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 search-and-optimizationgit 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/search-and-optimization)<a href="https://agentmods.dev/skills/sequenzia/agent-alchemy/search-and-optimization"><img src="https://agentmods.dev/badge/skills/sequenzia/agent-alchemy/search-and-optimization/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/search-and-optimization"><img src="https://agentmods.dev/badge/skills/sequenzia/agent-alchemy/search-and-optimization.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 | $0.00070 | $0.04643 |
| Opus 5.5 | $0.00028 | $0.01857 |
| Sonnet 5.5 | $0.00014 | $0.00929 |
| Haiku 4.5 | $0.00007 | $0.00464 |
Grade A, and why
search-and-optimization 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 — 494 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Search and Optimization Patterns
This reference covers eight foundational search and optimization techniques that appear across competitive programming, coding interviews, and production algorithms. Each pattern includes recognition signals, a core idea summary, a Python template with type hints, key edge cases, and common mistakes.
Pattern Recognition Table
| Trigger Signals | Technique | Typical Complexity |
|---|---|---|
| Sorted array, find target, O(log n) required | Binary Search | O(log n) |
| "Minimize the maximum", "find smallest feasible value" | Binary Search on Answer | O(n log S) where S = search space |
| Sorted array, find pair with target sum, in-place | Two Pointers | O(n) |
| Fixed/variable-length subarray, substring constraints | Sliding Window | O(n) |
| Local optimal leads to global optimal, exchange argument | Greedy | O(n log n) typical |
| Range sum queries, subarray sums, 2D region sums | Prefix Sums | O(n) build, O(1) query |
| Overlapping intervals, merge/insert, scheduling | Merge Intervals | O(n log n) |
| Next greater/smaller element, histogram areas | Monotonic Stack | O(n) |
Constraint-to-Technique Mapping
When the problem does not immediately suggest a technique, use constraints as a guide:
- n <= 10^5 and "find minimum/maximum feasible" — Binary search on answer with a greedy or simulation check function.
- Sorted input + pair/triplet finding — Two pointers before considering hash maps. Saves space and often required by the problem.
- Contiguous subarray/substring with a constraint — Sliding window. If the constraint is a sum threshold, variable-size window. If fixed length k, fixed-size window.
- Multiple range sum queries on static data — Prefix sums. For 2D grids, build a 2D prefix sum matrix.
- "Given a set of intervals" — Sort by start (or end), then merge or sweep. Check if the problem is really interval scheduling (sort by end, greedy).
- "For each element, find the next greater/smaller" — Monotonic stack. Direction of traversal (left-to-right or right-to-left) depends on whether you need "next" or "previous".
- Optimization under constraints with greedy proof — Try exchange argument: if swapping any two elements in the solution cannot improve it, greedy works.
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 · 494 lines · 70 tokens per session scan A 0a6394ba9df8
search-and-optimization is a skill published in the GitHub repository sequenzia/agent-alchemy (45 stars, last pushed 4mo ago), licensed MIT. It adds 70 tokens to every session and 4,643 once invoked, about $0.0003 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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