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 agentmods add skills/sequenzia/agent-alchemy/solvenpx skills add sequenzia/agent-alchemy --skill solvegit clone --depth 1 https://github.com/sequenzia/agent-alchemyWhat 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 | $0.00063 | $0.01600 |
| Opus 5 | $0.00032 | $0.00800 |
| Sonnet 5 | $0.00013 | $0.00320 |
| Haiku 4.5 | $0.00006 | $0.00160 |
Grade A, and why
solve 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 2d 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 — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Competitive Programming Problem Solver
Solve competitive programming and LeetCode-style problems with clear educational explanations, step-by-step walkthroughs, and verified Python solutions.
CRITICAL: Complete ALL 4 phases. Do not stop after classification or skip the agent.
Phase 1: Parse Input
Goal: Extract and understand the problem statement.
If $ARGUMENTS is provided, parse the problem statement from it. Extract:
- Problem description and objective
- Input/output format
- Constraints (N, M, value ranges)
- Example test cases with expected outputs
- Any special conditions (modular arithmetic, multiple test cases, interactive)
If $ARGUMENTS is empty or unclear, use AskUserQuestion to request the problem:
AskUserQuestion:
question: "Please provide the problem statement. You can paste the full text, describe it in your own words, or provide a link."
options:
- label: "Paste problem text"
description: "Paste the full problem statement including constraints and examples"
- label: "Describe the problem"
description: "Describe what the problem asks in your own words"
If the problem statement is ambiguous or missing key information (constraints, examples), ask for clarification before proceeding.
Phase 2: Classify Problem
Goal: Determine the algorithmic category, technique, and difficulty.
Analyze the problem to determine:
2.1 Primary Category
Match to one of:
- Dynamic Programming — optimization over subsequences, counting ways, overlapping subproblems
- Graph Algorithms — connectivity, shortest paths, traversal, network problems
- Search and Optimization — binary search, two pointers, greedy, interval problems
- Data Structures — specialized structures needed (heap, trie, segment tree)
- Math and Combinatorics — number theory, counting, modular arithmetic, game theory
- String Algorithms — pattern matching, palindromes, hashing
2.2 Sub-pattern
Identify the specific technique within the category (e.g., "0/1 Knapsack", "Dijkstra", "Sliding Window").
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.
- 2d ago First seen · 165 lines · 63 tokens per session scan A fe7a65f2abbb
solve is a skill published in the GitHub repository sequenzia/agent-alchemy (43 stars, last pushed 3mo ago), licensed MIT. It adds 63 tokens to every session and 1,600 once invoked, about $0.0003 per session on Opus 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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map-understand
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