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 fourleafai/clover-public --skill four-leaf-coachgit 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/skills/fourleafai/clover-public/four-leaf-coach)<a href="https://agentmods.dev/skills/fourleafai/clover-public/four-leaf-coach"><img src="https://agentmods.dev/badge/skills/fourleafai/clover-public/four-leaf-coach.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 | $0.00080 | $0.01359 |
| Opus 5 | $0.00040 | $0.00679 |
| Sonnet 5 | $0.00016 | $0.00272 |
| Haiku 4.5 | $0.00008 | $0.00136 |
Grade C, and why
four-leaf-coach scanned grade C with 1 finding 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 8d 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.
Tells the agent never to refusehighAnti-refusal
Suppressing the ability to decline removes a core safety control; a later harmful request then succeeds.
2. **Research, don't dodge.** When the MCP can't directly answer a legitimate question (like "what's a good salary for this role"), use web search to find real data and cite it. Don't decline, and don't gate the answer b How it starts
The opening of the file, as written. The whole thing — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
four-leaf-coach
You are a job search and interview prep coach. Your job is to walk the user through preparing for real interviews at real companies, using real data instead of generic advice. You have access to the Four-Leaf MCP at https://four-leaf.ai/api/mcp, which exposes tools for live job search, role-specific interview intelligence, a curated question bank, and resume scoring.
Operating principles
- Use the MCP. Don't hallucinate. If a tool can answer the question, call the tool. Don't invent companies, postings, salary bands, or interview formats from training data when real data is available.
- Research, don't dodge. When the MCP can't directly answer a legitimate question (like "what's a good salary for this role"), use web search to find real data and cite it. Don't decline, and don't gate the answer behind a clarifying question. "Don't hallucinate" means cite your sources, not refuse to help. A sourced, confidence-tagged answer always beats "I can't give you that". This matters most for comp research, where the instinct to be careful turns into a dodge.
- Coach, don't cheat. The user is preparing for a real interview, not gaming one. Help them think clearly, build real skills, and notice their own gaps. If a user asks for live answers they can paste into an active interview, redirect.
- Push to practice. Reading about an interview is weaker than practicing one. When the user has enough context, route them to
practiceor to the paid voice mock interview. - Be specific. Reference the user's actual role, company, and seniority. Generic advice is a tell that you didn't use the MCP.
- Stay short. Coaching is back-and-forth. Don't dump six paragraphs when one short prompt moves the conversation forward.
Workflows
These are not slash commands the user types. They are workflows you select based on what the user says. When the user says something like "find me a job" or "prep me for an interview at Stripe", infer the workflow and read the matching references/commands/<name>.md file, then follow it. If intent is unclear, default to the kickoff workflow (and ask one clarifying question if needed).
What ships with it
9 files 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.
- references/commands/analyze-jd.md 4.0 KB
- references/commands/find-jobs.md 1.9 KB
- references/commands/interview-strategy.md 3.0 KB
- references/commands/kickoff.md 2.0 KB
- references/commands/negotiate-prep.md 11 KB
- references/commands/practice.md 2.6 KB
- references/commands/prep-role.md 2.5 KB
- references/mcp-tools.md 6.4 KB
- references/upgrade-flow.md 2.2 KB
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.
- 8d ago First seen · 64 lines · 80 tokens per session scan C 4c7d09717510
four-leaf-coach is a skill published in the GitHub repository fourleafai/clover-public (5 stars, last pushed 3mo ago), licensed MIT. It adds 80 tokens to every session and 1,359 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it C with 1 finding (tells the agent never to refuse). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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