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.
git clone --depth 1 https://github.com/fourleafai/clover-publicWrote 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/commands/fourleafai/clover-public/prep-role)<a href="https://agentmods.dev/commands/fourleafai/clover-public/prep-role"><img src="https://agentmods.dev/badge/commands/fourleafai/clover-public/prep-role/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/commands/fourleafai/clover-public/prep-role"><img src="https://agentmods.dev/badge/commands/fourleafai/clover-public/prep-role.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 | $0.00000 | $0.00601 |
| Opus 5.5 | $0.00000 | $0.00240 |
| Sonnet 5.5 | $0.00000 | $0.00120 |
| Haiku 4.5 | $0.00000 | $0.00060 |
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 1mo 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 — 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
-
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. -
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.
-
Get the company if relevant. Optional. Adds flavor to step 5.
-
Call
get_role_intelligencewith the role id. This returns the structured pipeline, scoring rubric, and resume guidance. -
Call
explain_interview_formatwith role + seniority + company. This returns a grounded synthesis paragraph for "what to expect", "how to win", and "red flags". -
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'swhatToExpect. - 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.
- The pipeline. 2-3 sentences naming the typical rounds and what each tests. Pull from
-
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
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.
- 1mo ago First seen · 44 lines · 0 tokens per session scan A cd9fc264c8ca
prep-role is a command published in the GitHub repository fourleafai/clover-public (8 stars, last pushed 29d 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.
Other commands, from other repositories
explain
Explain the subject in depth using the fixed four-part structure: What, Why, Example, Consequences. Treat the subject as the thing to be explained, not as a literal question.
interview
Prepare for the interview from the filed application — predicted questions, STAR answers from real evidence, and gap-defence.
create-onboarding-guide
Command "create-onboarding-guide" from qdhenry/Claude-Command-Suite, covering create onboarding guide and instructions.
init-notebook
A command for creating a NotebookLM notebook containing documentation for a project and its technologies. NotebookLM is a research workspace that answers questions using sources added to a notebook.
ctx7
Quick Context7 Library Documentation Lookup.
diagram-coordinator
Coordinate with agents to generate comprehensive educational diagrams.