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 agentlas-ai/agentlas-desktop --skill tamarindgit clone --depth 1 https://github.com/agentlas-ai/agentlas-desktopWrote 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/agentlas-ai/agentlas-desktop/tamarind)<a href="https://agentmods.dev/skills/agentlas-ai/agentlas-desktop/tamarind"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/tamarind/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/agentlas-ai/agentlas-desktop/tamarind"><img src="https://agentmods.dev/badge/skills/agentlas-ai/agentlas-desktop/tamarind.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 | $0.00172 | $0.06417 |
| Opus 5.5 | $0.00069 | $0.02567 |
| Sonnet 5.5 | $0.00034 | $0.01283 |
| Haiku 4.5 | $0.00017 | $0.00642 |
Grade A, and why
tamarind scanned grade A with 1 finding 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 1mo 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl https://app.tamarind.bio/api/tools \ This is a copy
100% identical to tamarind — 395 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 — 286 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tamarind Bio
Tamarind Bio is a cloud platform that runs computational biology tools — structure prediction, protein and antibody design, docking, binding-affinity, MSA generation, and molecular dynamics — on managed GPUs. Users submit sequences or structures and get back predicted structures, designs, and biophysical scores, without provisioning their own hardware. It exposes hundreds of tools (AlphaFold, Boltz-2, Chai-1, RFdiffusion, ProteinMPNN, BoltzGen, ESMFold2, DiffDock, Autodock Vina, and many more) through one uniform job API.
Official docs: app.tamarind.bio/api-docs · platform UI at app.tamarind.bio
Canonical sources — fetch these, don't rely on a stale copy
Tamarind publishes live, machine-readable sources. Prefer fetching them at runtime over trusting any hardcoded list — tool names, schemas, and endpoints change frequently:
https://app.tamarind.bio/llms.txt— LLM index: links to the spec, API docs, and MCP guide.https://app.tamarind.bio/openapi.yaml— OpenAPI 3.0 spec for the 8 core job endpoints (submit-job/-batch, jobs, result, upload, files, delete-job/-file; authApiKeyAuth). Fetch it for those exact shapes. Discovery/management endpoints (/tools,/usage-statistics, pipelines, …) aren't in it — use the MCP/REST discovery tools for those.https://docs.tamarind.bio/llms.txt— documentation index; every page has a.mdform (e.g.docs.tamarind.bio/tamarind/batch.md,/tamarind/api.md,/tamarind/pipelines.md).- Live tool discovery —
GET /tools(REST) or MCPgetAvailableTools+getJobSchema(jobType)are the source of truth for what tools exist and their parameters.
This skill teaches the surface + the non-obvious behaviors those sources don't spell out (see the reference files). When in doubt about a shape, fetch openapi.yaml.
When to use this skill
Use Tamarind when the user wants to:
- Predict structure of a protein, complex, or protein-ligand system (AlphaFold, Boltz-2, Chai-1, ESMFold2, Chai/Boltz cofolding)
- Design proteins or binders (RFdiffusion, BoltzGen, BindCraft, ProteinMPNN/LigandMPNN inverse folding)
- Design or characterize antibodies/nanobodies (sequence generation, humanization, developability, immunogenicity)
- Dock small molecules to a protein (DiffDock, Autodock Vina) or predict binding affinity
- Generate MSAs for downstream folding
- Run molecular dynamics or other biophysical workflows on managed GPUs
- Batch-screen many sequences or designs through the same tool
- Chain tools into pipelines (e.g. design → fold → score) using the output of one job as the input of the next
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 286 lines · 172 tokens per session scan A 05332a8580d1
tamarind is a skill published in the GitHub repository agentlas-ai/agentlas-desktop (6 stars, last pushed yesterday), licensed Apache-2.0. It adds 172 tokens to every session and 6,417 once invoked, about $0.0007 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). It is 100% identical to tamarind, differing in 395 lines, and is treated as a copy.
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