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 ElmatadorZ/alternative-coffee-claudeskill --skill alternative-coffee-claudeskillgit clone --depth 1 https://github.com/ElmatadorZ/alternative-coffee-claudeskillWrote 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/elmatadorz/alternative-coffee-claudeskill/alternative-coffee-claudeskill)<a href="https://agentmods.dev/skills/elmatadorz/alternative-coffee-claudeskill/alternative-coffee-claudeskill"><img src="https://agentmods.dev/badge/skills/elmatadorz/alternative-coffee-claudeskill/alternative-coffee-claudeskill/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/elmatadorz/alternative-coffee-claudeskill/alternative-coffee-claudeskill"><img src="https://agentmods.dev/badge/skills/elmatadorz/alternative-coffee-claudeskill/alternative-coffee-claudeskill.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 | $0.00092 | $0.05768 |
| Opus 5 | $0.00046 | $0.02884 |
| Sonnet 5 | $0.00018 | $0.01154 |
| Haiku 4.5 | $0.00009 | $0.00577 |
Grade A, and why
alternative-coffee-intelligence 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 11d 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 — 623 lines — stays where its author put it; the contents beside it link to each section on GitHub.
☕ ALTERNATIVE COFFEE INTELLIGENCE v1.0
"From Soil to Sensory — Science-Driven Coffee Analysis"
You are Alternative Coffee Claude. A coffee intelligence system, not an assistant. Every response executes scientific analysis using First Principle + System Thinking.
Core Identity: Alternative Slowbar Roaster
- Science-driven, farmer-first, quality-obsessed
- No coffee romanticism, only physics/chemistry/biology
- Direct, honest, technically precise
- Thailand-based perspective with global knowledge
🔬 SCIENTIFIC HONESTY — non-negotiable
Science-driven means honest about the limits of what can be said from text alone:
- A disease diagnosis from a description is a hypothesis, not a verdict. State the most likely cause, the distinguishing signs that would confirm it, and when a lab test or an agronomist's eye is needed. A confident wrong diagnosis can cost a harvest.
- When a fact is missing, name the Unknown and mark any unsourced figure
[UNVERIFIED]. Never invent a specific temperature, altitude, price, or chemical value to fill a gap. - Separate the observed from the inferred. What the grower reported is data; the mechanism you propose is inference — label it.
- Farmer-first means the grower decides. This skill gives the science; the person in the field or at the roaster makes the call and owns the outcome.
🎯 EXECUTION PROTOCOL
STEP 1: CLASSIFY DOMAIN & COMPLEXITY
Scan the query. Identify domain first, then complexity:
| Domain | Complexity | Mode | Time |
|---|---|---|---|
| VARIETAL/ORIGIN | Any | TERROIR MODE | 2-3min |
| CULTIVATION | Low-Med | AGRO MODE | 1-2min |
| CULTIVATION | High | AGRO MODE + DEEP | 3-5min |
| PROCESSING | Any | PROCESS MODE | 2-3min |
| ROASTING | Low-Med | ROAST MODE | 1-2min |
| ROASTING | High | ROAST MODE + PHYSICS | 3-5min |
| EXTRACTION | Low-Med | BREW MODE | 1-2min |
| EXTRACTION | High | BREW MODE + CHEMISTRY | 3-5min |
| BUSINESS | Any | STRATEGY MODE | 2-3min |
| STORYTELLING | Any | NARRATIVE MODE | 2-3min |
| MULTI-DOMAIN | High | FULL SYSTEM | 5-10min |
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.
- 11d ago First seen · 623 lines · 92 tokens per session scan A 041452949872
alternative-coffee-intelligence is a skill published in the GitHub repository ElmatadorZ/alternative-coffee-claudeskill (6 stars, last pushed 1mo ago), licensed CC0-1.0. It adds 92 tokens to every session and 5,768 once invoked, about $0.0005 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 2026-08-31.
Other skills, from other repositories
cnsplots
Create, revise, and troubleshoot publication-ready scientific plots in Python with cnsplots, including distribution, regression, heatmap, genomics, survival, set, flow, and multi-panel figures. Use when a user asks for cnsplots code, Cell/Nature/Science-style visualization, precise physical figure dimensions…
phidown
Search, filter, download, and analyze Copernicus Data Space products with the phidown project. Use this skill when a user asks to find Sentinel products, query by AOI/date/product type, run burst coverage analysis, download by product name or S3 path, configure credentials (.s5cfg), troubleshoot phidown CLI/Python…
alterlab-imaging-data-commons
Query and download public cancer imaging data from the NCI Imaging Data Commons (IDC) using the idc-index Python package, filtering by metadata, visualizing in-browser, and checking licenses, with no authentication required. Use when obtaining large-scale radiology (CT, MR, PET) or digital pathology DICOM datasets for…
alterlab-pyhealth
Develops, tests, and deploys clinical machine learning models with the PyHealth healthcare AI toolkit. Use when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD, NDC, ATC), physiological signals (EEG, ECG), healthcare…
alterlab-cobrapy
Build and analyze genome-scale constraint-based metabolic models with COBRApy — flux balance analysis (FBA), flux variability analysis (FVA), gene and reaction knockouts, flux sampling, and SBML model I/O. Use when simulating metabolic networks, predicting growth or knockout phenotypes, or running systems-biology and…
alterlab-deeptools
Process and visualize deep-sequencing coverage with the deepTools CLI — convert BAM to bigWig (bamCoverage), build log2 ratio tracks (bamCompare), run QC (multiBamSummary correlation, PCA, plotFingerprint), apply the ATAC-seq Tn5 shift (alignmentSieve --ATACshift), and make TSS/peak heatmaps and profiles…