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/hamr0/agentic-toolkit/condition-based-waitingnpx skills add hamr0/agentic-toolkit --skill condition-based-waitinggit clone --depth 1 https://github.com/hamr0/agentic-toolkitWhat 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 | $0.00043 | $0.00909 |
| Opus 5 | $0.00022 | $0.00454 |
| Sonnet 5 | $0.00009 | $0.00182 |
| Haiku 4.5 | $0.00004 | $0.00091 |
Grade A, and why
condition-based-waiting 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 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.
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- condition-based-waiting — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Condition-Based Waiting
Overview
Flaky tests often guess at timing with arbitrary delays. This creates race conditions where tests pass on fast machines but fail under load or in CI.
Core principle: Wait for the actual condition you care about, not a guess about how long it takes.
When to Use
digraph when_to_use {
"Test uses setTimeout/sleep?" [shape=diamond];
"Testing timing behavior?" [shape=diamond];
"Document WHY timeout needed" [shape=box];
"Use condition-based waiting" [shape=box];
"Test uses setTimeout/sleep?" -> "Testing timing behavior?" [label="yes"];
"Testing timing behavior?" -> "Document WHY timeout needed" [label="yes"];
"Testing timing behavior?" -> "Use condition-based waiting" [label="no"];
}
Use when:
- Tests have arbitrary delays (
setTimeout,sleep,time.sleep()) - Tests are flaky (pass sometimes, fail under load)
- Tests timeout when run in parallel
- Waiting for async operations to complete
Don't use when:
- Testing actual timing behavior (debounce, throttle intervals)
- Always document WHY if using arbitrary timeout
Core Pattern
// ❌ BEFORE: Guessing at timing
await new Promise(r => setTimeout(r, 50));
const result = getResult();
expect(result).toBeDefined();
// ✅ AFTER: Waiting for condition
await waitFor(() => getResult() !== undefined);
const result = getResult();
expect(result).toBeDefined();
Quick Patterns
| Scenario | Pattern |
|---|---|
| Wait for event | waitFor(() => events.find(e => e.type === 'DONE')) |
| Wait for state | waitFor(() => machine.state === 'ready') |
| Wait for count | waitFor(() => items.length >= 5) |
| Wait for file | waitFor(() => fs.existsSync(path)) |
| Complex condition | waitFor(() => obj.ready && obj.value > 10) |
Implementation
Generic polling function:
async function waitFor<T>(
condition: () => T | undefined | null | false,
description: string,
timeoutMs = 5000
): Promise<T> {
const startTime = Date.now();
while (true) {
const result = condition();
if (result) return result;
if (Date.now() - startTime > timeoutMs) {
throw new Error(`Timeout waiting for ${description} after ${timeoutMs}ms`);
}
await new Promise(r => setTimeout(r, 10)); // Poll every 10ms
}
}
What ships with it
1 file 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.
- 3d ago First seen · 121 lines · 43 tokens per session scan A 41b66e433995
condition-based-waiting is a skill published in the GitHub repository hamr0/agentic-toolkit (22 stars, last pushed 3d ago), licensed Apache-2.0. It adds 43 tokens to every session and 909 once invoked, about $0.0002 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 2026-08-30.
Other skills, from other repositories
create_profile_style_skill
【META SKILL】分析当前剪辑逻辑与风格,总结并生成一个新的可复用 Skill 文件,存入剪辑技能库。Analyze the current editing logic and style, summarize and generate a new reusable Skill file, and store it in the editing skill library.
company-product-context
Compiles comprehensive company product context from PDF documents, web research, and industry knowledge.
Paper Writing Workflow
Workflow phases for paper-writing tasks: triage, material inventory, research question, literature review, paper outline, data analysis summary, figure storyline, reader testing, and finalize packet. Each phase is a short contract — read the relevant rows for the current task only.
codebase-context-extractor
This skill provides a comprehensive context extraction system for large codebases. It intelligently analyzes code structure, dependencies, and relationships to extract relevant context for understanding, debugging, or modifying code.
deep-researcher
Performs comprehensive, multi-layered research on any topic with structured analysis and synthesis of information from multiple sources.
llmtornado-tutorial-generator
Generates comprehensive code tutorials on LlmTornado API formatted for Medium publication with examples, explanations, and best practices.