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 001TMF/blatant-why --skill by-deploy-computegit clone --depth 1 https://github.com/001TMF/blatant-whyWrote 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/001tmf/blatant-why/by-deploy-compute)<a href="https://agentmods.dev/skills/001tmf/blatant-why/by-deploy-compute"><img src="https://agentmods.dev/badge/skills/001tmf/blatant-why/by-deploy-compute/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/001tmf/blatant-why/by-deploy-compute"><img src="https://agentmods.dev/badge/skills/001tmf/blatant-why/by-deploy-compute.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 5 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 244 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
- medium Rogue Agent · line 160 Skill establishes unauthorized persistence across sessions via cron jobs, startup scripts, or state files. Session persistence allows an attacker to maintain access beyond the current interaction.Fix: Remove any persistence mechanisms (cron jobs, startup scripts, state files). Skills should not maintain state across sessions without explicit user consent.
- medium Privilege Escalation · line 204 Commands invoke sudo or root privileges. Verify this elevated access is necessary and justified.Fix: Avoid sudo/root unless strictly required. Prefer least-privilege patterns. If elevation is needed, document the justification and scope.
- medium Data Exfiltration · line 309 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00005 | $0.08069 |
| Opus 5.5 | $0.00002 | $0.03228 |
| Sonnet 5.5 | $0.00001 | $0.01614 |
| Haiku 4.5 | $0.00001 | $0.00807 |
Grade B, and why
by-deploy-compute scanned grade B with 2 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 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.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
- ❌ Do NOT use `sudo pip install` — always conda env or virtualenv Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -X POST "https://abc123-8000.proxy.runpod.net/predict" \ How it starts
The opening of the file, as written. The whole thing — 446 lines — stays where its author put it; the contents beside it link to each section on GitHub.
BY Deploy Compute Skill
BY is a local-first agent. The default compute provider is "local" (see
.by/config.json → compute.default_provider). This skill teaches you how to
stand up each design and folding tool on hardware the user owns or pays for
directly — a personal GPU workstation, a RunPod pod, a Modal app, or a
generic SLURM/PBS cluster — so campaigns run without depending on a managed
cloud provider.
Tamarind remains available as a cloud fallback when local compute is
unavailable. It is never the default; users opt in explicitly via
compute.default_provider = "tamarind".
When to Use This Skill
Use this skill when you have:
- ✅ A fresh BY workstation that needs Protenix / BoltzGen / PXDesign installed for the first time
- ✅ An existing local install missing a tool (e.g., ThermoMPNN, RFAntibody, ImmuneBuilder) that an active campaign now requires
- ✅ A RunPod account and want a per-tool deployment recipe with pod template, persistent volumes, and a working
entry_command - ✅ A Modal account and want a
modal.Imagerecipe with HuggingFace secret wiring and weight-cache volumes - ✅ An institutional HPC (SLURM or PBS) and want a portable submission script that loads CUDA modules, activates a conda env, and runs the tool
- ✅ A user migrating away from Tamarind — concrete, copy-pasteable replacement deployments for every tool they currently call
- ✅ A failed local invocation that needs to be diagnosed as a deployment issue (missing weights, wrong CUDA, OOM) vs a tool-internal bug
Do NOT use this skill when:
- ❌ You just need to run a tool that is already installed — invoke the engine skill (
protenix,boltzgen,pxdesign) directly. - ❌ You are debugging tool-internal results (low ipSAE, high RMSD) — use
by-scoringorby-failure-diagnosis. Deployment is upstream of result quality. - ❌ The user has explicitly chosen Tamarind (
compute.default_provider = "tamarind") — call the Tamarind MCP path; don't try to deploy locally first. - ❌ The campaign is already running and just needs a status check — that's
by-campaign-manageror/by:watch. - ❌ You need to allocate a campaign directory — that is
by-session/by-campaign-manager. - ❌ The question is "which tool should I use" — that is
by-design-workflow; deployment is per-tool plumbing, not strategy.
What ships with it
6 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 · 446 lines · 5 tokens per session scan B 6111808edea8
by-deploy-compute is a skill published in the GitHub repository 001TMF/blatant-why (119 stars, last pushed 9d ago), licensed MIT. It adds 5 tokens to every session and 8,069 once invoked, about $0.0000 per session on Opus 5.5. A static security scan graded it B with 2 findings (asks for root, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
statistical-modeling
Statistical modeling and machine learning for biomarker discovery, survival analysis, classification, regression, and model interpretation.
chromatin-regulation
Chromatin regulation analysis from called peaks and count matrices — differential binding, signal summarisation, peak annotation, and scATAC-seq.
genomic-variants
Analysis of called genomic variants — filtering, annotation, GWAS, and population-genetics summaries from VCF and PLINK-format data.
bulk-transcriptomics
Bulk RNA-seq and microarray differential expression analysis including method selection, batch correction, and complex experimental designs.
spatial-omics
Spatial transcriptomics and spatial proteomics analysis covering technology-specific workflows, spatial statistics, deconvolution, and niche analysis.
drug-repurposing
Systematic drug repurposing via signature matching, target-based analysis, network proximity, genetic evidence scoring, and clinical evidence mining.