial model, then choose the weakest mechanism that keeps expected probes, memory movement, deletion behavior, and cache locality within the real service constraints.
Shared CLRS Conventions
Follow the parent clrs skill for mathematical formatting, formula-free headings, direct polished answers, and CLRS-wide answer style.
When to Use
- A prompt involves dictionaries, symbol tables, direct addressing, hash tables, hash functions, collision resolution, chaining, open addressing, double hashing, linear probing, tombstones, load factor, or universal hashing.
- You need to translate expected-time hashing assumptions into production capacity planning, p99 latency, adversarial-input defense, or memory-hierarchy behavior.
- A proposed design relies on expected constant time and you must state the hashing assumptions that make the claim meaningful.
- Keys are integers, byte strings, vectors, identifiers, request parameters, or user-controlled data whose distribution may matter.
Do not use this skill merely because a platform dictionary exists. Use the language or database default unless algorithm choice, attack resistance, resizing behavior, deletion semantics, or cache locality is the actual question.
First Decision: What Problem Are You Really Solving?
| Need | Prefer | Why |
|---|---|---|
| Dense small universe with no huge gaps | Direct addressing or bit vector | Worst-case constant lookup is paid for by allocating the whole universe |
| Sparse universe with dynamic inserts and deletes | Hash table | Compresses the address space to live keys while keeping expected constant operations |
| Frequent deletes and simple stable performance | Chaining, or open addressing with explicit cleanup policy | Deletion is straightforward for chaining; tombstones in open addressing accumulate |
| Cache-sensitive high-throughput table | Open addressing, often linear-probing-family designs at conservative load | Avoids pointers and probes contiguous cache blocks |
| Unknown or adversarial keys | Randomized or keyed hash family | Fixed static functions can be collision-targeted |
| Static set with only search | Consider sorted array, perfect hashing, or open addressing with extra capacity | Preprocessing can buy simpler worst-case lookup behavior |
| Variable-length byte strings | A string hash or keyed/cryptographic-inspired mixer plus a collision strategy | Division and word multiply-shift are integer-key building blocks, not complete string-table designs |
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 · 226 lines · 45 tokens per session scan A d8d3cbd84dc3
hash-tables 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 3,502 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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