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 skills add 001TMF/blatant-why --skill by-campaign-managergit 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/skills/001tmf/blatant-why/by-campaign-manager)<a href="https://agentmods.dev/skills/001tmf/blatant-why/by-campaign-manager"><img src="https://agentmods.dev/badge/skills/001tmf/blatant-why/by-campaign-manager/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/skills/001tmf/blatant-why/by-campaign-manager"><img src="https://agentmods.dev/badge/skills/001tmf/blatant-why/by-campaign-manager.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 490 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00004 | $0.08865 |
| Opus 5 | $0.00002 | $0.04432 |
| Sonnet 5 | $0.00001 | $0.01773 |
| Haiku 4.5 | $0.00000 | $0.00886 |
Grade A, and why
by-campaign-manager 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 12d 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 — 746 lines — stays where its author put it; the contents beside it link to each section on GitHub.
BY Campaign Manager
Plan, execute, monitor, and assess protein/antibody design campaigns. This skill governs how to size a campaign, track run state, coordinate multi-run efforts, estimate cost and time on each compute target (local GPU, HPC, Tamarind), monitor progress, and evaluate campaign health.
When to Use This Skill
Use this skill when you need to:
- ✅ Start a new design campaign — size designs/scaffolds, pick modality, choose compute tier
- ✅ Track run state through the campaign state machine (draft → configured → designing → screening → ranked → submitted)
- ✅ Estimate cost and time before launching (local GPU hours, HPC $/hr, Tamarind credits)
- ✅ Resume an interrupted campaign from the last checkpoint without re-doing work
- ✅ Assess mid-run health — pass rate trajectory, score distributions, abort/continue decisions
- ✅ Coordinate multi-run efforts — vary one parameter axis at a time, aggregate cross-run results
- ✅ Decide when to iterate — low pass rate, poor scores, insufficient diversity
Don't use this skill for:
- ❌ Target research and epitope mapping → use
by-research - ❌ Submitting individual jobs or scoring single designs → use
by-design/by-screening - ❌ Lab submission gating → use
by-lab(triple-gated) - ❌ HPC infrastructure setup → use
by-deploy-compute - ❌ Sequence/structure clustering for diverse panels → use
by-diversity
Quick Start
A typical campaign launch from the planner sub-agent:
# 1. Capture user preferences (modality, tier, scaffolds)
/by:plan-campaign
# 2. Estimate cost on each compute target before approval
python scripts/estimate_campaign.py \
--target PD-L1 \
--modality vhh \
--tier standard \
--scaffolds 2
# 3. Initialize campaign state and launch design phase
# (delegate via Task(by-design, ...))
# 4. Monitor mid-run
python scripts/track_progress.py \
--campaign-dir campaigns/pdl1/campaign_20260520_001
Expected estimate_campaign.py output: cost table for local / HPC RunPod / Tamarind,
with wall-clock hours and confidence intervals.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 746 lines · 4 tokens per session scan A df19cc808724
by-campaign-manager is a skill published in the GitHub repository 001TMF/blatant-why (114 stars, last pushed 26d ago), licensed MIT. It adds 4 tokens to every session and 8,865 once invoked, about $0.0000 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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bulk-transcriptomics
Bulk RNA-seq and microarray differential expression analysis including method selection, batch correction, and complex experimental designs.
chromatin-regulation
Chromatin regulation analysis from called peaks and count matrices — differential binding, signal summarisation, peak annotation, and scATAC-seq.
spatial-omics
Spatial transcriptomics and spatial proteomics analysis covering technology-specific workflows, spatial statistics, deconvolution, and niche analysis.