OpenClaw Master Skills is a curated, regularly updated collection of skills that extends an AI personal assistant platform with capabilities such as research, browser automation, presentation creation, and prompt work. It is intended for people using OpenClaw or MyClaw.ai to give their agents additional tasks and workflows. The catalogue contains many skills and agents from this collection.
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 LeoYeAI/openclaw-master-skills --skill afrexai-recruiting-enginegit clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skillsWrote 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/leoyeai/openclaw-master-skills/afrexai-recruiting-engine)<a href="https://agentmods.dev/skills/leoyeai/openclaw-master-skills/afrexai-recruiting-engine"><img src="https://agentmods.dev/badge/skills/leoyeai/openclaw-master-skills/afrexai-recruiting-engine/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/leoyeai/openclaw-master-skills/afrexai-recruiting-engine"><img src="https://agentmods.dev/badge/skills/leoyeai/openclaw-master-skills/afrexai-recruiting-engine.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00033 | $0.04312 |
| Opus 5.5 | $0.00013 | $0.01725 |
| Sonnet 5.5 | $0.00007 | $0.00862 |
| Haiku 4.5 | $0.00003 | $0.00431 |
Grade A, and why
AI Recruiting Engine 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 — 535 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Recruiting Engine
You are an expert recruiting agent. You run the entire hiring lifecycle — from intake to offer acceptance — using structured frameworks, scoring rubrics, and data-driven decisions.
1. ROLE INTAKE FRAMEWORK
Before sourcing a single candidate, build a Role Blueprint:
role_blueprint:
title: "Senior Backend Engineer"
department: Engineering
reports_to: "VP Engineering"
headcount: 1
urgency: high | medium | low
business_case:
why_now: "Scaling API layer for enterprise launch"
cost_of_vacancy: "$45K/month in delayed revenue"
success_metric: "API throughput 3x within 6 months"
must_haves: # Hard requirements — non-negotiable
- "Distributed systems design (3+ production systems)"
- "Go or Rust in production"
- "Experience with >10K RPS systems"
nice_to_haves: # Differentiators — not filters
- "Open source contributions"
- "Conference speaking"
- "Prior startup experience"
anti_patterns: # Explicit disqualifiers
- "Cannot work async (team is distributed)"
- "Needs heavy management oversight"
compensation:
base_range: "$180K-$220K"
equity: "0.05-0.1%"
bonus: "15% target"
flexibility: "Remote-first, async"
interview_stages:
- { name: "Screen", owner: "Recruiter", duration: "30min" }
- { name: "Technical Deep-Dive", owner: "Staff Eng", duration: "60min" }
- { name: "System Design", owner: "VP Eng", duration: "60min" }
- { name: "Values & Culture Add", owner: "Cross-functional", duration: "45min" }
timeline:
sourcing_start: "Week 1"
first_interviews: "Week 2"
offer_target: "Week 4-5"
Intake Questions to Ask Hiring Manager
- What does "great" look like in 90 days? In 1 year?
- Who's the best person you've worked with in this role — what made them great?
- What's the #1 reason someone would fail in this role?
- What's the honest pitch? Why would an A-player leave their current job for this?
- What's non-negotiable vs "we'll teach them"?
- What's the interview panel's availability for the next 4 weeks?
What ships with it
2 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.
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 · 535 lines · 33 tokens per session scan A d9e147cf8cb9
AI Recruiting Engine is a skill published in the GitHub repository LeoYeAI/openclaw-master-skills (2,168 stars, last pushed today), licensed MIT. It adds 33 tokens to every session and 4,312 once invoked, about $0.0001 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 2026-09-03.
Other skills, from other repositories
cc-skill-project-guidelines-example
Project Guidelines Skill (Example).
artifact-yylo
Capture and retrieve durable YYLO Ledger artifact Records with intentional profiles, payload modes, provenance, retention, and secret-safe immutable evidence.
task-tracker
A task-list workflow that stores tasks in a Markdown file, a plain-text file format commonly used for checklists.
lark-workflow-meeting-summary
A workflow for collecting Feishu meeting notes from a chosen time range and turning them into a structured report. Feishu meeting notes are records created from workplace meetings.
gogopowermanager
Orchestrates and ships a milestone, release, issue set, or multi-workstream engineering goal through one manager and bounded parallel workers. Use when the user asks to manage a milestone, build a dependency graph, execute independent issues in parallel, coordinate threads, sessions, subagents, or worktrees, recover a…
topydo
Manages todo.txt tasks using topydo CLI. Adds, lists, completes, prioritizes, tags, and organizes tasks with dependencies, due dates, recurrence, and projects. Use for any task management, todo lists, or when the user mentions tasks, todos, or todo.txt. Requires Python 3 and pip. Works on macOS, Linux, and Windows.