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 Buzzmatic/awesome-agent-skills --skill openroutergit clone --depth 1 https://github.com/Buzzmatic/awesome-agent-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/buzzmatic/awesome-agent-skills/openrouter)<a href="https://agentmods.dev/skills/buzzmatic/awesome-agent-skills/openrouter"><img src="https://agentmods.dev/badge/skills/buzzmatic/awesome-agent-skills/openrouter/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/buzzmatic/awesome-agent-skills/openrouter"><img src="https://agentmods.dev/badge/skills/buzzmatic/awesome-agent-skills/openrouter.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00097 | $0.01226 |
| Opus 5.5 | $0.00039 | $0.00490 |
| Sonnet 5.5 | $0.00019 | $0.00245 |
| Haiku 4.5 | $0.00010 | $0.00123 |
Grade A, and why
openrouter 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 — 157 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OpenRouter API Skill
Access hundreds of AI models through a single unified API.
Quick Start
All scripts:
- Accept an optional
--output-dirparameter (default:output/) - Save raw JSON and processed data to that directory
- Return JSON with
{"output_files": [...], "summary": "..."}
Environment Setup
Set the required environment variable:
export OPENROUTER_API_KEY="sk-or-..."
Core Operations
Chat Completions (Single Request)
Send a single chat completion request:
python skills/openrouter/scripts/chat.py \
--model "anthropic/claude-3-opus" \
--messages '[{"role":"user","content":"Summarize the benefits of SEO"}]' \
--max-tokens 500 \
--temperature 0.7
With system prompt:
python skills/openrouter/scripts/chat.py \
--model "openai/gpt-4" \
--messages '[{"role":"system","content":"You are an SEO expert"},{"role":"user","content":"Explain keyword clustering"}]' \
--max-tokens 1000
Chat Completions (Bulk Mode)
Process multiple prompts from a JSONL file:
python skills/openrouter/scripts/chat.py \
--model "openai/gpt-4-turbo" \
--messages-file data/processed/prompts.jsonl \
--max-tokens 500 \
--batch-size 10 \
--rate-limit 60
JSONL format (one request per line):
{"messages":[{"role":"user","content":"Analyze keyword: seo tools"}],"id":"kw_001"}
{"messages":[{"role":"user","content":"Analyze keyword: backlink checker"}],"id":"kw_002"}
With tools/function calling:
python skills/openrouter/scripts/chat.py \
--model "openai/gpt-4-turbo" \
--messages '[{"role":"user","content":"Get weather for Berlin"}]' \
--tools-file tools.json \
--tool-choice auto
With structured JSON output:
python skills/openrouter/scripts/chat.py \
--model "openai/gpt-4-turbo" \
--messages '[{"role":"user","content":"Extract entities from: Apple released iPhone 15"}]' \
--response-format '{"type":"json_schema","json_schema":{"name":"entities","schema":{"type":"object","properties":{"entities":{"type":"array"}}}}}'
What ships with it
5 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 · 157 lines · 97 tokens per session scan A 20b7e2294b6b
openrouter is a skill published in the GitHub repository Buzzmatic/awesome-agent-skills (1 stars, last pushed 4mo ago), licensed MIT. It adds 97 tokens to every session and 1,226 once invoked, about $0.0004 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 2026-08-31.
Other skills, from other repositories
agent-platform-model-registry
Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.
foundry-config-setup
Resolve missing setup caused by a hardcoded Foundry project endpoint or model in a sample. Use when a sample fails because it uses a placeholder/hardcoded projectendpoint (for example "https://your-project.services.ai.azure.com") or a hardcoded model instead of reading them from the environment.
google-cloud-solution-agentic-analytics-spark-knowledge-catalog
Discovers requirements and designs an end-to-end governed agentic analytics solution using Knowledge Catalog and Managed Service for Apache Spark (Lightning Engine). Use when designing data science and analytics workflows across structured and unstructured distributed data (including in S3, Azure Blob, AlloyDB, and…
training-check
Interactively monitor training metrics from the current Codex session, periodically checking WandB or fallback logs for NaN, divergence, plateaus, and broken runs.
nemo-automodel-launcher-config
Configure NeMo AutoModel job launches for interactive runs, Slurm clusters, and SkyPilot cloud execution.
finetuning
Fine-tune models on Microsoft Foundry using SFT (supervised), DPO (preference), or RFT (reinforcement with graders). Covers dataset preparation, training job submission, deployment, and evaluation. USE FOR: fine-tune, SFT, DPO, RFT, training data, grader, distillation, fine-tuned model, training job, large file…