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.
git 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/plugins/curiositech/some_claude_skills/llm-streaming-response-handler)<a href="https://agentmods.dev/plugins/curiositech/some_claude_skills/llm-streaming-response-handler"><img src="https://agentmods.dev/badge/plugins/curiositech/some_claude_skills/llm-streaming-response-handler.svg" alt="Measured on agentmods" height="20"></a>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 yesterday.
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.
What it actually says
{
"name": "llm-streaming-response-handler",
"description": "Build production LLM streaming UIs with Server-Sent Events, real-time token display, cancellation, error recovery. Handles OpenAI/Anthropic/Claude streaming APIs. Use for chatbots, AI assistants, real-time text generation. Activate on \"LLM streaming\", \"SSE\", \"token stream\", \"chat UI\", \"real-time AI\". NOT for batch processing, non-streaming APIs, or WebSocket bidirectional chat."
}
What it installs
The manifest is a name and a version. 1 skill travel with it, and installing the plugin installs all of them — 95 tokens a session between them. Each is measured on its own page, and each can be installed alone.
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.
- yesterday First seen · 5 lines scan A 68f7c4bbd289
llm-streaming-response-handler is a plugin published in the GitHub repository curiositech/some_claude_skills (211 stars, last pushed yesterday), licensed MIT. Its token cost is not measured: this kind of file is read by the harness, not the model. 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 2026-09-06.
Other plugins, from other repositories
data-engineering
ETL pipeline construction, data warehouse design, batch processing workflows, and data-driven feature development.
data-validation-suite
Schema validation, data quality monitoring, streaming validation pipelines, and input validation for backend APIs.
fuse-laravel
Expert Laravel 13 + PHP 8.3+ with SOLID principles, first-class PHP Attributes, Laravel AI SDK, JSON:API Resources, native vector search (pgvector), Eloquent, Livewire, queues with routing, and comprehensive documentation.
sap-hana-cloud-data-intelligence
Develops data processing pipelines, integrations, and machine learning scenarios in SAP Data Intelligence Cloud. Use when building graphs/pipelines with operators, integrating ABAP/S4HANA systems, creating replication flows, developing ML scenarios with JupyterLab, or using Data Transformation Language functions.…
powabase
Build on Powabase — the AI Backend-as-a-Service (RAG, agents, orchestration, workflows) plus a Supabase-style BaaS layer. Bundles the powabase agent skill under skills/.
axon
Self-hosted RAG engine in Rust: crawl, scrape, ingest, embed, and query any source, with hybrid retrieval and cited LLM synthesis over MCP, CLI, and REST.