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/aiskillstore/marketplace/python-async-patternsnpx skills add aiskillstore/marketplace --skill python-async-patternsgit clone --depth 1 https://github.com/aiskillstore/marketplaceWhat 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 | $0.00040 | $0.01016 |
| Opus 5 | $0.00020 | $0.00508 |
| Sonnet 5 | $0.00008 | $0.00203 |
| Haiku 4.5 | $0.00004 | $0.00102 |
Grade A, and why
python-async-patterns scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
async def fetch(url: str) -> str: The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
What ships with it
10 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.
- assets/async-project-template.py 3.6 KB runs code
- references/aiohttp-patterns.md 6.1 KB
- references/concurrency-patterns.md 6.0 KB
- references/debugging-async.md 5.8 KB
- references/error-handling.md 12 KB
- references/mixing-sync-async.md 5.7 KB
- references/performance.md 10 KB
- references/production-patterns.md 10 KB
- scripts/find-blocking-calls.sh 1.4 KB runs code
- skill-report.json 52 KB
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 · 161 lines · 40 tokens per session scan A c57e3c129203
python-async-patterns is a skill published in the GitHub repository aiskillstore/marketplace (416 stars, last pushed 2d ago), with no licence file. It adds 40 tokens to every session and 1,016 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
playwright-cli
Automates browser interactions for testing and validating your own web applications using playwright-cli. Use when you need terminal-first browser control for navigation, form filling, screenshots, tracing, bound browser sessions, debugging, or generating Playwright test code. Only use against applications you own or…
report-helper
当用户说「深度研究 X」「深入研究 X」或要求生成某产品、公司、概念、人物、产业链、政策、趋势的深度研究/发展研究报告时触发，自动进行联网搜索和研究，产出排版后的 PDF 文档，总字数通常 1-3 万字。 作者：嘉然 | GitHub: https://github.com/Jiaranbb/report-helper.
unity-yaml-format
Inspect, explain, diff, and carefully edit Unity text-serialized files such as .unity, .prefab, .asset, and related YAML-based project files. Use when mapping class IDs and fileIDs, tracing object references, reviewing merge conflicts, or making minimal safe edits to existing UnityYAML documents.
cheat-init
让用户从零到能跑第一篇预测，全程 ≤ 5 分钟（没发过历史的）或 ≤ 10 分钟（已发过、要 import 历史的）。.
cheat-predict
为最终稿创建不可变盲预测日志，并在已有预测时追加新版本。通过 cheat-score-blind 做隔离评分，再由主窗口复核落盘。触发词包括启动预测、start prediction、打分并预测、写预测日志。.
cheat-seed
跟用户对话讨论选题——默认一次一个，用户主动给主题或经历，AI 围绕用户的输入深挖、提炼角度、写一份 draft。不是 AI 拿三个开放问题追用户，也不是一次给 5 个候选。触发词："找选题"/"我想做一条 X"/"最近有个想法"/"seed"/"启动种子"。可选 batch 模式：/cheat-seed --batch 5 走旧的 brainstorm 5 候选 + 写 5 draft 流程。.