Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add ashlrai/ashlr-plugin/plugin install ashlrWrote 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/ashlrai/ashlr-plugin/ashlr-ollama-setup)<a href="https://agentmods.dev/commands/ashlrai/ashlr-plugin/ashlr-ollama-setup"><img src="https://agentmods.dev/badge/commands/ashlrai/ashlr-plugin/ashlr-ollama-setup.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 5.1 | $0.00045 | $0.00476 |
| Opus 5 | $0.00023 | $0.00238 |
| Sonnet 5 | $0.00009 | $0.00095 |
| Haiku 4.5 | $0.00005 | $0.00048 |
Grade A, and why
ashlr-ollama-setup 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 6d 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.
What it actually says
Pro tip: Pro and Team users can skip Ollama entirely — the plugin auto-routes summarization to the hosted cloud LLM (xAI Grok 4.3) once
~/.ashlr/pro-tokenis present. See/ashlr-upgrade. Ollama remains the recommended path for free tier and offline use.
Run the guided Ollama setup script and relay its output.
Steps:
-
If the user asked for "automatic", "auto", "just install it", "yes", or passed a
--yes/-y/autoargument, include--yesin the command below. Otherwise omit it — the script will print the exactollama pullcommand for the user to run themselves. -
Run this via Bash:
bun run ${CLAUDE_PLUGIN_ROOT}/scripts/ollama-setup.ts [--yes]If
$CLAUDE_PLUGIN_ROOTis unset, resolve the plugin root the same way other ashlr commands do (walk up from this command file to find.claude-plugin/plugin.json). -
Relay the script's stdout verbatim inside a fenced code block. Do not paraphrase or truncate — each line carries a glyph (
✓pass,✗fail,⚠warn,ℹinfo) and anyfix:line is copy-pasteable. -
If the script exits non-zero:
1means a user-actionable issue (Ollama not installed, daemon not running, or a model needs to be pulled). The output already contains the exact next command — do not invent your own instructions.2means an internal error. Ask the user to file an issue with the output block.
-
If the script exits
0, confirm briefly that/ashlr-genome-init --summarizewill now use the recommended model. No other preamble.
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.
- 6d ago First seen · 40 lines · 45 tokens per session scan A 094510a85903
ashlr-ollama-setup is a command published in the GitHub repository ashlrai/ashlr-plugin (3 stars, last pushed 6d ago), licensed MIT. It adds 45 tokens to every session and 476 once invoked, about $0.0002 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.
Other commands, from other repositories
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mlops
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dare-llm-integration
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rag-retrieval
RAG pipeline patterns for grounded LLM responses. Use when building a Q&A system, adding citations, implementing a knowledge base, or preventing hallucinations. Triggers on RAG, retrieval augmented, knowledge base, Q&A pipeline, citations, hybrid search, context retrieval, hallucination prevention.
mindforge:data-pipeline
Design data pipeline with quality gates. Usage: /mindforge:data-pipeline [domain] [--type batch|streaming|hybrid] [--orchestrator airflow|dagster].