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 frumu-ai/tandem --skill bio-strategygit clone --depth 1 https://github.com/frumu-ai/tandemWrote 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/frumu-ai/tandem/bio-strategy)<a href="https://agentmods.dev/skills/frumu-ai/tandem/bio-strategy"><img src="https://agentmods.dev/badge/skills/frumu-ai/tandem/bio-strategy/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/frumu-ai/tandem/bio-strategy"><img src="https://agentmods.dev/badge/skills/frumu-ai/tandem/bio-strategy.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 | $0.00024 | $0.04473 |
| Opus 5 | $0.00012 | $0.02237 |
| Sonnet 5 | $0.00005 | $0.00895 |
| Haiku 4.5 | $0.00002 | $0.00447 |
Grade A, and why
bio-strategy 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 9d 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.
The source is not reproduced here
A licence we could not identify
The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.
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.
- 9d ago First seen · 584 lines · 24 tokens per session scan A b2ed39b6a89b
bio-strategy is a skill published in the GitHub repository frumu-ai/tandem (119 stars, last pushed 3d ago), with no licence file. It adds 24 tokens to every session and 4,473 once invoked, about $0.0001 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.
Other skills, from other repositories
sandbox-expr
Evaluate a math formula without eval in a Code node or CodeAct action, with expr-eval running on the host.
geogebra-drawing
A GeoGebra skill for creating interactive mathematical drawings and visualisations with GGBScript code.
byok-custom-model
Register a custom LLM endpoint with your own API key for chat in Starchild. Use when adding a personal Anthropic, OpenAI, Grok, Qwen, DeepSeek, Meta (Muse Spark), NEAR AI, or Venice key as a chat model (e.g. add my Claude key, register DeepSeek, use Muse Spark 1.1).
tabpfn-regress
Run a TabPFN regression baseline, generate the first submission, then optimize with GBT ensembles and regression-specific post-processing (clipping, target transforms, rank blending). Use after tabpfn-explore has prepared the data and CV folds.
tabpfn-explore
EDA, data profiling, adversarial validation, preprocessing checks, CV scheme setup, and API budget verification for tabular Kaggle competitions. Run at the start of every new competition before any modeling.
drug-discovery
Drug discovery: ChEMBL search, drug-likeness, interactions.