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 sequenzia/agent-alchemy --skill data-structuresgit clone --depth 1 https://github.com/sequenzia/agent-alchemyWrote 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/sequenzia/agent-alchemy/data-structures)<a href="https://agentmods.dev/skills/sequenzia/agent-alchemy/data-structures"><img src="https://agentmods.dev/badge/skills/sequenzia/agent-alchemy/data-structures/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/sequenzia/agent-alchemy/data-structures"><img src="https://agentmods.dev/badge/skills/sequenzia/agent-alchemy/data-structures.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 | $0.00053 | $0.04310 |
| Opus 5.5 | $0.00021 | $0.01724 |
| Sonnet 5.5 | $0.00011 | $0.00862 |
| Haiku 4.5 | $0.00005 | $0.00431 |
Grade A, and why
data-structures 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 — 451 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Structure Patterns
This skill provides recognition signals, core ideas, Python templates, and pitfall guidance for seven advanced data structure patterns. Use it when a problem's constraints or access patterns suggest a specialized structure beyond basic arrays, hash maps, or linked lists. Each pattern section follows a consistent format: when to reach for it, how it works, a clean implementation template, and the mistakes that cost time in practice.
Pattern Recognition Table
| Trigger Signals | Technique | Typical Complexity |
|---|---|---|
| "k-th largest/smallest", "top K", "merge K sorted" | Heap / Priority Queue | O(n log k) |
| "next greater/smaller element", "sliding window max/min" | Monotonic Stack / Queue | O(n) |
| "prefix matching", "autocomplete", "word search in grid" | Trie | O(L) per operation |
| "range query + point update", "range min/max/sum" | Segment Tree | O(log n) per query/update |
| "prefix sums with updates", "count of elements less than X" | Fenwick Tree (BIT) | O(log n) per query/update |
| "balanced parentheses", "evaluate expression", "nested structures" | Stack-Based Parsing | O(n) |
| "rank of element", "k-th smallest in dynamic set", "floor/ceiling" | Ordered Set (SortedList) | O(log n) per operation |
Constraint-to-Technique Mapping
When the problem statement does not name a structure directly, use constraints to narrow the choice:
- n <= 10^5 with repeated range queries -- Segment Tree or Fenwick Tree. Prefer Fenwick when only prefix sums are needed; use Segment Tree for arbitrary range operations.
- n <= 10^6 and "next greater" or "span" language -- Monotonic Stack. Linear time is essential at this scale.
- String dictionary with prefix lookups -- Trie. Hash maps work for exact match but not prefix enumeration.
- "Top K" or "k-th element" with streaming data -- Heap. A size-k heap avoids sorting the full dataset.
- Dynamic insertions + rank/order queries -- SortedList. Python lacks a built-in balanced BST; sortedcontainers fills this gap.
- Nested or recursive syntax (math expressions, HTML tags) -- Stack-Based Parsing. The stack mirrors the nesting depth.
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 · 451 lines · 53 tokens per session scan A 4f5f4f1ecaa9
data-structures is a skill published in the GitHub repository sequenzia/agent-alchemy (45 stars, last pushed 4mo ago), licensed MIT. It adds 53 tokens to every session and 4,310 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 2026-08-30.
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