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 ashish7802/awesome-api-skills --skill ollamagit clone --depth 1 https://github.com/ashish7802/awesome-api-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/ashish7802/awesome-api-skills/ollama)<a href="https://agentmods.dev/skills/ashish7802/awesome-api-skills/ollama"><img src="https://agentmods.dev/badge/skills/ashish7802/awesome-api-skills/ollama/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/ashish7802/awesome-api-skills/ollama"><img src="https://agentmods.dev/badge/skills/ashish7802/awesome-api-skills/ollama.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 | $0.00000 | $0.00703 |
| Opus 5 | $0.00000 | $0.00351 |
| Sonnet 5 | $0.00000 | $0.00141 |
| Haiku 4.5 | $0.00000 | $0.00070 |
Grade A, and why
ollama 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ollama Skill
Get up and running with large language models locally.
Ecosystem Graph Preview
graph LR
ollama["ollama"]:::core
classDef core fill:#f9f,stroke:#333,stroke-width:4px;
ollama -- "alternative to" --> vllm
ollama -- "integrates with" --> langchain
ollama -- "alternative to" --> openai
vllm -- "alternative to" --> ollama
Recommended Next Skills
- vllm (Score: 0.92) Why: Direct relationship, Both are AI, Shared ecosystem (ai), Can deploy to docker, Similar network profile
- langchain (Score: 0.88) Why: Direct relationship, Both are AI, Shared ecosystem (ai), Similar network profile, Logical next step
- openai (Score: 0.76) Why: Direct relationship, Both are AI, Similar network profile, Logical next step
Quick Start
Ollama bundles model weights, configuration, and data into a single package. It exposes a local REST API that perfectly mimics the OpenAI API, making local AI drop-in compatible with existing tooling.
ollama run llama3
Production Patterns
Model Customization (Modelfiles)
Do not rely on system prompts passed via the API for complex, repetitive behaviors. Create a Modelfile to bake the system prompt, parameters (temperature), and custom logic into a new, specialized local model.
Architecture & Scaling
CPU vs GPU
Ollama automatically detects Apple Silicon, NVIDIA, and AMD GPUs. If VRAM is insufficient, it dynamically offloads layers to system RAM and the CPU, allowing massive models to run (albeit slower) on consumer hardware.
Error Recovery
If the Ollama daemon consumes too much VRAM and refuses to unload a model, simply restart the Ollama service. Models are cached in memory for 5 minutes by default after the last request.
Security Notes
By default, the Ollama API binds to 127.0.0.1. If you expose it to a local network (OLLAMA_HOST=0.0.0.0), beware that there is absolutely zero built-in authentication.
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.
- 10d ago First seen · 70 lines · 0 tokens per session scan A 10c4a2d84758
ollama is a skill published in the GitHub repository ashish7802/awesome-api-skills (13 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 703 tokens. 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
model-cost-compare
Trigger when the user asks which model to use, wants to compare model costs, says "what's cheapest for this task", "should I use Opus or Sonnet", "can a smaller model handle this", or "/model-cost-compare". Estimates token cost across Opus 4.6, Sonnet 4.6, GLM-5.1, Minimax M2.7, and local Gemma 4, then recommends the…
ai-ml-development
AI and machine learning development with PyTorch, TensorFlow, and LLM integration. Use when building ML models, training pipelines, fine-tuning LLMs, or implementing AI features.
composio
Build AI agents and apps with Composio - access 200+ external tools with Tool Router or direct execution.
ai-policy-generator
AI governance policy creation for nonprofits and enterprises with frameworks, risk assessment, ethical guidelines, and compliance templates. Use when drafting AI usage policies, responsible AI frameworks, or organizational AI governance documents.
data-science
Data science and analytics expertise for statistical analysis, machine learning pipelines, data governance, business intelligence, predictive modeling, and analytics strategy. Use when building ML models, analyzing data, creating dashboards, or designing data architectures.
data-engineering
ETL/ELT pipelines, data warehousing (BigQuery, Snowflake, Redshift), stream processing (Kafka, Spark Streaming), orchestration (Airflow, Dagster, Prefect), dbt transformations, and data lake architecture. Use when building data pipelines, designing warehouse schemas, or implementing real-time data processing.