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 Aibier/claude-em-toolkit --skill 1on1-prepgit clone --depth 1 https://github.com/Aibier/claude-em-toolkitWrote 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/aibier/claude-em-toolkit/1on1-prep)<a href="https://agentmods.dev/skills/aibier/claude-em-toolkit/1on1-prep"><img src="https://agentmods.dev/badge/skills/aibier/claude-em-toolkit/1on1-prep/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/aibier/claude-em-toolkit/1on1-prep"><img src="https://agentmods.dev/badge/skills/aibier/claude-em-toolkit/1on1-prep.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 | $0.00041 | $0.00609 |
| Opus 5.5 | $0.00016 | $0.00244 |
| Sonnet 5.5 | $0.00008 | $0.00122 |
| Haiku 4.5 | $0.00004 | $0.00061 |
Grade A, and why
1on1-prep 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 2mo 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
1:1 Prep & Feedback Drafting
Helps an EM walk into a 1:1 or write feedback prepared — grounded in actual recent work, not vague impressions. This skill drafts prompts for your own thinking and conversation, not verdicts about a person.
Steps: prepping for a 1:1
-
Pull recent, concrete signal about the person's work (with their knowledge — this is prep for a conversation with them, not surveillance):
- Recent PRs/commits (
git log --author=...), issues they closed/are working on (via the connected Jira/Linear MCP), code review comments they gave or received - Anything they flagged as a blocker recently (cross-reference
jira-progress-pulsehistory if available)
- Recent PRs/commits (
-
Turn that into talking points, not a performance review:
## 1:1 prep — <name> — <date> **Recent work worth acknowledging** - <specific, concrete — "shipped X" not "has been productive"> **Things to ask about** - <something that looked stuck, ambiguous, or like it might need support — framed as a question, not a conclusion> **Career/growth thread to follow up on** - <whatever's open from the last conversation — don't let this drop>
Steps: drafting written feedback
-
Ask what the feedback is for (review cycle? in-the-moment course correction? recognition?) — the right tone and specificity depend entirely on this, and getting it wrong is worse than not drafting at all.
-
Ground every point in a specific, verifiable instance — "in the auth refactor PR, you caught an edge case the rest of us missed" beats "good attention to detail." Vague feedback is unfalsifiable and unhelpful.
-
Draft, then explicitly hand it back for the EM to apply their own judgment about tone, relationship history, and timing — things no amount of context-gathering substitutes for. This is the clearest case in this whole toolkit where your domain knowledge — about this specific person, this relationship, this moment — has to be the final filter.
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.
- 2mo ago First seen · 58 lines · 41 tokens per session scan A ee31515cb553
1on1-prep is a skill published in the GitHub repository Aibier/claude-em-toolkit (5 stars, last pushed 5mo ago), licensed MIT. It adds 41 tokens to every session and 609 once invoked, about $0.0002 per session on Opus 5.5. 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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