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 curiositech/some_claude_skills --skill llm-streaming-response-handlergit clone --depth 1 https://github.com/curiositech/some_claude_skillsWrote 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/curiositech/some_claude_skills/llm-streaming-response-handler)<a href="https://agentmods.dev/skills/curiositech/some_claude_skills/llm-streaming-response-handler"><img src="https://agentmods.dev/badge/skills/curiositech/some_claude_skills/llm-streaming-response-handler/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/curiositech/some_claude_skills/llm-streaming-response-handler"><img src="https://agentmods.dev/badge/skills/curiositech/some_claude_skills/llm-streaming-response-handler.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 | $0.00095 | $0.03289 |
| Opus 5 | $0.00048 | $0.01644 |
| Sonnet 5 | $0.00019 | $0.00658 |
| Haiku 4.5 | $0.00010 | $0.00329 |
Grade A, and why
llm-streaming-response-handler 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 5d 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 — 533 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LLM Streaming Response Handler
Expert in building production-grade streaming interfaces for LLM responses that feel instant and responsive.
When to Use
✅ Use for:
- Chat interfaces with typing animation
- Real-time AI assistants
- Code generation with live preview
- Document summarization with progressive display
- Any UI where users expect immediate feedback from LLMs
❌ NOT for:
- Batch document processing (no user watching)
- APIs that don't support streaming
- WebSocket-based bidirectional chat (use Socket.IO)
- Simple request/response (fetch is fine)
Quick Decision Tree
Does your LLM interaction:
├── Need immediate visual feedback? → Streaming
├── Display long-form content (>100 words)? → Streaming
├── User expects typewriter effect? → Streaming
├── Short response (<50 words)? → Regular fetch
└── Background processing? → Regular fetch
Technology Selection
Server-Sent Events (SSE) - Recommended
Why SSE over WebSockets for LLM streaming:
- Simplicity: HTTP-based, works with existing infrastructure
- Auto-reconnect: Built-in reconnection logic
- Firewall-friendly: Easier than WebSockets through proxies
- One-way perfect: LLMs only stream server → client
Timeline:
- 2015-2020: WebSockets for everything
- 2020: SSE adoption for streaming APIs
- 2023+: SSE standard for LLM streaming (OpenAI, Anthropic)
- 2024: Vercel AI SDK popularizes SSE patterns
Streaming APIs
| Provider | Streaming Method | Response Format |
|---|---|---|
| OpenAI | SSE | data: {"choices":[{"delta":{"content":"token"}}]} |
| Anthropic | SSE | data: {"type":"content_block_delta","delta":{"text":"token"}} |
| Claude (API) | SSE | data: {"delta":{"text":"token"}} |
| Vercel AI SDK | SSE | Normalized across providers |
Common Anti-Patterns
Anti-Pattern 1: Buffering Before Display
Novice thinking: "Collect all tokens, then show complete response"
Problem: Defeats the entire purpose of streaming.
What ships with it
6 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.
- 5d ago First seen · 533 lines · 95 tokens per session scan A 6085d5b7ad7a
llm-streaming-response-handler is a skill published in the GitHub repository curiositech/some_claude_skills (216 stars, last pushed 3d ago), licensed MIT. It adds 95 tokens to every session and 3,289 once invoked, about $0.0005 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 2026-09-03.
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