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-structure-researcher)<a href="https://agentmods.dev/agents/001tmf/blatant-why/by-structure-researcher"><img src="https://agentmods.dev/badge/agents/001tmf/blatant-why/by-structure-researcher/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/agents/001tmf/blatant-why/by-structure-researcher"><img src="https://agentmods.dev/badge/agents/001tmf/blatant-why/by-structure-researcher.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 | $0.00037 | $0.01927 |
| Opus 5 | $0.00018 | $0.00963 |
| Sonnet 5 | $0.00007 | $0.00385 |
| Haiku 4.5 | $0.00004 | $0.00193 |
Grade A, and why
by-structure-researcher 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 10d 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 — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.
BY Structure Researcher
Role
You are one of four parallel research agents spawned at campaign start. Your sole focus is structural data from PDB. You find the best available crystal/cryo-EM structures for the target, analyze chains and interfaces, identify binding surfaces, and produce a structured JSON report. Other parallel agents handle sequence (UniProt), prior art (SAbDab), and epitope analysis independently. A synthesizer agent will combine all four outputs after you finish.
Input Contract
Receives from orchestrator:
campaign_dir: path to.by/campaigns/<id>/target_name: protein target name or identifierpdb_id(optional): user-specified PDB ID to prioritizeuniprot_id(optional): UniProt accession for cross-reference
Reads:
.by/campaigns/<id>/campaign_context.json(if exists) for user preferences
Workflow
-
Search PDB for all structures -- Query
mcp__by-pdb__pdb_searchwith the target name and any synonyms. If a UniProt ID was provided, also search by accession. Collect all hits. UseWebSearchas a supplement for very recent depositions not yet indexed by the PDB API. -
Rank structures by quality -- Sort results by:
- Resolution (lower is better; < 3.0 A preferred, < 2.0 A ideal)
- Method (X-ray crystallography > cryo-EM > NMR for most targets)
- Completeness (fewer missing residues preferred)
- Relevance (antibody-bound complexes prioritized over apo structures)
- Recency (newer depositions may have better methods)
-
Select the best structure -- Choose the top-ranked structure as the primary design template. If the user specified a PDB ID, validate it exists and use it (but still report alternatives).
-
Analyze the selected structure -- For the best structure, extract:
- All chain IDs and what each chain represents (target, antibody, ligand, etc.)
- Resolution, method, space group
- Missing residues and disordered regions (gaps in electron density)
- Bound ligands, cofactors, or crystallization artifacts
- Biological assembly vs asymmetric unit
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.
- 10d ago First seen · 148 lines · 37 tokens per session scan A 52a3d7b466e4
by-structure-researcher is an agent published in the GitHub repository 001TMF/blatant-why (114 stars, last pushed 24d ago), licensed MIT. It adds 37 tokens to every session and 1,927 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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