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/st1page/agent-knowledge-framework/textual-async-data-loadingnpx skills add st1page/agent-knowledge-framework --skill textual-async-data-loadinggit clone --depth 1 https://github.com/st1page/agent-knowledge-frameworkWhat 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 | $0.00056 | $0.00941 |
| Opus 5 | $0.00028 | $0.00470 |
| Sonnet 5 | $0.00011 | $0.00188 |
| Haiku 4.5 | $0.00006 | $0.00094 |
Grade A, and why
textual-async-data-loading scanned grade A with 2 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 3d 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.
result = fetch(run_id) Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
result = subprocess.run(...) # 在后台线程执行 How it starts
The opening of the file, as written. The whole thing — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
textual TUI 异步数据加载模式
目标:在 textual TUI 中执行阻塞 IO(subprocess、HTTP)时,保持 UI 响应,同时避免竞态和重复请求。
核心三件套
1. @work(thread=True) — 阻塞 IO 移出 UI 线程
from textual import work # 注意:不是 from textual.work import work
@work(thread=True)
def _fetch_data(self) -> None:
result = subprocess.run(...) # 在后台线程执行
self.app.call_from_thread(self._render, result) # 回主线程更新 UI
陷阱:from textual.work import work 会 ModuleNotFoundError。
2. LoadingIndicator — 加载过渡
compose 时同时 yield 数据 widget 和 loading indicator,用 display 属性互斥切换:
def compose(self) -> ComposeResult:
yield LoadingIndicator(id="loading")
yield DataTable(id="table", cursor_type="row")
def on_mount(self) -> None:
self.query_one("#table").display = False # 初始隐藏数据
def _render(self, data) -> None:
self.query_one("#loading").display = False
self.query_one("#table").display = True
# ... 填充数据
3. call_from_thread — worker 回调回主线程
worker 线程中不能直接操作 UI widget。必须通过 self.app.call_from_thread(callback, *args) 调度回主线程。
增强模式
4. exclusive worker group — 高频触发防抖
光标快速移动时,每次触发数据请求。用 exclusive=True + group 自动取消旧请求:
@work(thread=True, exclusive=True, group="jobs")
def _fetch_jobs(self, run_id: int) -> None:
...
效果:连续触发只执行最后一个请求。
5. dict 缓存 + stale check
简单 dict 缓存已请求过的数据;异步回调时检查数据是否仍然是当前需要的:
_cache: dict[int, Data] = {}
@work(thread=True, exclusive=True, group="jobs")
def _fetch_jobs(self, run_id: int) -> None:
if run_id in self._cache:
self.app.call_from_thread(self._render_jobs, run_id, self._cache[run_id])
return
result = fetch(run_id)
self._cache[run_id] = result
# stale check:渲染前确认用户没有切走
if self._selected_run_id == run_id:
self.app.call_from_thread(self._render_jobs, run_id, result)
刷新时清空整个 cache(CLI 工具生命周期短,不需要 TTL)。
headless 测试中等待 worker
pilot.pause() 只处理事件队列,不等 worker 线程。必须轮询:
async def wait_workers(app, pilot, timeout=15):
import asyncio
for _ in range(int(timeout / 0.1)):
await pilot.pause()
if all(w.is_finished for w in app.workers):
return
await asyncio.sleep(0.1)
raise TimeoutError("workers not finished")
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.
- 3d ago First seen · 114 lines · 56 tokens per session scan A b934e60ebc06
textual-async-data-loading is a skill published in the GitHub repository st1page/agent-knowledge-framework (41 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 56 tokens to every session and 941 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 2 findings (makes network calls, runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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