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 rse/ase --skill ase-meta-quorumgit clone --depth 1 https://github.com/rse/aseWrote 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/rse/ase/ase-meta-quorum)<a href="https://agentmods.dev/skills/rse/ase/ase-meta-quorum"><img src="https://agentmods.dev/badge/skills/rse/ase/ase-meta-quorum/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/rse/ase/ase-meta-quorum"><img src="https://agentmods.dev/badge/skills/rse/ase/ase-meta-quorum.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 | $0.00050 | $0.02325 |
| Opus 5 | $0.00025 | $0.01162 |
| Sonnet 5 | $0.00010 | $0.00465 |
| Haiku 4.5 | $0.00005 | $0.00232 |
Grade A, and why
ase-meta-quorum scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 249 lines — stays where its author put it; the contents beside it link to each section on GitHub.
@${CLAUDE_SKILL_DIR}/../../meta/ase-control.md @${CLAUDE_SKILL_DIR}/../../meta/ase-skill.md @${CLAUDE_SKILL_DIR}/../../meta/ase-getopt.md
$ARGUMENTS
-
The user-selectable foreign models are restricted by the
--models/-moption, parsed into as a comma-separated list of model tokens. The getopt parser validates only the first token, so you MUST validate each remaining token yourself against the allowed setall,chatgpt,gemini,deepseek,grok,glm,qwen,claude,codex,copilot. If any token is not in this set, bind to that offending token, then only output the following and then immediately STOP processing the entire current skill:Prepare the LLM query by setting to the following :
For yourself (Anthropic Claude), first answer this in advance yourself by showing your own answer to the query as a sneak preview. For this, output the following :
-
The default is the single token
all. If contains the tokenall, you MUST treat it as the full listchatgpt,gemini,deepseek,grok,glm,qwen(all API-based models). The harness-based modelsclaude,codex, andcopilotare never part ofalland are queried only when their token is given explicitly (in addition toall, if wished). Anthropic Claude (yourself) is always included, independent of this option.Agent( description: "Query Foreign LLM: <arg1/>", subagent_type: "ase:ase-meta-chat", prompt: "<arg2/> <query/>", run_in_background: false )Query only those foreign models whose token is contained in (where
allselects every model); silently skip all others. Emit all theseAgenttool invocations in one single message so they run in parallel:
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.
- 9d ago First seen · 249 lines · 50 tokens per session scan A 8257fa698854
ase-meta-quorum is a skill published in the GitHub repository rse/ase (47 stars, last pushed today), licensed Apache-2.0. It adds 50 tokens to every session and 2,325 once invoked, about $0.0003 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
ai-infrastructure-replicate
Replicate SDK patterns for TypeScript/Node.js -- client setup, predictions, streaming, webhooks, file handling, model versioning, deployments, and training.
ai-orchestration-langchain
LangChain.js patterns for building LLM applications — chat models, LCEL chains, prompt templates, structured output, agents, tools, RAG, streaming, and LangSmith tracing.
ai-provider-anthropic-sdk
Official Anthropic SDK patterns for TypeScript/Node.js — client setup, Messages API, streaming, tool use, vision, extended thinking, structured outputs, prompt caching, batch API, and production best practices.
ai-provider-cohere-sdk
Official Cohere TypeScript SDK patterns -- CohereClientV2, chat, embeddings, rerank, RAG with citations, tool use, streaming, and model selection.
ai-provider-google-gemini-sdk
Official TypeScript SDK for Google Gemini — client setup, text generation, multimodal input, function calling, structured output, streaming, embeddings, context caching, and chat sessions.
ai-provider-mistral-sdk
Official Mistral AI TypeScript SDK patterns — client setup, chat completions, streaming, function calling, structured outputs, embeddings, vision, Codestral FIM, and production best practices.