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/load)<a href="https://agentmods.dev/commands/001tmf/blatant-why/load"><img src="https://agentmods.dev/badge/commands/001tmf/blatant-why/load/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/load"><img src="https://agentmods.dev/badge/commands/001tmf/blatant-why/load.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 | $0.00013 | $0.00834 |
| Opus 5.5 | $0.00005 | $0.00334 |
| Sonnet 5.5 | $0.00003 | $0.00167 |
| Haiku 4.5 | $0.00001 | $0.00083 |
Grade A, and why
by:load scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/load — Load and Analyze a Protein Target
Load a protein target by name, PDB ID, or UniProt accession and run initial research analysis to prepare for a design campaign.
Instructions
Step 0: Show BY banner and read model profile
Display the BY session banner first:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
BY ► LOADING TARGET
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Then read model profile:
MODEL_PROFILE=$(cat .by/config.json 2>/dev/null | grep -o '"model_profile"[[:space:]]*:[[:space:]]*"[^"]*"' | grep -o '"[^"]*"$' | tr -d '"' || echo "balanced")
Model lookup for this command:
| Agent | quality | balanced | budget |
|---|---|---|---|
| by-research | opus | sonnet | sonnet |
Step 1: Parse input
Determine the input type:
- PDB ID: 4-character alphanumeric (e.g.,
1ABC,7XYZ) - UniProt accession: alphanumeric with pattern like
P12345orQ9UHD2 - Free text: treat as a target name or description for search
Step 2: Create campaign directory
CAMPAIGN_ID="campaign_$(date +%Y%m%d_%H%M%S)"
mkdir -p .by/campaigns/$CAMPAIGN_ID/{designs,screening,logs,research}
echo ".by/campaigns/$CAMPAIGN_ID" > .by/active_campaign
Initialize state.json with phase=RESEARCH, round=1, target info.
Step 3: Spawn by-research agent
Delegate to a by-research agent via Task() (model per profile table above).
Use MCP research tools (PDB, UniProt, SAbDab) -- NOT web search. The by-research agent has access to all structured databases via MCP servers.
Analyze target:
{user_argument}Tasks:
- Resolve the target to a PDB structure and UniProt entry using MCP tools
- Identify chains, domains, and key binding interfaces
- Search SAbDab for existing antibodies against this target
- Identify known epitopes and druggable sites
- Assess target difficulty (structured/disordered, glycosylation, flexibility)
- Recommend design strategy: binder vs antibody vs nanobody
- Suggest initial parameters (scaffold, CDR lengths, seed count)
Write analysis to
{campaign_dir}/research/target_analysis.json. Write human-readable summary to{campaign_dir}/research/summary.md.
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 · 113 lines · 13 tokens per session scan A 7d546d95c535
by:load is a command published in the GitHub repository 001TMF/blatant-why (119 stars, last pushed 8d ago), licensed MIT. It adds 13 tokens to every session and 834 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.