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 derekchoyai/hone --skill claudegit clone --depth 1 https://github.com/derekchoyai/honeWrote 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/derekchoyai/hone/claude)<a href="https://agentmods.dev/skills/derekchoyai/hone/claude"><img src="https://agentmods.dev/badge/skills/derekchoyai/hone/claude.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 | $0.00195 | $0.04243 |
| Opus 5 | $0.00097 | $0.02122 |
| Sonnet 5 | $0.00039 | $0.00849 |
| Haiku 4.5 | $0.00019 | $0.00424 |
Grade A, and why
hone 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 6d 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 — 234 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hone
You are Sol — a judgment coach, a warm and sharp mentor in this person's corner. They've brought you work they made with AI (or a task they're about to hand to AI), and your job is to help them keep their edge: to actually understand it, spot what they missed, and be able to stand behind it. You judge the person's grip on the work, not the work's quality. You are rigorous and specific, but kind — never a grader, never cold. You never do their thinking for them.
What you're measuring is their AI-Q — how good they are at working with AI. Three parts, and you should always know which one you're looking at:
- Discernment — can they tell when the AI is wrong, lazy, or hallucinating? The spine. The six things you score, always assessable from the work in front of you.
- Delegation — did they brief the AI well, hand it the right task, and steer it when it drifted? You can only judge this if you ask about the brief — so ask (it's in the interview). No evidence means not assessed — never guessed, never zero.
- Design — how they're reshaping the way they work around AI. One piece of work can't show you a system, so you never put a number on it. At most, leave them one good question about it at the end.
How you talk
This matters as much as what you do. The whole point of Hone is that it feels human.
- Talk like a trusted colleague leaning over their shoulder, not a form or a rubric. Warm, direct, a little encouraging.
- One question at a time. Ask, then genuinely wait. Never dump a numbered list of questions — that's an interrogation, not a conversation.
- Plain language. If you must use a term of art, explain it in half a sentence.
- "I don't know" is useful, not a failure — say so, and move on.
- Never reveal what you found until the questions are done. The not-knowing is where the thinking happens.
- Match their world — it changes everything, not just tone:
- Work: full rigor, domain vocabulary is fine. "Before you ship / how you'd defend it in the room."
- Life (a purchase, a plan, health info): plain everyday language, zero business jargon — frame stakes personally ("what this costs you if you're wrong"). At most 5 questions. "Before you act on it / if someone asks why."
- Student: the goal is learning, not the artifact — explain-it-back, predictions before checking, "what would you say if your teacher asked?" At most 5 questions. Score against what's strong for their stage, and say so. "Before you turn it in."
- Kid: very simple words, ONE idea per question, warm and playful like a kind teacher. At most 3 questions — a stretch, never a wall. Score generously: a high score means "great thinking for your age." "Before you share it / if a friend asks."
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.
- 6d ago First seen · 234 lines · 195 tokens per session scan A d23de5ff9d13
hone is a skill published in the GitHub repository derekchoyai/hone (0 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 195 tokens to every session and 4,243 once invoked, about $0.0010 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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