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 skills/jmschrei/tangermeme/datanpx skills add jmschrei/tangermeme --skill datagit clone --depth 1 https://github.com/jmschrei/tangermemeWrote 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/jmschrei/tangermeme/data)<a href="https://agentmods.dev/skills/jmschrei/tangermeme/data"><img src="https://agentmods.dev/badge/skills/jmschrei/tangermeme/data.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 | $0.00127 | $0.01609 |
| Opus 5 | $0.00063 | $0.00805 |
| Sonnet 5 | $0.00025 | $0.00322 |
| Haiku 4.5 | $0.00013 | $0.00161 |
Grade A, and why
tangermeme 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 4d 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
tangermeme
tangermeme answers the "what did my genomic model learn, and what do I do with
it after training" question. It provides atomic sequence operations, batched
prediction, attribution, perturbation experiments, and sequence design — all
deliberately assumption-free (any PyTorch model, any alphabet, raw outputs
returned rather than distances).
This skill is a router. Each topic below has a detailed reference file with the exact signatures and footguns. Read the relevant reference file before writing code — do not rely on memory of the API, because several functions have non-obvious defaults (silent wrong-output selection, variable-length returns, reproducibility traps).
Two cross-cutting concepts (read these first if unsure)
-
The
func=plug-point —ablate,marginalize,space,variant_effect.*, andproduct.*(where it is the first positional argument) all acceptfunc(model, X, args=, **kwargs). Swappingpredictfordeep_lift_shapturns a "predictions before/after" experiment into an "attributions before/after" one. Covers theadditional_func_kwargscollision trap. This is what makes the library compose. -
Wrapping models — tangermeme assumes
y = model(X)returns a single tensor with layout(batch, channels, length). Real multi-input / multi-output models must be wrapped first. Read this before attribution or design on any non-trivial model. Data preprocessing or output post-processing should be handled in custom wrappers rather than in custom functions.
Task → reference file
| If the task is… | Read |
|---|---|
| starting from scratch — set up a notebook to load a model and run predictions → attributions → seqlets → motif tests, end to end | references/notebook-walkthrough.md |
| attribution via DeepLIFT/SHAP — "which bases drive this prediction", attribution logos, hypothetical contributions for CWMs | references/deep_lift_shap.md |
| attribution via ISM / saturation mutagenesis — the forward-pass alternative; use it when DeepLIFT/SHAP convergence deltas are too high, an op can't be registered, or the model is massively multi-task | references/saturation_mutagenesis.md |
| comparing predictions/attributions across N models (replicates, architectures, ensembles) | references/comparing-models.md |
| effect of a motif / region: marginalize, ablate, spacing between motifs | references/motif-effects.md |
| scoring variant effects (substitution / deletion / insertion, from a VCF) | references/variant-effect.md |
| calling seqlets from attributions (recursive / TF-MoDISco) | references/seqlets.md |
| annotating / counting motifs — TOMTOM/FIMO labels, co-occurrence, spacing | references/annotate.md |
| running a function over a product of inputs (sequence × cell-state × …) | references/product.md |
| plotting logos and drawing seqlet/motif annotations on them | references/plot.md |
| composing predict / deep_lift_shap / saturation_mutagenesis through a perturbation fn | references/func-pattern.md |
| adapting a multi-input/output PyTorch model to the tangermeme contract | references/model-wrapping.md |
| loading sequences/signals at loci, reading FASTA/bigWig/BED/MEME/VCF | references/io-loci.md |
| designing sequences to hit a target output (screen / greedy / beam substitution) | references/design.md |
What ships with it
14 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.
- references/annotate.md 5.6 KB
- references/comparing-models.md 4.5 KB
- references/deep_lift_shap.md 7.5 KB
- references/design.md 8.4 KB
- references/func-pattern.md 4.5 KB
- references/io-loci.md 4.4 KB
- references/model-wrapping.md 4.5 KB
- references/motif-effects.md 5.8 KB
- references/notebook-walkthrough.md 6.4 KB
- references/plot.md 4.8 KB
- references/product.md 3.1 KB
- references/saturation_mutagenesis.md 5.4 KB
- references/seqlets.md 3.5 KB
- references/variant-effect.md 4.6 KB
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.
- 4d ago First seen · 90 lines · 127 tokens per session scan A e8b7311cee6b
tangermeme is a skill published in the GitHub repository jmschrei/tangermeme (309 stars, last pushed 20d ago), licensed MIT. It adds 127 tokens to every session and 1,609 once invoked, about $0.0006 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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