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 commands/001tmf/blatant-why/screengit clone --depth 1 https://github.com/001TMF/blatant-whyWhat 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 | $0.00007 | $0.00177 |
| Opus 5 | $0.00003 | $0.00088 |
| Sonnet 5 | $0.00001 | $0.00035 |
| Haiku 4.5 | $0.00001 | $0.00018 |
Grade A, and why
screen 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 3d 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.
What it actually says
Run the complete screening battery on design $ARGUMENTS.
Use the by-screening MCP server to run:
- PTM liability scan (deamidation, isomerization, oxidation, glycosylation, free Cys)
- Net charge at pH 7.4
- Developability assessment (CDR length, hydrophobic fraction, composition)
- ipSAE scoring (if NPZ available)
- Composite score with pass/fail verdict
Present results with:
- Per-category breakdown with severity levels
- Overall verdict (PASS/MARGINAL/FAIL)
- Specific recommendations for addressing any issues
- Comparison to quality thresholds from by-screening skill
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.
- 3d ago First seen · 23 lines · 7 tokens per session scan A f376fbcf8cb0
screen is a command published in the GitHub repository 001TMF/blatant-why (114 stars, last pushed 17d ago), licensed MIT. It adds 7 tokens to every session and 177 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.
Other commands, from other repositories
run-archesweather
Guide the user through running ArchesWeather end-to-end on an AMD cluster.
run-mattergen
Guide the user through running MatterGen end-to-end on an AMD cluster via SLURM or Docker.
run-matey
Guide the user through training or inference with MATEY on AMD GPUs.
run-aurora
Guide the user through running Aurora (0.1° resolution) end-to-end on an AMD cluster.
run-semlaflow
Guide the user through generating 3D molecular structures with SemlaFlow on AMD GPUs.
add-recipe
Create or improve a recipe (inference, fine-tune, eval, etc.) for a model already in AI4Science Studio.