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 Cognigy/cognigy-plugin --skill llm-providersgit clone --depth 1 https://github.com/Cognigy/cognigy-pluginWrote 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/cognigy/cognigy-plugin/llm-providers)<a href="https://agentmods.dev/skills/cognigy/cognigy-plugin/llm-providers"><img src="https://agentmods.dev/badge/skills/cognigy/cognigy-plugin/llm-providers/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/cognigy/cognigy-plugin/llm-providers"><img src="https://agentmods.dev/badge/skills/cognigy/cognigy-plugin/llm-providers.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 78 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00078 | $0.01452 |
| Opus 5 | $0.00039 | $0.00726 |
| Sonnet 5 | $0.00016 | $0.00290 |
| Haiku 4.5 | $0.00008 | $0.00145 |
Grade A, and why
llm-providers 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 10d 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LLM Provider Reference
setup_llm parameters
| provider | modelType examples | Connection type | Notes |
|---|---|---|---|
| openAI | gpt-4o, gpt-4o-mini, gpt-4.1, gpt-4.1-mini | OpenAIProvider | Requires apiKey (sk-...) |
| anthropic | claude-sonnet-4-0, claude-opus-4-0, claude-3-opus-20240229 | AnthropicProvider | Requires apiKey |
| azureOpenAI | gpt-4o (deployment name) | AzureOpenAIProviderV2 | Requires apiKey, may need connectionId with deployment config |
| gemini-2.0-flash, gemini-1.5-pro | GoogleVertexAIProvider | Requires apiKey | |
| mistral | mistral-small-2503, mistral-medium-latest | MistralProvider | Requires apiKey |
| openAICompatible | custom-model, custom-embedding-model | OpenAICompatibleProvider | Requires apiKey + baseCustomUrl + customModel (see below) |
Model groups
setup_llm can create different kinds of Cognigy LLM resources. The important distinction is the modelType:
- Chat models: used for AI Agents, Knowledge Search, and Answer Extraction.
Examples:
gpt-4o,gpt-4o-mini,gpt-4.1,claude-sonnet-4-0,gemini-2.0-flash,mistral-small-2503. - Embedding models: used for knowledge-store vector indexing.
Examples:
text-embedding-3-small,text-embedding-3-large,text-embedding-ada-002,luminous-embedding-128,amazon.titan-embed-text-v2:0,Pharia-1-Embedding-4608,gemini-embedding-001,custom-embedding-model.
Chat/completion models are not embedding models. gpt-4o-mini is a chat model, not a valid embedding-model choice for knowledge-store indexing.
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.
- 10d ago First seen · 90 lines · 78 tokens per session scan A d365cbf9ca81
llm-providers is a skill published in the GitHub repository Cognigy/cognigy-plugin (12 stars, last pushed yesterday), licensed MIT. It adds 78 tokens to every session and 1,452 once invoked, about $0.0004 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 2026-08-31.
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