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 hamzaPixl/pixl-ai --skill benchmarkgit clone --depth 1 https://github.com/hamzaPixl/pixl-aiWrote 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/hamzapixl/pixl-ai/benchmark)<a href="https://agentmods.dev/skills/hamzapixl/pixl-ai/benchmark"><img src="https://agentmods.dev/badge/skills/hamzapixl/pixl-ai/benchmark/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/hamzapixl/pixl-ai/benchmark"><img src="https://agentmods.dev/badge/skills/hamzapixl/pixl-ai/benchmark.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 | $0.00061 | $0.01320 |
| Opus 5 | $0.00030 | $0.00660 |
| Sonnet 5 | $0.00012 | $0.00264 |
| Haiku 4.5 | $0.00006 | $0.00132 |
Grade A, and why
benchmark 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 9d 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 — 164 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Benchmark
Compare local code against real-world open-source implementations. Find high-quality references, analyze differences, and produce actionable improvement recommendations.
Workflow
Step 1: Scope
Identify what to benchmark from the user's request or argument:
- Pattern: a specific code pattern (e.g. "repository pattern", "middleware chain")
- Architecture: a structural approach (e.g. "hexagonal architecture", "monorepo layout")
- Feature: a concrete feature implementation (e.g. "auth with JWT refresh", "i18n setup")
- Library usage: how a library is used in practice (e.g. "Prisma with soft deletes", "Zustand store patterns")
Read the local implementation to understand:
- Which files implement the pattern (use Glob + Grep)
- The current approach, structure, and key decisions
- Stack details (language, framework, libraries) to target relevant references
Ask the user to confirm scope if ambiguous (AskUserQuestion).
Step 2: Search for References
Use WebSearch to find 3–5 high-quality references. Search strategies:
"<pattern> site:github.com <stack>" — direct GitHub repos
"<pattern> best practices <framework> <year>" — articles and guides
"<pattern> example implementation <language>" — tutorials with code
"awesome-<topic> github" — curated lists
Filter criteria:
- Stars/popularity: prefer repos with >500 stars or articles from known sources
- Recency: prefer references updated within the last 2 years
- Stack match: same language/framework as the local codebase
- Production quality: skip toy examples, prefer real-world usage
Collect for each reference:
- URL
- Repo name or article title
- Why it's relevant
- Star count / authority signal
Step 3: Analyze References
Use WebFetch on the top 3 matches to extract:
- Approach: high-level strategy and design decisions
- File structure: how they organize the feature
- Key patterns: naming, error handling, testing, configuration
- Trade-offs: what they optimize for (simplicity, performance, extensibility)
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.
- 9d ago First seen · 164 lines · 61 tokens per session scan A 5e1405788b00
benchmark is a skill published in the GitHub repository hamzaPixl/pixl-ai (2 stars, last pushed 4mo ago), licensed MIT. It adds 61 tokens to every session and 1,320 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 2026-08-31.
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