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 agentsope/SkillAlchemy --skill agentsop-streaming-outputgit clone --depth 1 https://github.com/agentsope/SkillAlchemyWrote 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/agentsope/skillalchemy/agentsop-streaming-output)<a href="https://agentmods.dev/skills/agentsope/skillalchemy/agentsop-streaming-output"><img src="https://agentmods.dev/badge/skills/agentsope/skillalchemy/agentsop-streaming-output/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/agentsope/skillalchemy/agentsop-streaming-output"><img src="https://agentmods.dev/badge/skills/agentsope/skillalchemy/agentsop-streaming-output.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 | $0.00140 | $0.05638 |
| Opus 5.5 | $0.00056 | $0.02255 |
| Sonnet 5.5 | $0.00028 | $0.01128 |
| Haiku 4.5 | $0.00014 | $0.00564 |
Grade A, and why
agentsop-streaming-output 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 — 378 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Streaming Tool/Agent Output · SOP (Enhancement Overlay)
Source posture: every non-trivial claim is cited inline. Short tags like
[lg/stream],[lc/astream-events],[oai/stream],[anthropic/stream],[mdn/sse]resolve againstreferences/R1-source-evidence.md.This is an ENHANCE overlay: it sits on top of
[[agentsop-langgraph]](which names the four stream modes but treats streaming as one of ten operations) and[[langchain]]. Read those for the orchestration; read this for the streaming SOP. Cross-link:[[agentsop-langgraph]]OP-8.
何时激活 (Activation Rules)
Activate when any of these fire:
- The run is long (multi-second to multi-minute agent loop, RAG over many docs, multi-tool chain) and the user is waiting — perceived latency, not total latency, is the product metric.
- The user asks to "stream the response", "show a typing effect", "show progress", "show which tool the agent is running", or "show the chain of thought".
- You are building a chat surface (stream final tokens) OR an agent surface (stream intermediate steps: node entered, tool called, partial state) OR a long task surface (stream custom progress like "embedded 40/200 docs").
- You must pick a transport: Server-Sent Events (SSE) vs WebSocket vs plain chunked HTTP, and handle client disconnect / cancellation cleanly.
- You're wiring
graph.stream(...)/astream_events/ OpenAIstream=True/ Anthropicclient.messages.streamand need to know which mode and what to forward to the client.
Do not activate for: a single fast (<1s) completion, a batch/offline job with no waiting human, or a pure front-end animation question (that's CSS, not a backend SOP). Streaming a 300ms call adds protocol overhead for zero UX gain — see 反模式.
核心心智模型 (Core Mental Model)
Stream what the user needs to see, not everything the engine emits. A backend stream is a curated projection of the run's internal event firehose onto exactly three audiences:
What ships with it
3 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 · 378 lines · 140 tokens per session scan A fabf39592307
agentsop-streaming-output is a skill published in the GitHub repository agentsope/SkillAlchemy (445 stars, last pushed 14d ago), licensed MIT. It adds 140 tokens to every session and 5,638 once invoked, about $0.0006 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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