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/commands/001tmf/blatant-why/welcome)<a href="https://agentmods.dev/commands/001tmf/blatant-why/welcome"><img src="https://agentmods.dev/badge/commands/001tmf/blatant-why/welcome/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/commands/001tmf/blatant-why/welcome"><img src="https://agentmods.dev/badge/commands/001tmf/blatant-why/welcome.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 | $0.00016 | $0.00739 |
| Opus 5.5 | $0.00006 | $0.00296 |
| Sonnet 5.5 | $0.00003 | $0.00148 |
| Haiku 4.5 | $0.00002 | $0.00074 |
Grade A, and why
by:welcome 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/welcome — First-Run Orientation
Introduce BY to a new user and surface the core workflows without overwhelming them with internal complexity.
Instructions
Step 1: Check if this is a first run
Read .by/environment.json. If it does not exist, this is likely a fresh install.
Note the result for Step 3 and Step 4.
Step 2: Display welcome message
Present the following orientation:
Welcome to BY (Blatant-Why)
BY is a protein design agent that helps you design nanobodies, antibodies, and protein binders. You describe a target, BY handles the computation.
Getting Started (pick one)
1. Design nanobodies against a target
Example: "Design VHH nanobodies against PD-L1"
BY will research your target, ask a few quick questions about your preferences, then run the full design-screen-rank pipeline. You get back a ranked table of candidates ready for lab testing.
2. Load and explore a target first
Example:
/by:load PD-L1or/by:load 5JDSResearch a protein target before committing to a design campaign. BY pulls structural data, known binders, and epitope information so you can make an informed decision.
3. Check your environment
Example:
/by:setupSee what compute providers are available and which API keys are configured. Useful if you want to confirm Tamarind Bio access or check for local GPU tools.
4. Resume an existing campaign
Example:
/by:statusthen/by:resultsCheck the state of a running campaign or view ranked designs from a completed one.
Key Commands
| Command | What it does |
|---|---|
/by:plan-campaign |
Quick discussion to capture your design preferences before launching |
/by:load |
Research a protein target (structure, known binders, epitopes) |
/by:status |
Check current campaign progress |
/by:results |
View ranked design candidates with scores |
/by:setup |
Configure compute providers and API keys |
What happens behind the scenes
BY uses specialized MCP tools to search protein databases (PDB, UniProt, SAbDab), run structure predictions and design computations via Tamarind Bio cloud (free tier available), and score candidates with custom metrics (ipSAE for interface quality, ipTM for global confidence). You do not need to manage any of this -- just describe what you want.
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 · 85 lines · 16 tokens per session scan A 5b03c2cbd9b1
by:welcome is a command published in the GitHub repository 001TMF/blatant-why (119 stars, last pushed 8d ago), licensed MIT. It adds 16 tokens to every session and 739 once invoked, about $0.0001 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 commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
speckit.bug.fix
Apply the remediation from a bug assessment and record what was changed.
speckit.bug.test
Validate that a previously fixed bug is resolved and record the verification report.