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-sessiongit 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-session)<a href="https://agentmods.dev/skills/001tmf/blatant-why/by-session"><img src="https://agentmods.dev/badge/skills/001tmf/blatant-why/by-session.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, 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 276 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.
- medium Excessive Agency · line 278 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 | $0.00002 | $0.04521 |
| Opus 5 | $0.00001 | $0.02261 |
| Sonnet 5 | $0.00000 | $0.00904 |
| Haiku 4.5 | $0.00000 | $0.00452 |
Grade A, and why
by-session 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 — 342 lines — stays where its author put it; the contents beside it link to each section on GitHub.
BY Session Skill
Session initialization and configuration for BY projects. This skill defines the full
session-start sequence (banner, environment check, status display) and the first-run
configuration questionnaire that writes .by/config.json. It is not optional — it
runs every time a new session opens in a BY project directory.
The session skill is the single source of truth for which compute provider BY will
use this session. Every downstream skill (by-design-workflow, by-screening, the
design engines) reads .by/config.json written here.
When to Use This Skill
Use this skill when you have:
- ✅ A new session opening in a BY project directory — the SessionStart hook fires this skill automatically
- ✅ No
.by/config.jsonyet — run the first-run questionnaire to capture compute provider, model profile, campaign defaults - ✅ A request to refresh environment discovery — re-scan local tools, conda envs, GPU, API keys
- ✅ A request to switch compute provider — update
compute.default_providercleanly without overwriting unrelated fields - ✅ An in-progress campaign to detect and surface (
campaigns/*/campaign_log.jsonwith status notcomplete) - ✅ Staleness checks —
.by/environment.jsonolder than 24h should prompt a refresh
Do NOT use this skill when:
- ❌ You are mid-campaign and just want status → use the
statusskill (it reads but does not re-questionnaire) - ❌ You need to deploy compute on RunPod / HPC → use
by-deploy-compute(this skill only records the choice of HPC) - ❌ You are scoring designs or running design engines → use
by-scoring,boltzgen,pxdesign, orprotenix - ❌ You want to inspect a specific campaign's state → use
by-campaign-manager - ❌ The user is editing
.by/config.jsonby hand → read it back withvalidate_config.py, do not re-run the questionnaire
Quick Start
User opens a new terminal in a BY project directory.
Agent (by-session):
1. Banner → BY ► Protein Design Agent
2. Check → .by/config.json exists?
3a. NO → run init_questionnaire.py (or its inline AskUserQuestion equivalent)
→ writes .by/config.json with local-first defaults
3b. YES → read config + environment, no questions asked
4. Resume → check campaigns/*/campaign_log.json for in-progress work
5. Status → print compute + profile + campaign count
6. Done → "Ready" prompt with suggested actions
What ships with it
4 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.
- 8d ago First seen · 342 lines · 2 tokens per session scan A 9fbce2dc6b1d
by-session is a skill published in the GitHub repository 001TMF/blatant-why (114 stars, last pushed 22d ago), licensed MIT. It adds 2 tokens to every session and 4,521 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 skills, from other repositories
alphafold
Skill for protein structure prediction and analysis with AlphaFold. Use this skill whenever a user wants to predict or fetch a protein 3D structure, download structures from the AlphaFold Database (AFDB), run ColabFold for novel proteins, parse pLDDT confidence scores or PAE (predicted aligned error) from AlphaFold…
esm2
Skill for working with ESM2 protein language models from Meta FAIR. Use this skill whenever the user wants to generate protein embeddings or representations, score variant effects or predict mutation fitness, run contact prediction, or use ESMFold for structure prediction. Also trigger when the user mentions ESM2…
evo2
Skill for genomic sequence modeling and design with Evo2 from Arc Institute. Use this skill when a user wants to model or generate DNA sequences, score variant effects at single-nucleotide resolution, extract genomic embeddings, analyze mutations in non-coding or coding regions, design synthetic genomic elements…
rfdiffusion
Skill for de novo protein backbone generation with RFdiffusion from the Baker Lab (Institute for Protein Design). Use this skill when a user wants to design a new protein backbone from scratch, scaffold a functional motif into a new protein, design a protein binder against a target, generate symmetric oligomers…
alphafold3
Skill for structure prediction with AlphaFold 3 (AF3) from Google DeepMind. Use this skill when a user wants to predict the structure of a protein complex with ligands, DNA, or RNA; predict protein-ligand binding poses; model protein-nucleic acid interactions; use SMILES or CCD codes to specify small molecules; parse…
boltz
Skill for biomolecular structure and binding affinity prediction with Boltz-2. Use this skill when a user wants to predict protein-ligand complex structures, estimate binding affinities (IC50/ΔG), screen compound libraries, optimize lead compounds, model protein-DNA or protein-RNA interactions, specify binding pockets…