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-design-workflowgit 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-design-workflow)<a href="https://agentmods.dev/skills/001tmf/blatant-why/by-design-workflow"><img src="https://agentmods.dev/badge/skills/001tmf/blatant-why/by-design-workflow/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-design-workflow"><img src="https://agentmods.dev/badge/skills/001tmf/blatant-why/by-design-workflow.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
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 →
- medium Excessive Agency · line 50 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00004 | $0.08341 |
| Opus 5.5 | $0.00002 | $0.03336 |
| Sonnet 5.5 | $0.00001 | $0.01668 |
| Haiku 4.5 | $0.00000 | $0.00834 |
Grade A, and why
by-design-workflow 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 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.
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 — 475 lines — stays where its author put it; the contents beside it link to each section on GitHub.
BY Design Workflow — Master Orchestration Skill
by-design-workflow is the routing brain of BY. It does not run any engine itself.
Instead, it answers four questions before any GPU compute is committed:
- Which engine? — Protenix (fold), PXDesign (de novo binder), or BoltzGen (antibody / nanobody / fallback binder).
- Which preset / protocol / tier? — Preview vs Standard vs Production vs Exploratory;
nanobody-anythingvsantibody-anythingvsprotein-anything; PXDesignpreviewvsextended. - Which compute target? —
local(default),hpc(RunPod / Modal / SLURM viaby-deploy-compute), ortamarind(cloud fallback). - Accept or re-run? — given the screening output, do the designs clear the quality bar, or does the campaign need a second pass with new hotspots, a new modality, or a different scaffold?
This skill encodes the canonical decision tree, the modality → tool mapping, the preset
comparison, the quality thresholds, and the accept-vs-re-run heuristics that other
skills depend on. It is the connective tissue between by-research / by-campaign-manager
(upstream) and the three tool wrappers protenix, pxdesign, boltzgen (downstream).
When to Use This Skill
Use this skill when you have:
- ✅ A new design project starting — research is done, but the tool / preset / compute target has not been chosen yet
- ✅ An ambiguous modality — user said "I want a binder against X" without specifying antibody vs nanobody vs de novo
- ✅ A target description but no plan — PDB ID or sequence in hand, need a campaign sized end-to-end
- ✅ A screening result and a decision to make — designs are scored, should we lab-submit, re-run with new hotspots, or escalate?
- ✅ A compute-target conflict — local GPU OOMs on a 1500-residue target, decide HPC vs Tamarind vs target cropping
- ✅ A novel target with no clear preset — need to pick between Standard and Exploratory tiers based on prior-art density
- ✅ A multi-target sweep request — orchestrate the same protocol over N targets and decide whether to batch or serialize
What ships with it
5 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 · 475 lines · 4 tokens per session scan A 29854c793d63
by-design-workflow is a skill published in the GitHub repository 001TMF/blatant-why (119 stars, last pushed 9d ago), licensed MIT. It adds 4 tokens to every session and 8,341 once invoked, about $0.0000 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.
Other skills, from other repositories
drug-design
End-to-end drug discovery pipeline orchestration. Deterministic Python script that auto-chains structure prediction, pocket detection, de novo design, docking, scoring, and ADMET filtering into reproducible workflows.
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.