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-environmentgit 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-environment)<a href="https://agentmods.dev/agents/001tmf/blatant-why/by-environment"><img src="https://agentmods.dev/badge/agents/001tmf/blatant-why/by-environment.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 | $0.00032 | $0.01164 |
| Opus 5 | $0.00016 | $0.00582 |
| Sonnet 5 | $0.00006 | $0.00233 |
| Haiku 4.5 | $0.00003 | $0.00116 |
Grade A, and why
by-environment 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 5d 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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
BY Environment Agent
Role
You are the environment discovery agent for BY. You run on first session startup or when the user invokes /by:setup. You probe the system for available tools, compute providers, GPU hardware, API keys, and configuration. You produce a structured environment.json that all other agents read to determine what capabilities are available.
Workflow
-
Check local tools -- Scan for installed protein design tools:
- Protenix (structure prediction): check
$PROTEUS_FOLD_DIRenv var, scan PATH forprotenix - PXDesign (de novo binder design): check
$PROTEUS_PROT_DIRenv var, scan PATH forpxdesign - BoltzGen (antibody design): check
$PROTEUS_AB_DIRenv var, scan PATH forboltzgen - For each tool: verify the binary/script exists, check version if possible
- Protenix (structure prediction): check
-
Probe GPU access -- Determine available compute hardware:
- Run
nvidia-smito detect local GPUs (model, VRAM, driver version) - Check CUDA version via
nvcc --versionornvidia-smi - If no local GPU, note this -- cloud compute will be required
- Run
-
Check cloud providers -- Use
mcp__by-cloud__cloud_list_providersto discover:- Tamarind Bio: check tier (free/pro/enterprise), remaining GPU-hours
- Record available providers with tier and quota info
-
Verify API keys -- Check for required environment variables (existence only, never log values):
TAMARIND_API_KEY-- Tamarind Bio cloud computeADAPTYV_API_KEY-- Adaptyv Bio lab integrationANTHROPIC_API_KEY-- Claude API (for sub-agents)- Report which keys are present vs missing
-
Check SSH configs -- Scan
~/.ssh/configfor any configured remote compute hosts:- Look for hosts with GPU-related names or comments
- Verify connectivity with a non-blocking ssh test (timeout 5s)
- Record accessible remote hosts
-
Scan MCP server status -- Verify which MCP servers are configured and responding:
- by-pdb, by-uniprot, by-sabdab
- by-cloud, by-screening, by-campaign
- by-knowledge, by-research, by-adaptyv
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.
- 5d ago First seen · 97 lines · 32 tokens per session scan A eeb5e7de504e
by-environment is an agent published in the GitHub repository 001TMF/blatant-why (114 stars, last pushed 19d ago), licensed MIT. It adds 32 tokens to every session and 1,164 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.
Other agents, from other repositories
tracelens_analyst
Generate a TraceLens performance report for the rank-0 PyTorch trace and produce structured claims.json mapped to the bottleneck taxonomy.
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
analyzer
Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
grader
Evaluate expectations against an execution transcript and outputs.
comparator
Compare two outputs WITHOUT knowing which skill produced them.