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 agentmods add commands/gopherguides/gopher-ai/ollamagit clone --depth 1 https://github.com/gopherguides/gopher-aiWrote 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/commands/gopherguides/gopher-ai/ollama)<a href="https://agentmods.dev/commands/gopherguides/gopher-ai/ollama"><img src="https://agentmods.dev/badge/commands/gopherguides/gopher-ai/ollama.svg" alt="Measured on agentmods" 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 | $0.00013 | $0.01521 |
| Opus 5 | $0.00006 | $0.00760 |
| Sonnet 5 | $0.00003 | $0.00304 |
| Haiku 4.5 | $0.00001 | $0.00152 |
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 4d 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 — 237 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Use Local Models via Ollama
If $ARGUMENTS is empty or not provided:
Display usage information and ask for input:
This command runs prompts through local models via Ollama. Your data stays on your machine.
Usage: /ollama <prompt>
Examples:
| Command | Description |
|---|---|
/ollama review this authentication code |
Code review |
/ollama explain this concurrent pattern |
Code explanation |
/ollama suggest Go idioms for this function |
Best practices |
/ollama what security issues do you see |
Security analysis |
Model choice is based on what is installed locally. When running a prompt,
first call ollama list, prefer the first installed model whose name contains
code or coder, and fall back to the first installed model. If no models are
installed, offer pull suggestions such as qwen3-coder, qwen2.5-coder, or
deepseek-coder-v2 as examples only.
Privacy Note: All processing happens locally. Your code never leaves your machine.
Ask the user: "What would you like to analyze locally?"
If $ARGUMENTS is provided:
Run a task using Ollama with the prompt: $ARGUMENTS
1. Check Prerequisites
First, check if Ollama is installed:
which ollama
If not found, inform the user:
Ollama is not installed. Install it with:
brew install ollamaOr visit: https://ollama.ai
Then ask if they want to proceed after installation or use /codex or /gemini instead.
2. Check if Ollama is Running
ollama ps 2>/dev/null
If not running or errors, offer to start it:
Ollama server is not running. Would you like me to start it?
If yes:
ollama serve &
sleep 2
3. List Available Models
ollama list
Show the user which models are already downloaded. Use the first column
(NAME) as the installed model list, excluding the header row.
4. Select Model
Build the model menu from the actual ollama list output, not from a static
table.
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.
- 4d ago First seen · 237 lines · 13 tokens per session scan A 096cb433b9e4
ollama is a command published in the GitHub repository gopherguides/gopher-ai (21 stars, last pushed today), licensed MIT. It adds 13 tokens to every session and 1,521 once invoked, about $0.0001 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 commands, from other repositories
git
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checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.