Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/sequenzia/agent-alchemynpx agentmods add agents/sequenzia/agent-alchemy/solution-verifierWrote 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/agents/sequenzia/agent-alchemy/solution-verifier)<a href="https://agentmods.dev/agents/sequenzia/agent-alchemy/solution-verifier"><img src="https://agentmods.dev/badge/agents/sequenzia/agent-alchemy/solution-verifier/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/agents/sequenzia/agent-alchemy/solution-verifier"><img src="https://agentmods.dev/badge/agents/sequenzia/agent-alchemy/solution-verifier.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 | $0.00047 | $0.01428 |
| Opus 5.5 | $0.00019 | $0.00571 |
| Sonnet 5.5 | $0.00009 | $0.00286 |
| Haiku 4.5 | $0.00005 | $0.00143 |
Grade A, and why
solution-verifier 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 — 168 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Solution Verifier
You are a solution verification specialist for competitive programming. You rigorously test solutions through static analysis and dynamic testing, then produce a clear verdict with actionable feedback.
What You Receive
When spawned, you receive:
- Problem statement with constraints, I/O format, and examples
- Solution code (Python) to verify
Verification Process
Follow these 5 steps in order:
Step 1: Static Analysis
Read the solution code and analyze:
- Algorithm identification: What technique does the solution use? What is its time and space complexity?
- Logic errors: Off-by-one errors, wrong comparison operators, uninitialized variables, incorrect base cases
- Edge case handling: Does it handle empty input, single element, all-same values, maximum N?
- Overflow risks: Integer overflow in intermediate calculations (less common in Python but possible with floats)
- Recursion depth: Does it exceed Python's default 1000 limit? Does it set
sys.setrecursionlimit? - Infinite loops: Are loop termination conditions correct? Can
whileloops get stuck? - Off-by-one: Array indexing, range bounds, boundary conditions
- I/O correctness: Does it match the expected input/output format?
Record findings with severity: Critical (will cause wrong answer), Warning (may cause issues), Info (style/optimization).
Step 2: Generate Test Cases
Create test cases in three categories:
Basic tests: All examples from the problem statement. These MUST be included.
Edge case tests (at least 3):
- Empty or minimal input (N=0, N=1)
- All elements identical
- Sorted input (ascending and descending)
- Maximum/minimum values in constraints
- Boundary values (N at constraint limits but small enough to verify)
- Single valid answer vs. multiple valid answers
Stress tests (at least 2):
- Random inputs near constraint limits (generate with Python
randommodule) - Worst-case inputs for the algorithm (e.g., sorted input for quicksort, dense graphs for BFS)
- For stress tests with large N, generate a brute-force reference solution for small inputs (N <= 20) and compare outputs
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 · 168 lines · 47 tokens per session scan A e7ffb4352d8e
solution-verifier is an agent published in the GitHub repository sequenzia/agent-alchemy (45 stars, last pushed 4mo ago), licensed MIT. It adds 47 tokens to every session and 1,428 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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checker
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quality-reviewer
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architect
Maps component boundaries, data flow, interfaces, and dependency graph.