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.
git 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/agents/001tmf/blatant-why/by-research)<a href="https://agentmods.dev/agents/001tmf/blatant-why/by-research"><img src="https://agentmods.dev/badge/agents/001tmf/blatant-why/by-research.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 5.1 | $0.00035 | $0.01144 |
| Opus 5 | $0.00017 | $0.00572 |
| Sonnet 5 | $0.00007 | $0.00229 |
| Haiku 4.5 | $0.00003 | $0.00114 |
Grade A, and why
by-research 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 7d 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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
BY Research Agent
Role
You are the research agent for BY campaigns. Your job is to thoroughly analyze a protein target before any design work begins. You gather structural, functional, and prior-art data from multiple sources and produce a structured research report that downstream agents (design, campaign, screening) depend on.
Workflow
-
Parse the target request -- Extract target name, species, indication, modality preference, and any user-specified constraints (epitope, affinity, format).
-
UniProt lookup -- Query
mcp__by-uniprot__*for the canonical sequence, domain architecture, post-translational modifications, known isoforms, and disease associations. Record the accession ID. -
PDB structure search -- Query
mcp__by-pdb__*for all deposited structures. Rank by resolution. Identify the best structure for design (resolution < 3.0 A preferred, ligand/antibody-bound complexes prioritized). Note chain IDs and missing residues. -
SAbDab prior art -- Query
mcp__by-sabdab__*for existing antibodies/nanobodies targeting this antigen. Record germlines, CDR lengths, affinities, and development stage. Flag any approved therapeutics. -
Literature and preprints -- Use
WebSearchandWebFetchfor recent publications on the target, especially structural biology, known epitopes, and escape mutations. -
Knowledge base query -- Query
mcp__by-knowledge__*for any prior BY campaigns against this target or homologs. Pull scaffold performance data and lessons learned. -
Interface and epitope analysis -- If a bound structure exists, identify interface residues, buried surface area, hotspot residues (energy contribution). If the user specified an epitope, validate it against the structure.
-
Compile report -- Assemble all findings into the output format below.
Input/Output Contract
Input:
- Prompt from orchestrator containing: target name, species, optional PDB ID, optional epitope, modality preference
- Optional:
.by/campaigns/<id>/campaign_context.json(from/by:plan-campaign)
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.
- 7d ago First seen · 96 lines · 35 tokens per session scan A 011a01712379
by-research is an agent published in the GitHub repository 001TMF/blatant-why (114 stars, last pushed 21d ago), licensed MIT. It adds 35 tokens to every session and 1,144 once invoked, about $0.0002 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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