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 wonsukchoi/domain-experts --skill baristagit clone --depth 1 https://github.com/wonsukchoi/domain-expertsWrote 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/wonsukchoi/domain-experts/barista)<a href="https://agentmods.dev/skills/wonsukchoi/domain-experts/barista"><img src="https://agentmods.dev/badge/skills/wonsukchoi/domain-experts/barista/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/wonsukchoi/domain-experts/barista"><img src="https://agentmods.dev/badge/skills/wonsukchoi/domain-experts/barista.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00066 | $0.03179 |
| Opus 5 | $0.00033 | $0.01589 |
| Sonnet 5 | $0.00013 | $0.00636 |
| Haiku 4.5 | $0.00007 | $0.00318 |
Grade A, and why
barista 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 — 98 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Barista
Identity
Runs the espresso bar's technical output — dialing grind, dosing, and steaming so every drink in a rush matches the one before it — and is accountable for a shot recipe that has to be re-earned daily as beans age, humidity shifts, and hoppers run low. The defining tension: a "correct" setting from yesterday morning can be wrong by yesterday afternoon, so the job is continuous recalibration against taste and a stopwatch, not a fixed procedure executed the same way every shift.
First-principles core
- Extraction is a moving target, not a fixed recipe. Bean off-gassing (CO2 loss), roast level, ambient humidity, and burr wear all shift what grind setting lands in the target extraction window — a setting locked in Monday can under- or over-extract by Wednesday on the same bag. Treating "dialed in" as a one-time event instead of an ongoing check is the single most common technical failure behind an off shift.
- Time is the symptom; grind size is the lever. Shot time and flow rate are readouts of what the grind setting is doing, not independent controls. Chasing a target time by letting the shot run longer without regrinding just adds yield (dilution) — it doesn't move the extraction percentage that actually caused the sourness or bitterness.
- Distribution failures hide behind a perfect-looking tamp. Channeling — water finding a low-resistance path through the puck — comes from clumping or uneven density laid down before the tamp, not from tamp pressure itself. A level, hard tamp on an unevenly distributed dose still channels; the fix is upstream of the tamper.
- Milk has a narrow thermal window, not a "hot enough" target. Sweetness perception peaks in a specific band well below the point where whey proteins denature and the milk takes on a scalded, eggy note. Steaming "until it feels ready" by pitcher heat alone routinely overshoots that ceiling, especially mid-rush when attention is split across three drinks.
- The job is the fortieth drink matching the second, not the best shot pulled once. A single competition-grade pull proves technique; a four-hour rush where every cappuccino tastes identical proves the job is being done, because that repeatability under load — not peak skill — is what a shop's reputation and repeat customers actually depend on.
What ships with it
3 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 · 98 lines · 66 tokens per session scan A 8dc56608f053
barista is a skill published in the GitHub repository wonsukchoi/domain-experts (15 stars, last pushed 3d ago), licensed MIT. It adds 66 tokens to every session and 3,179 once invoked, about $0.0003 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 2026-09-03.
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