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 verifygit 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/verify)<a href="https://agentmods.dev/skills/sequenzia/agent-alchemy/verify"><img src="https://agentmods.dev/badge/skills/sequenzia/agent-alchemy/verify/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/verify"><img src="https://agentmods.dev/badge/skills/sequenzia/agent-alchemy/verify.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 | $0.00063 | $0.01115 |
| Opus 5.5 | $0.00025 | $0.00446 |
| Sonnet 5.5 | $0.00013 | $0.00223 |
| Haiku 4.5 | $0.00006 | $0.00112 |
Grade A, and why
verify 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 — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Solution Verifier
Verify competitive programming and LeetCode solutions for correctness through static analysis, automated test case generation, and execution.
CRITICAL: Complete ALL 4 phases. Do not stop after static analysis.
Phase 1: Parse Input
Goal: Extract the problem statement and solution code.
Parse $ARGUMENTS to extract:
- Problem statement — the full problem description with constraints, I/O format, and examples
- Solution code — the user's Python solution (inline code, file path, or pasted)
If both are in $ARGUMENTS, separate them. The problem statement typically comes first, followed by the solution code in a code block or after a separator.
If the solution is a file path, read it:
Read [file_path]
If the problem statement is missing, ask for it:
AskUserQuestion:
question: "Please provide the problem statement for verification. I need it to generate test cases and validate correctness."
options:
- label: "Paste problem text"
description: "Paste the full problem statement with constraints and examples"
- label: "Describe the problem"
description: "Describe what the problem asks — I'll generate test cases from that"
If the solution code is missing, ask for it:
AskUserQuestion:
question: "Please provide your solution code to verify."
options:
- label: "Paste code"
description: "Paste your Python solution code"
- label: "Provide file path"
description: "Give the path to your solution file"
If the solution is not Python, note this to the user and proceed with analysis (the verifier agent will handle language adaptation).
Phase 2: Spawn Verifier Agent
Goal: Delegate verification to the specialized agent.
Use the Task tool to spawn the solution-verifier agent:
Task:
subagent_type: "agent-alchemy-cs-tools:solution-verifier"
prompt: |
## Problem Statement
[full problem text with all constraints, I/O format, and examples]
## Solution Code
```python
[the user's solution code]
```
Perform full verification following your structured process:
1. Static analysis for logic errors and edge case handling
2. Generate test cases (basic, edge, stress)
3. Write and execute test harness
4. Compile report with verdict
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 · 137 lines · 63 tokens per session scan A 9219cb193a7f
verify is a skill published in the GitHub repository sequenzia/agent-alchemy (45 stars, last pushed 4mo ago), licensed MIT. It adds 63 tokens to every session and 1,115 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.
Other skills, from other repositories
linked-intent-dev
Guide for linked-intent development (LID). Consult for ALL code changes. Walks changes through a mode-aware six-phase workflow (HLD → LLD → EARS → intent-narrowing edge audit → tests-first → code) with mandatory stops between each phase. Bugs walk the arrow like any other change — no short-circuit. Enforces cascade…
bidirectional-differential
Audit coherence across an arrow of intent by running two parallel fresh Claude sessions — one reconstructs code from a single EARS, the other reconstructs the EARS from stripped code — then classifies the drift between them. Use when the user invokes /differential-audit, asks to audit EARS-to-code drift for a feature…
mutation-test
Mutation testing with two engines. Uses the project's NATIVE mutation runner (StrykerJS / Infection / mutmut / PIT / cargo-mutants) when one is configured — installing it on explicit consent when it is not — for a reproducible, comparable score; and an LLM-guided engine for the mutation classes native mutators cannot…
write-tests
Write tests for existing production code. Processes ONE file at a time through a full pipeline: analyze, inventory (frozen BEFORE writing), write, executable coverage gate, verify, blind coverage audit, adversarial review, log. Uses CodeSift for discovery and analysis when available. Modes: [path] (specific target)…
auto-loop
TDD-based autonomous development loop with checkpoint recovery and observability changelog.
code-audit
Batch audit of production files against CQ1-CQ40 quality gates and CAP1-CAP29 anti-patterns. Tiered output (A/B/C/D), critical gate enforcement, evidence-backed scoring, cross-file pattern analysis, and prioritized execution plan. Flags: zuvo:code-audit all | [path] | [file] | --deep | --quick | --services |…