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 instructions/pblagoje/mcp-ollama-python/claude-mdgit clone --depth 1 https://github.com/pblagoje/mcp-ollama-pythonWrote 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/instructions/pblagoje/mcp-ollama-python/claude-md)<a href="https://agentmods.dev/instructions/pblagoje/mcp-ollama-python/claude-md"><img src="https://agentmods.dev/badge/instructions/pblagoje/mcp-ollama-python/claude-md.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 | $0.00902 | $0.00902 |
| Opus 5 | $0.00451 | $0.00451 |
| Sonnet 5 | $0.00180 | $0.00180 |
| Haiku 4.5 | $0.00090 | $0.00090 |
Grade A, and why
mcp-ollama-python CLAUDE.md 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 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.
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.
This is a copy
94% identical to mcp-ollama-python AGENTS.md — 5 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 46 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GitNexus — Code Intelligence
This project is indexed by GitNexus as mcp-ollama-python (3035 symbols, 8959 relationships, 79 execution flows). Use the GitNexus MCP tools to understand code, assess impact, and navigate safely.
Index stale? Run
node .gitnexus/run.cjs analyzefrom the project root — it auto-selects an available runner. No.gitnexus/run.cjsyet?npx gitnexus analyze(npm 11 crash →npm i -g gitnexus; #1939).
Always Do
- MUST run impact analysis before editing any symbol. Before modifying a function, class, or method, run
impact({target: "symbolName", direction: "upstream"})and report the blast radius (direct callers, affected processes, risk level) to the user. For unified PDG impact, addmode: "pdg"with optionalline: <N>— it returns statement-levelaffectedStatementsover CDG + REACHING_DEF and inter-procedural symbols ininterproceduralByDepth/byDepth; no-layer/degraded PDG results are UNKNOWN-risk notes (--pdglayer). - MUST run
detect_changes()before committing to verify your changes only affect expected symbols and execution flows. For regression review, compare against the default branch:detect_changes({scope: "compare", base_ref: "main"}). - MUST warn the user if impact analysis returns HIGH or CRITICAL risk before proceeding with edits.
- When exploring unfamiliar code, use
query({search_query: "concept"})to find execution flows instead of grepping. It returns process-grouped results ranked by relevance. - When you need full context on a specific symbol — callers, callees, which execution flows it participates in — use
context({name: "symbolName"}). - For security review,
explain({target: "fileOrSymbol"})lists taint findings (source→sink flows; needsanalyze --pdg). - For control/data dependence,
pdg_query({mode: "controls", target: "fileOrSymbol"})answers "under what condition does X run?" (CDG, incl. guard clauses) andpdg_query({mode: "flows", target, variable})traces "where does variable Y flow?" (REACHING_DEF).--pdglayer.
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 · 46 lines · 902 tokens per session scan A c860435ab53c
mcp-ollama-python CLAUDE.md is an instructions file published in the GitHub repository pblagoje/mcp-ollama-python (5 stars, last pushed 13d ago), licensed MIT. It adds 902 tokens to every session, about $0.0045 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to mcp-ollama-python AGENTS.md, differing in 5 lines, and is treated as a copy.
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