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 RoyZhao1991/LingShu --skill create-skillgit clone --depth 1 https://github.com/RoyZhao1991/LingShuWrote 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/royzhao1991/lingshu/create-skill)<a href="https://agentmods.dev/skills/royzhao1991/lingshu/create-skill"><img src="https://agentmods.dev/badge/skills/royzhao1991/lingshu/create-skill/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/royzhao1991/lingshu/create-skill"><img src="https://agentmods.dev/badge/skills/royzhao1991/lingshu/create-skill.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 | $0.00043 | $0.00805 |
| Opus 5.5 | $0.00017 | $0.00322 |
| Sonnet 5.5 | $0.00009 | $0.00161 |
| Haiku 4.5 | $0.00004 | $0.00081 |
Grade A, and why
create-skill 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- create-skill — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create Skill
Interactively gather requirements from the user and create a fully working Grok skill on disk.
Step 1: Gather information
Ask the user the following questions one at a time as regular conversation questions (do NOT use structured option prompts for free-text inputs):
- Skill name - ask the user to type a name. Lowercase letters (a-z), digits (0-9), and hyphens (-) only. Must start and end with a letter or digit. Must be 2-64 characters long (e.g.
deploy-k8s). Validate the name before proceeding. - Scope - present the user with two options:
- Project (Recommended):
<repo-root>/.grok/skills/<name>/SKILL.md- available only in this repo, shareable with teammates - User:
~/.grok/skills/<name>/SKILL.md- available in all projects - Default to Project if inside a git repo, otherwise User.
- Project (Recommended):
- What it should do - ask the user to describe the workflow, paste an example prompt they keep repeating, or explain the task the skill should automate.
Step 2: Draft the description
Write a description frontmatter value that includes:
- What the skill does (1-2 sentences)
- Trigger phrases and keywords so Grok knows when to auto-invoke it
- The slash command name (e.g. "Use when the user runs /deploy-k8s")
Show the drafted description to the user and let them approve or edit it.
Step 3: Create the directory
Run this bash command to create the skill directory:
mkdir -p <SKILL_DIR>
Where <SKILL_DIR> is:
- User scope:
~/.grok/skills/<name> - Project scope:
<repo-root>/.grok/skills/<name>
If the skill needs helper scripts, also create <SKILL_DIR>/scripts/.
If the skill needs reference docs, also create <SKILL_DIR>/references/.
Step 4: Write SKILL.md
Use search_replace with an empty old_string to create the file at <SKILL_DIR>/SKILL.md.
The file MUST follow this exact format:
---
name: <skill-name>
description: <the description from Step 2>
---
<markdown body with instructions, steps, code blocks>
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 · 81 lines · 43 tokens per session scan A fead9243b902
create-skill is a skill published in the GitHub repository RoyZhao1991/LingShu (11 stars, last pushed 12d ago), licensed Apache-2.0. It adds 43 tokens to every session and 805 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 2026-08-30.
Other skills, from other repositories
backend-integrator
Complete guide for integrating a new LLM backend into MassGen. Use when adding a new provider (e.g., Codex, Mistral, DeepSeek) or when auditing an existing backend for missing integration points. Covers all 15 files that need touching.
massgen-log-analyzer
Run MassGen experiments and analyze logs using automation mode, logfire tracing, and SQL queries. Use this skill for performance analysis, debugging agent behavior, evaluating coordination patterns, and improving the logging structure, or whenever an ANALYSISREPORT.md is needed in a log directory.
massgen-develops-massgen
Guide for using MassGen to develop and improve itself. This skill should be used when agents need to run MassGen experiments programmatically (using automation mode) OR analyze terminal UI/UX quality (using visual evaluation tools). These are mutually exclusive workflows for different improvement goals.
model-registry-maintainer
Guide for maintaining the MassGen model and backend registry. This skill should be used when adding new models, updating model information (release dates, pricing, context windows), or ensuring the registry stays current with provider releases. Covers both the capabilities registry and the pricing/token manager.
release-prep
Prepare release documentation including CHANGELOG entry, announcement text, and validation. Run before tagging a new release.
serena
This skill provides symbol-level code understanding and navigation using Language Server Protocol (LSP). Enables IDE-like capabilities for finding symbols, tracking references, and making precise code edits at the symbol level.