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 skills/termio-sh/termio/prototypenpx skills add termio-sh/termio --skill prototypegit clone --depth 1 https://github.com/termio-sh/termioWhat 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 | $0.00048 | $0.01634 |
| Opus 5 | $0.00024 | $0.00817 |
| Sonnet 5 | $0.00010 | $0.00327 |
| Haiku 4.5 | $0.00005 | $0.00163 |
Grade A, and why
prototype 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 2d 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.
This is a copy
100% identical to prototype — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prototyping Variants
A divergence skill. It does ONE thing: take a described piece of UI ("a toast", "the pricing card", "a hold-to-delete button"), build several genuinely different versions of it, and put them behind a visual picker so the user can flip through them live and choose a winner. It does not review existing UI (that's review-animations), plan fixes for it (that's improve-animations), or choose dependencies (that's pick-ui-library).
Operating Posture
You are a senior design engineer running a design exploration. The entire value of this skill is divergence: three tints of the same idea waste the picker — the user learns nothing by flipping between them. Each variant must be a direction you could defend shipping on its own, exploring a genuinely different answer to the same brief.
Divergence is not an excuse to drop the craft bar. Every variant individually meets Emil Kowalski's standards — right easing (ease-out on entrances, never ease-in), sub-300ms UI motion, correct transform-origin, transform/opacity only, reduced-motion handled. A sloppy variant doesn't widen the exploration; it just loses on execution and teaches nothing about the direction it represents.
Hard Rules
- Never touch production code during exploration. Everything lives in an isolated prototype surface (see Phase 4). Integration happens only in Phase 6, only for the variant the user picked.
- Variants diverge on a named axis — layout, density, personality, motion, interaction model. Before building, you must be able to state each variant's axis in a phrase. Sharing the project's tokens is not convergence; variants should feel native to the product.
- Every variant fully works. Real interactions, real motion, realistic content — actual product-shaped copy, plausible names and numbers. No lorem ipsum, no dead buttons, no "imagine this part".
- The picker is chrome, not a contestant. Its exact markup, styles, and behavior are specified in PICKER.md — copy them verbatim. Its look is not a design decision and never adapts to the project.
- Clean up after the choice. When a winner is promoted, delete the prototype surface unless the user asks to keep it.
What ships with it
1 file 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.
- 2d ago First seen · 91 lines · 48 tokens per session scan A 2ad8401c4dea
prototype is a skill published in the GitHub repository termio-sh/termio (359 stars, last pushed 2d ago), licensed MIT. It adds 48 tokens to every session and 1,634 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to prototype, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
memory
Use durable BB memory when prior project knowledge or cross-project user preferences can improve the current task, and save durable new learning through the bb memory CLI.
argent-tv-interact
Control and inspect TV apps via argent — Apple TV (tvOS), Android TV (leanback), and Amazon Fire TV (Vega). Boot the target, read focus, navigate with the D-pad remote, type, screenshot, and on Vega debug the JS runtime (evaluate, console logs, network inspector). Use when a task targets a TV (runtimeKind "tv", or…
review-offered-task
Review a task that has been offered to you and decide whether to accept or reject it.
company-hiring-intelligence
Reverse-engineer what a company is building by scraping their job postings, careers page, LinkedIn Jobs, and engineering blog using TinyFish web agents. Use whenever a user wants to understand a company's strategic direction from hiring signals, do competitive intelligence, figure out a tech stack from job…
aidd-dev:08:for-sure
Iterative agent loop that tracks attempts and retries until a success condition is met. Use when the user says "for sure", "make sure", "keep trying until", "loop until done", "don't stop until", or needs guaranteed completion of a task with explicit success criteria.
performance-optimization
Measure-first performance work. Use on triggers like "it's slow", "profile this", "optimize perf", "fix the bottleneck", "improve load time / Core Web Vitals", or when a measured regression needs fixing. Enforces measure-before-optimize. Fills a perf gap not covered by existing project skills. Not a build/ship…