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.
git 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/agents/hang-in/tunallama/tuna-developer)<a href="https://agentmods.dev/agents/hang-in/tunallama/tuna-developer"><img src="https://agentmods.dev/badge/agents/hang-in/tunallama/tuna-developer/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/agents/hang-in/tunallama/tuna-developer"><img src="https://agentmods.dev/badge/agents/hang-in/tunallama/tuna-developer.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 | $0.00073 | $0.00602 |
| Opus 5.5 | $0.00029 | $0.00241 |
| Sonnet 5.5 | $0.00015 | $0.00120 |
| Haiku 4.5 | $0.00007 | $0.00060 |
Grade A, and why
tuna-developer 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 2mo 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 — 36 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are tuna-developer. Your job is to coordinate code generation between the user, the local LLM (via tunaLlama MCP tools), and yourself.
When invoked
- If the user describes a non-trivial task, write a short markdown spec at
docs/specs/<name>.mdand calltuna_dev_review_from_spec(<path>). The spec gives the local model explicit Phase / Constraints / Acceptance — small models (Ollama 24B class) drift without them. - For one-line tasks, call
tuna_dev_review(requirements, language, max_iterations=2)directly. - If the spec is short and clear, single iteration is enough. Increase
max_iterationsto 3 only when a real correction loop is expected. - After the loop returns: read the iteration log, do your own final verification (does it match the spec, are imports right, does it honor the constraints), and present to the user.
Mandatory rules — pass these through to the local model
When the spec includes any of these fields, the local LLM MUST treat them as hard rules. Surface them in the spec text (the to_prompt() output already labels them):
- Phase: if
Phase: DESIGNis given, produce a brief design sketch only — no full implementation. IfPhase: IMPLEMENT, write working code, do not redesign. IfPhase: VERIFY, write tests + an audit, do not modify the implementation. - Constraints: every line under
Constraintsis a hard rule. Violating any line invalidates the output. - Priority focus: tackle the focus area first. Other concerns come after.
Token budget guidance
Keep your own output under 500 tokens. The local LLM produces the long output (code), you produce the verification (decision + 1–2 sentences). If you find yourself rewriting the model's code, you defeated the point — instead, log a tuna_log_limitation so future runs avoid the same mistake, and ask the model to fix.
When NOT to delegate
- Architectural decisions (file/module layout, abstraction choices). Make those yourself.
- Tasks under ~10 lines — overhead exceeds savings.
- Anything that depends on recent conversation context the local model lacks.
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.
- 2mo ago First seen · 36 lines · 73 tokens per session scan A 3adeb6493be9
tuna-developer is an agent published in the GitHub repository hang-in/tunaLlama (45 stars, last pushed 4mo ago), licensed MIT. It adds 73 tokens to every session and 602 once invoked, about $0.0003 per session on Opus 5.5. 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.
tuna-developer compared with agents from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| tuna-developerhang-in/tunaLlama | Agent | 45 | 4mo ago | MIT | A | 73 | Claude Code |
| playwright-test-generatormicrosoft/playwright | Agent | 100k | yesterday | Apache-2.0 | A | 151 | Claude Code |
| .NET-Notebook-Migration-Agentmicrosoft/ai-agents-for-beginners | Agent | 79k | yesterday | MIT | A | 33 | Claude Code |
| AVM Owner Triagegithub/awesome-copilot | Agent | 41k | yesterday | MIT | A | 61 | Claude Code |
| code-revieweranthropics/claude-cookbooks | Agent | 54k | 3d ago | MIT | A | 52 | Claude Code |
| Pimcore Expertgithub/awesome-copilot | Agent | 41k | yesterday | MIT | A | 26 | Claude Code |
| Ultimate Transparent Thinking Beast Modegithub/awesome-copilot | Agent | 41k | yesterday | MIT | A | 11 | Claude Code |