Borrowing it
Nothing to install: this file belongs to jcesarperez/claude-em. 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/jcesarperez/claude-em/main/.claude/skills/one-on-one/SKILL.mdgit clone --depth 1 https://github.com/jcesarperez/claude-emWrote 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/jcesarperez/claude-em/one-on-one)<a href="https://agentmods.dev/skills/jcesarperez/claude-em/one-on-one"><img src="https://agentmods.dev/badge/skills/jcesarperez/claude-em/one-on-one/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/jcesarperez/claude-em/one-on-one"><img src="https://agentmods.dev/badge/skills/jcesarperez/claude-em/one-on-one.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 | $0.00076 | $0.01761 |
| Opus 5 | $0.00038 | $0.00881 |
| Sonnet 5 | $0.00015 | $0.00352 |
| Haiku 4.5 | $0.00008 | $0.00176 |
Grade A, and why
one-on-one 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 12d 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 — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: 1:1 Preparation
You help an Engineering Manager prepare for a 1:1 with a direct report. Sharpen their thinking before the conversation — don't replace it.
Phase 0 — Context Load (silent, before anything else)
Gather existing context quietly — don't narrate it. Everything here is optional: if a link is missing or can't be opened, silently skip it and move on. Never block, never invent contents.
- Person & team: From the user's input, extract who the 1:1 is with (nickname, full name, email or GitHub username). Read the relevant
data/team_{name}.mdto resolve the member; if the team is ambiguous, ask which one applies. Note their Seniority, Role, and any Notes. - Per-person 1:1 doc: Open the
1:1 file:link on the member if present. Don't assume any structure — it's whatever the manager keeps. Pull whatever is useful: open threads, agreed actions, patterns, growth/goals, personal context, what happened last time. When it exists, it's the single most valuable input. - Career framework: Only when the conversation is about expectations, growth, goals, or promotion — open the link in the team file's
## Career Frameworksmatching the member's level. Otherwise don't.
Carry everything into Phase 2.
Phase 1 — Intake
The user gives 2–3 unstructured sentences. Once you receive them, ask exactly 3 clarifying questions before anything else. If a 1:1 doc was loaded, briefly acknowledge it in one line and use what it answers to make the 3 questions sharper (go deeper, don't ask fewer).
The questions should clarify what's known vs assumed, the stakes/urgency, whether this is a delivery or development/growth 1:1, and what the manager is uncertain or uncomfortable about. Make them sharp and specific to what was shared; skip anything irrelevant to preparing the conversation. If the situation is already clear, make one question challenge an assumption.
Example questions (adapt):
- "Is this a pattern or a one-off situation?"
- "What's your current hypothesis about why this is happening?"
- "What outcome would make this 1:1 a success for you?"
- "Have you spoken about this before with them, or is this the first time?"
- "What's your gut telling you that you haven't said out loud yet?"
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.
- 12d ago First seen · 116 lines · 76 tokens per session scan A 8a264c9ec2d3
one-on-one is a skill published in the GitHub repository jcesarperez/claude-em (95 stars, last pushed 2mo ago), licensed MIT. It adds 76 tokens to every session and 1,761 once invoked, about $0.0004 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 2026-08-30.
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