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 ericrisco/rsc-harness --skill ollamagit clone --depth 1 https://github.com/ericrisco/rsc-harnessWrote 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/ericrisco/rsc-harness/ollama)<a href="https://agentmods.dev/skills/ericrisco/rsc-harness/ollama"><img src="https://agentmods.dev/badge/skills/ericrisco/rsc-harness/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/ericrisco/rsc-harness/ollama"><img src="https://agentmods.dev/badge/skills/ericrisco/rsc-harness/ollama.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 Data Exfiltration · line 99 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00093 | $0.03264 |
| Opus 5 | $0.00046 | $0.01632 |
| Sonnet 5 | $0.00019 | $0.00653 |
| Haiku 4.5 | $0.00009 | $0.00326 |
Grade B, and why
ollama scanned grade B with 2 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 5d 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.
Sends data to an external URLmediumData exfiltration
A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.
curl http://localhost:11434/api/chat -d '{ Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl http://localhost:11434/api/chat -d '{ How it starts
The opening of the file, as written. The whole thing — 214 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ollama — run open-weight LLMs on one box
Ollama serves GGUF models from a local daemon at http://localhost:11434, exposing both a native
HTTP API and an OpenAI-compatible layer. Your job: reach for the right command, the right endpoint,
and the right quant for the hardware in front of you — and recognize when the model does not fit
and the work belongs on a remote GPU instead.
This skill owns: install/serve, pull/tag, the local API (native + OpenAI-compat), Modelfiles, quantization choice, and VRAM/RAM sizing on a single machine.
When to use / when not
Use when the model runs on this machine: pulling/running a model, fixing an OOM, choosing
Q4 vs Q8, authoring a Modelfile, or wiring an app to localhost:11434.
Go elsewhere when:
- Hosting behind a managed/remote GPU, autoscaling, or serverless inference →
runpod,modal,replicate,together-fireworks,fal. Ollama is local, single-box, no autoscale. - Downloading raw weights, datasets,
hf/transformers, repo management →huggingface. - Designing chunking / retrieval / reranking around a model →
ragorembeddings-search. - Orchestrating multi-step calls, routing, pipeline evals →
llm-pipeline/agent-eval. - Writing the prompt/system-message content itself →
prompt-engineering.
(Those siblings live in the catalog by id; link them only once their SKILL.md exists on disk.)
Quickstart
ollama serve # start the daemon (a desktop install already runs it)
ollama pull qwen3:8b # download a model + tag; :8b is explicit — avoid bare :latest
ollama run qwen3:8b # interactive REPL, or: ollama run qwen3:8b "summarize this"
ollama ps # what is LOADED in VRAM right now + when it unloads (keep_alive)
ollama list # what is on disk (pulled), not what is loaded
ollama show qwen3:8b # template, params, context length, quant of a model
ollama rm qwen3:8b # free disk; ollama stop qwen3:8b unloads from memory
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.
- 5d ago First seen · 214 lines · 93 tokens per session scan B af17c97098d0
ollama is a skill published in the GitHub repository ericrisco/rsc-harness (74 stars, last pushed 2d ago), licensed MIT. It adds 93 tokens to every session and 3,264 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it B with 2 findings (sends data to an external url, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
attach-receipts
Attaches sourcedoc/extractionrunid/schemaversion receipts to every edge at write time, and refuses to write any edge missing one of the three.
supersede-claim
Corrects a wrong claim by adding a new node holding the correction and a supersedes edge back to the old node, which stays in the graph flagged stale rather than being edited or deleted.
gguf-quantization
GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.
local-model-selection
Use this skill when the user asks which local model to run, which quant to pick, which uncensored variant to use, how to pair models across GPUs, or what auxiliary model to use alongside their main model.
local-model-selection
Use this skill when the user asks which local model to run, which quant to pick, which uncensored variant to use, how to pair models across GPUs, or what auxiliary model to use alongside their main model.
gguf-quantization
GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.