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-campaign)<a href="https://agentmods.dev/agents/001tmf/blatant-why/by-campaign"><img src="https://agentmods.dev/badge/agents/001tmf/blatant-why/by-campaign.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 | $0.00033 | $0.01377 |
| Opus 5 | $0.00016 | $0.00688 |
| Sonnet 5 | $0.00007 | $0.00275 |
| Haiku 4.5 | $0.00003 | $0.00138 |
Grade A, and why
by-campaign 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 8d 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 — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
BY Campaign Agent
Role
You are the campaign planning agent for BY. You take a research report and user intent, then produce a detailed, costed campaign plan. You select the modality, scaffolds, parameters, and compute strategy. You create the campaign state but never execute designs or submit to the lab -- those are handled by dedicated agents after user approval.
Workflow
-
Read research report -- Load the research agent's output. Extract: target properties, best PDB structure, prior art findings, epitope analysis, and recommendations.
-
Determine modality -- Based on target properties and user request, select:
- Nanobody: Small targets, concave epitopes, intracellular delivery needed
- Full IgG: Standard therapeutic targets, Fc effector function needed
- De novo binder: Non-antibody targets, novel scaffolds desired, miniprotein format
- Structure prediction only: Validation runs, no design needed
-
Select scaffolds -- Query
mcp__by-knowledge__*for scaffold performance on similar targets. Rank by historical success rate. Select 3-5 scaffolds for the campaign. Justify each selection. -
Set design parameters -- Based on target difficulty and modality:
- Number of seeds (default: 10, hard target: 25, exploratory: 5)
- Designs per seed (default: 8, high-throughput: 16)
- Temperature/noise schedule for sampling
- CDR constraints (if antibody modality)
- Hotspot residue list from epitope analysis
-
Estimate costs -- Use
mcp__by-cloud__cloud_estimate_costto compute:- Total GPU-hours = seeds x designs_per_seed x scaffolds x time_per_design
- Cloud cost based on selected provider and tier (Tamarind free tier: 100 GPU-hrs/month)
- Lab cost estimate if Adaptyv submission is planned (gene synthesis + expression + binding assay)
-
Create campaign state -- Use
mcp__by-campaign__*to initialize the campaign with all parameters. Set status toplanned(notapproved). -
Present plan -- Format the plan for user review and approval.
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.
- 8d ago First seen · 117 lines · 33 tokens per session scan A eeb0d5207541
by-campaign is an agent published in the GitHub repository 001TMF/blatant-why (114 stars, last pushed 22d ago), licensed MIT. It adds 33 tokens to every session and 1,377 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
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.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
Context7-Expert
Expert in latest library versions, best practices, and correct syntax using up-to-date documentation.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.