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 hang-in/tunaLlama --skill delegate-to-ollamagit clone --depth 1 https://github.com/hang-in/tunaLlamaWrote 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/hang-in/tunallama/delegate-to-ollama)<a href="https://agentmods.dev/skills/hang-in/tunallama/delegate-to-ollama"><img src="https://agentmods.dev/badge/skills/hang-in/tunallama/delegate-to-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/hang-in/tunallama/delegate-to-ollama"><img src="https://agentmods.dev/badge/skills/hang-in/tunallama/delegate-to-ollama.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00087 | $0.00918 |
| Opus 5 | $0.00044 | $0.00459 |
| Sonnet 5 | $0.00017 | $0.00184 |
| Haiku 4.5 | $0.00009 | $0.00092 |
Grade A, and why
delegate-to-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 11d 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 — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
When to use tunaLlama tools
tuna_* MCP tools delegate coding work to a local/cloud LLM. This is the
Opus-with-Sonnet-subagent pattern: a smaller model handles the actual code
generation, while you stay the architect.
The local LLM's window is smaller than yours, so the architect's job before delegation is fetching the context the subagent lacks. Use them when:
-
The user asks for code generation and you have clear requirements. Use
tuna_generate_codeinstead of generating the code yourself. -
The user asks to review or analyze a file. Use
tuna_review_file(passing the path) instead of reading the file first. The file content stays out of your context — major token savings. -
The user asks for refactoring or test writing with a defined scope. Use
tuna_refactor_codeortuna_write_tests. -
The user asks a question about multiple files. Use
tuna_analyze_filesso file contents bypass your context.
When NOT to delegate
- Tasks requiring deep judgment about architecture or design — keep these yourself.
- Short snippets (< ~10 lines) — overhead exceeds savings.
- Tasks that require knowledge of recent conversation context the local LLM does not have.
- Anything safety-critical or involving the user's intent interpretation.
Standard pattern: context-fetch then delegate then verify
- Context-fetch (architect's responsibility for the smaller subagent):
- If the task is non-trivial, call
tuna_recallto surface relevant past work in this project. The local LLM doesn't share your conversation context - relevant snippets help it avoid reinventing or contradicting. - Load project rules: the MCP resource
tunallama://memory/stateshould auto-attach. If not visible, calltuna_load_memoryonce per session.
- If the task is non-trivial, call
- Decompose the user's request into clear instructions, including the fetched context that the local LLM lacks.
- Call the appropriate
tuna_*tool. - Verify the returned output - catch obvious problems (wrong API, missing edge cases, divergence from project conventions in state.md).
- If wrong, call
tuna_fix_codewith a specific error description. - Present the verified result to the user.
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.
- 11d ago First seen · 80 lines · 87 tokens per session scan A 7168c77804d2
delegate-to-ollama is a skill published in the GitHub repository hang-in/tunaLlama (44 stars, last pushed 2mo ago), licensed MIT. It adds 87 tokens to every session and 918 once invoked, about $0.0004 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.
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