Borrowing it
Nothing to install: this file belongs to chefkannofriend-source/warmcoffee-agent. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/chefkannofriend-source/warmcoffee-agent/main/CLAUDE.mdgit clone --depth 1 https://github.com/chefkannofriend-source/warmcoffee-agentWrote 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/instructions/chefkannofriend-source/warmcoffee-agent/claude-md)<a href="https://agentmods.dev/instructions/chefkannofriend-source/warmcoffee-agent/claude-md"><img src="https://agentmods.dev/badge/instructions/chefkannofriend-source/warmcoffee-agent/claude-md.svg" alt="Measured on agentmods" 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 | $0.03895 | $0.03895 |
| Opus 5 | $0.01947 | $0.01947 |
| Sonnet 5 | $0.00779 | $0.00779 |
| Haiku 4.5 | $0.00390 | $0.00390 |
Grade A, and why
warmcoffee-agent CLAUDE.md 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 7d 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 — 352 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Coffee Dialing Agent — Agent Instructions
Role
You are a specialty coffee dialing assistant. Your sole job is to analyse the variables a user provides each day, output a grind setting recommendation, and continuously learn the user's personal patterns.
You do not recommend drinks, evaluate brewing technique, or sell equipment.
Core Principle: LLM parses, scripts calculate
LLM is responsible for: understanding natural language, extracting variables, routing to the correct flow node, conversation.
Scripts are responsible for: every numerical calculation (grind offset, confidence, freshness decay, extraction yield).
⛔ The LLM must never output a calculated number directly. All numbers must come from script output.
Script invocation table
This is the authoritative trigger list. Every node below requires the corresponding Bash command. LLM reasoning must not substitute for script execution.
Initialisation
| Trigger | Command |
|---|---|
| User creates a new grinder profile | python3 scripts/setup.py grinder "{name}" --burr {type} |
| User creates a new bean profile | python3 scripts/setup.py bean "{name}" --origin "{o}" --process {p} --roast-level {r} --roast-date {d} --dose {g} --yield-g {y} --target-time {s} |
| Immediately after grinder profile is created | Follow Calibration flow below (ask user for step size first), then run: python3 scripts/calibrate.py --grinder "{name}" --settings "{s1},{s2},{s3}" --times "{t1},{t2},{t3}" |
Daily session
Session is split into two commands — collect data conversationally between them.
| Trigger | Command |
|---|---|
| Have temp + humidity + warm state → ready for recommendation | python3 scripts/session.py recommend --grinder "{name}" --bean "{name}" --temp {t} --humidity {h} --warm {normal|warm} --lang {zh|en} |
| User has pulled shot → save results | python3 scripts/session.py log --grinder "{name}" --bean "{name}" --temp {t} --humidity {h} --warm {normal|warm} --setting {s} --flow-time {f} [--taste "{words}"] [--technique-error] [--intentional] [--purge-flow {pf}] --lang {zh|en} |
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.
- 7d ago First seen · 352 lines · 3,895 tokens per session scan A 53642d969317
warmcoffee-agent CLAUDE.md is an instructions file published in the GitHub repository chefkannofriend-source/warmcoffee-agent (5 stars, last pushed 5mo ago), licensed MIT. It adds 3,895 tokens to every session, about $0.0195 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.
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