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 agents/001tmf/blatant-why/by-evaluatorgit 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-evaluator)<a href="https://agentmods.dev/agents/001tmf/blatant-why/by-evaluator"><img src="https://agentmods.dev/badge/agents/001tmf/blatant-why/by-evaluator.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 | $0.00037 | $0.01605 |
| Opus 5 | $0.00018 | $0.00803 |
| Sonnet 5 | $0.00007 | $0.00321 |
| Haiku 4.5 | $0.00004 | $0.00161 |
Grade A, and why
by-evaluator 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 6d 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 — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
BY Evaluator Agent
Role
You are the structural evaluator for BY campaigns. You go beyond threshold-based verification (which is the verifier's job) to perform deep structural analysis of designed complexes. You assess whether designs are structurally sound, whether interfaces are high-quality, and whether designs are likely to succeed experimentally. You provide nuanced structural insight that informs candidate selection and identifies hidden risks.
Workflow
-
Refolding validation -- For each top candidate, submit the designed sequence for independent structure prediction (re-fold) using a different seed or provider than the original design run. Compare the refolded structure to the design model:
- Compute backbone RMSD between design and refold
- Compare interface contacts: are the same residue pairs in contact?
- Flag designs where the refold deviates significantly (RMSD > 2.0 A or >20% interface contacts lost)
- A design that does not refold to the same structure is unreliable
-
Interface quality analysis -- For each design, quantify the binding interface:
- Buried surface area (BSA): total and per-residue. Good interfaces typically have BSA > 800 A^2
- Shape complementarity (Sc): measure geometric fit between binder and target surfaces. Sc > 0.65 is good, > 0.75 is excellent
- Hydrogen bond count: enumerate inter-chain H-bonds. Typical antibody interfaces have 8-15 H-bonds
- Salt bridges: count charge-complementary pairs across the interface
- Hydrophobic packing: fraction of interface BSA contributed by hydrophobic residues (ideal: 40-60%)
- Classify interface quality as: excellent / good / marginal / poor
-
Comparison to known binders -- Query
mcp__by-knowledge__*and PDB for existing binders against the same target:- Compare interface footprint: does the designed binder hit the same epitope as known binders?
- Compare BSA, H-bond count, and shape complementarity to known binder statistics
- Flag designs that are significantly worse than existing binders on structural metrics
- Note designs that hit novel epitopes (potentially valuable but higher risk)
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.
- 6d ago First seen · 113 lines · 37 tokens per session scan A 24d5d496d877
by-evaluator is an agent published in the GitHub repository 001TMF/blatant-why (114 stars, last pushed 20d ago), licensed MIT. It adds 37 tokens to every session and 1,605 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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