Create Agent Skills
ListedGenerates new, functional agent skills based on provided descriptions and desired capabilities for cfircoo agents.
Install on your platform
Showing Claude Code. One command installs this skill into any of the agents below.
manage-skills, not the name it is listed under, so the command below uses that name.- 1
Run this in your project
terminal$ npx skills add cfircoo/claude-code-toolkit --skill manage-skills -a claude-code Uses the open-source
skillsCLI and needs Node.js. Add-gto install it for your user instead of this project.
What we know about this skill
Publisher
cfircoo
Taken from the source listing. We haven't verified who controls this account.
Install target
Live repository
Not pinned. Installing fetches whatever the repository holds at the time you run the command.
Automated safety checks
This skill failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.
No credential access: failed
Touches credential material in 6 places.
Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.
What it found (3 of 6)
- skills/create-agent-skills/references/api-security.md:74environment file outside the project
grep -q "YOURSERVICE_MAIN_API_KEY=" ~/.claude/.env 2>/dev/null || \ - skills/create-agent-skills/references/api-security.md:75environment file outside the project
echo -e "\n# Your Service - Main profile\nYOURSERVICE_MAIN_API_KEY=\nYOURSERVICE_MAIN_ACCOUNT_ID=" >> ~/.claude/.env - skills/create-agent-skills/references/api-security.md:77environment file outside the project
echo "Added credential placeholders to ~/.claude/.env - user needs to fill them in"
Declares what it does: passed
SKILL.md parses and declares a name and a description.
SKILL.md exists, its frontmatter parses, and it declares a name and a description.
No undeclared network calls: passed
Makes no network calls.
Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.
No obfuscated payloads: passed
No encoded or obfuscated payloads.
No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.
No pipe-to-shell installers: passed
No pipe-to-shell or download-and-run installers.
No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.
Pinned to a commit: passed
Scanned cfircoo/claude-code-toolkit at d6264305dc.
The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.
- Commit
- d6264305dc
- Scanned
- 25 files, 6,028 lines
About this skill
What it does
This skill allows AI agents to generate custom skills for other agents. It can define the skill's purpose, inputs, and outputs, effectively creating a modular system of agent capabilities. The generated skill definitions are designed to be easily integrated into an agent platform.
When to use it
- Expanding Agent Functionality: You need your AI agent to perform tasks beyond its current skillset.
- Creating Specialized Agents: You want to build agents with very specific, narrow functions.
- Automating Skill Development: You have a repetitive process for defining new agent skills and want to automate it.
- Modular Agent Architectures: You’re building an agent system where components can be easily swapped in and out.
Key capabilities
- Skill definition generation
- Input/Output specification
- Purpose articulation
- Agent capability modularization
Example prompts
- "Create a skill for an agent that summarizes customer support tickets."
- "Define a new skill to extract key data points from financial reports."
- "Generate a skill that translates English text into Spanish and then calculates the sentiment score."
Tips & gotchas
The quality of the generated skill definition depends heavily on the clarity and specificity of your prompt. Providing detailed examples or outlining expected behavior will yield better results.