prep-role

prep-role is a command for coding agents from fourleafai/clover-public. It costs 0 tokens per session (601 once invoked), scanned A, original, MIT.

A guided command for preparing for an interview for a specific job role. It uses role information and interview-format details to explain what to expect and how candidates are assessed.

In plain words
What is it for?
Use it to review the interview stages, understand evaluation criteria, improve your resume, and identify preparation priorities and common mistakes.
Why use it?
It replaces broad interview advice with preparation focused on the chosen role, seniority, and optionally a company.

Command

Part of the four-leaf plugin — 1 skill, 7 commands, 2 MCP servers shipped together

Install

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.

agentmods
npx agentmods add commands/fourleafai/clover-public/prep-role
Clone the repo
git clone --depth 1 https://github.com/fourleafai/clover-public

Or install four-leaf, the plugin that ships this one along with the rest of its 1 skill, 7 commands, 2 MCP servers.

Wrote 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.

agentmods badge for prep-role

README.md
[![agentmods](https://agentmods.dev/badge/commands/fourleafai/clover-public/prep-role.svg)](https://agentmods.dev/commands/fourleafai/clover-public/prep-role)
Your own site
<a href="https://agentmods.dev/commands/fourleafai/clover-public/prep-role"><img src="https://agentmods.dev/badge/commands/fourleafai/clover-public/prep-role.svg" alt="Measured on agentmods" height="20"></a>
Per session 0 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 601 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00000 $0.00601
Opus 5 $0.00000 $0.00300
Sonnet 5 $0.00000 $0.00120
Haiku 4.5 $0.00000 $0.00060

Measured 3d ago against content hash cd9fc264c8ca, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

prep-role 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 3d 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.

skills/four-leaf-coach/references/commands/prep-role.md · 44 lines

How it starts

The opening of the file, as written. The whole thing — 44 lines — stays where its author put it; the contents beside it link to each section on GitHub.

prep-role

Deep prep for a specific role's interview process. The user wants to know what to expect, how to win, and what kills candidates at their level.

When to run

  • User says things like "what's a senior data scientist interview at Anthropic like?" or "I'm prepping for a staff engineering loop".
  • User names a specific role and wants pipeline + scoring detail rather than open-ended strategy.

Flow

  1. Resolve the role. If the user named a role, validate against list_roles. If it's not in the catalog, suggest the closest match and confirm before continuing.

  2. Pick a seniority if the user didn't give one. Ask:

    Are you targeting entry, mid, senior, or staff? Calibration matters for what's coming.

  3. Get the company if relevant. Optional. Adds flavor to step 5.

  4. Call get_role_intelligence with the role id. This returns the structured pipeline, scoring rubric, and resume guidance.

  5. Call explain_interview_format with role + seniority + company. This returns a grounded synthesis paragraph for "what to expect", "how to win", and "red flags".

  6. Present in this order, conversationally:

    • The pipeline. 2-3 sentences naming the typical rounds and what each tests. Pull from get_role_intelligence.
    • What to expect at this seniority. From explain_interview_format's whatToExpect.
    • How to win at this seniority. From howToWin. Be prescriptive.
    • Red flags to avoid. From redFlags. Be specific.
    • Scoring rubric. Name the 5 dimensions evaluators score on. Pulled from get_role_intelligence.
    • Resume guidance. 2-3 bullets on what resumes for this role need. Pulled from get_role_intelligence.
  7. Route forward. End with one specific next step, phrased as a natural offer (not a command for the user to type):

    Say the word and I'll generate practice questions tailored to this role. Paste a JD when you're ready and I'll score your resume against it.

Edge cases

Read the full file on GitHub · 44 lines

Changes

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.

  1. 3d ago First seen · 44 lines · 0 tokens per session scan A cd9fc264c8ca

Subscribe to this mod's changes

prep-role is a command published in the GitHub repository fourleafai/clover-public (5 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 601 tokens. 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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