Agent Scripts is a repository of shared instructions, reusable skills, helper scripts, and local safeguards for coding-agent workflows across multiple workspaces. It is for maintaining common agent behavior and portable development utilities between repositories.
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 steipete/agent-scripts --skill swiftui-performance-auditgit clone --depth 1 https://github.com/steipete/agent-scriptsWrote 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/steipete/agent-scripts/swiftui-performance-audit)<a href="https://agentmods.dev/skills/steipete/agent-scripts/swiftui-performance-audit"><img src="https://agentmods.dev/badge/skills/steipete/agent-scripts/swiftui-performance-audit/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/steipete/agent-scripts/swiftui-performance-audit"><img src="https://agentmods.dev/badge/skills/steipete/agent-scripts/swiftui-performance-audit.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00024 | $0.01222 |
| Opus 5.5 | $0.00010 | $0.00489 |
| Sonnet 5.5 | $0.00005 | $0.00244 |
| Haiku 4.5 | $0.00002 | $0.00122 |
Grade A, and why
swiftui-performance-audit 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 — 190 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SwiftUI Performance Audit
Attribution: copied from @Dimillian’s Dimillian/Skills (2025-12-31).
Overview
Audit SwiftUI view performance end-to-end, from instrumentation and baselining to root-cause analysis and concrete remediation steps.
Workflow Decision Tree
- If the user provides code, start with "Code-First Review."
- If the user only describes symptoms, ask for minimal code/context, then do "Code-First Review."
- If code review is inconclusive, go to "Guide the User to Profile" and ask for a trace or screenshots.
1. Code-First Review
Collect:
- Target view/feature code.
- Data flow: state, environment, observable models.
- Symptoms and reproduction steps.
Focus on:
- View invalidation storms from broad state changes.
- Unstable identity in lists (
idchurn,UUID()per render). - Heavy work in
body(formatting, sorting, image decoding). - Layout thrash (deep stacks,
GeometryReader, preference chains). - Large images without downsampling or resizing.
- Over-animated hierarchies (implicit animations on large trees).
Provide:
- Likely root causes with code references.
- Suggested fixes and refactors.
- If needed, a minimal repro or instrumentation suggestion.
2. Guide the User to Profile
Explain how to collect data with Instruments:
- Use the SwiftUI template in Instruments (Release build).
- Reproduce the exact interaction (scroll, navigation, animation).
- Capture SwiftUI timeline and Time Profiler.
- Export or screenshot the relevant lanes and the call tree.
Ask for:
- Trace export or screenshots of SwiftUI lanes + Time Profiler call tree.
- Device/OS/build configuration.
3. Analyze and Diagnose
Prioritize likely SwiftUI culprits:
- View invalidation storms from broad state changes.
- Unstable identity in lists (
idchurn,UUID()per render). - Heavy work in
body(formatting, sorting, image decoding). - Layout thrash (deep stacks,
GeometryReader, preference chains). - Large images without downsampling or resizing.
- Over-animated hierarchies (implicit animations on large trees).
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 190 lines · 24 tokens per session scan A 594a38f2568e
swiftui-performance-audit is a skill published in the GitHub repository steipete/agent-scripts (7,379 stars, last pushed 2d ago), licensed MIT. It adds 24 tokens to every session and 1,222 once invoked, about $0.0001 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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