creating-agents — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited creating-agents (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Agents are isolated agentic mini-loops with their own model, tool whitelist, and instruction file (AGENT.md). Use agents when you need to delegate a self-contained task to a separate LLM context — typically work that benefits from a different model, a restricted tool set, or clean isolation from the main conversation.
Use a skill (skills/) | Use an agent (agents/) |
|---|---|
| Instruction to load into the main context | Isolated task with its own model |
| No tool calls needed, or shares main tool set | Needs a restricted tool whitelist |
| Workflow guidance or persona adjustment | Sub-task that returns a single result |
Lives in skills/<name>/SKILL.md | Lives in agents/<name>/AGENT.md |
---
name: agent-name # lowercase letters, numbers, hyphens only
description: One sentence — when to invoke this agent and what it returns.
model: provider/model-id # required: the model this agent uses
tools: [tool1, tool2] # required: exact tool names the agent may call
max_iterations: 5 # optional: default is global max (25)
tags: [tag1, tag2] # optional
---
# Agent Name
(Instructions the agent follows. Starts with what it should do on first call.)
## Protocol
1. Step one
2. Step two
3. Return final answerTools must be the exact runtime names visible to the agent:
| Category | Name format | Example |
|---|---|---|
| Built-in | plain name | read_file, write_file, execute_command |
| Skill tools | plain name | read_skill_file, write_skill_file, reload_skills |
| Agent tools | plain name | read_agent_file, write_agent_file, reload_agents |
| MCP tools | mcp_{server}_{tool} | mcp_google-workspace_query_gmail_emails |
read_skill_file and read_agent_file are always available to every agent — no need to list them.
AGENT.md using write_agent_file: write_agent_file(agent_name="my-agent", relative_path="AGENT.md", content="...") reload_agents() invoke_agent(agent="my-agent", prompt="<test task>")invoke_agent(agent="agent-name", prompt="Task description here")Optional overrides for one-off invocations:
invoke_agent(agent="agent-name", prompt="...", model="anthropic/claude-sonnet-4-6", tools=["read_agent_file", "mcp_threads_post"])---
name: summariser
description: Summarises a block of text into 3 bullet points. Invoke when the user asks for a summary.
model: qwen/qwen3-235b-a22b
tools: []
max_iterations: 2
---
# Summariser
Read your instructions (already loaded), then summarise the text in the prompt into exactly 3 bullet points. Return only the bullets — no preamble.Check agents/ for current agents. Use reload_agents after any changes to activate them.
Skills with a model: field in skills/ still work via invoke_agent — the agent registry is checked first, then the skills registry. New agents should go in agents/.
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.