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 gabrielmoreira/agent-skills-mirror --skill create-skillgit clone --depth 1 https://github.com/gabrielmoreira/agent-skills-mirrorWrote 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/gabrielmoreira/agent-skills-mirror/create-skill)<a href="https://agentmods.dev/skills/gabrielmoreira/agent-skills-mirror/create-skill"><img src="https://agentmods.dev/badge/skills/gabrielmoreira/agent-skills-mirror/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/gabrielmoreira/agent-skills-mirror/create-skill"><img src="https://agentmods.dev/badge/skills/gabrielmoreira/agent-skills-mirror/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 | $0.00129 | $0.04143 |
| Opus 5 | $0.00064 | $0.02072 |
| Sonnet 5 | $0.00026 | $0.00829 |
| Haiku 4.5 | $0.00013 | $0.00414 |
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 6d 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.
This is a copy
100% identical to create-skill — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 437 lines — stays where its author put it; the contents beside it link to each section on GitHub.
List Affitor Skill
Turn a repeatable AI prompt or workflow into a structured, shareable skill for the
affiliate-skills GitHub repository.
The output is a complete SKILL.md file that works in any AI agent — shared via
npx skills add Affitor/affiliate-skills so anyone can install it.
Stage
This skill belongs to Stage S8: Meta
When to Use
- User has a prompt they keep reusing and wants to turn it into a shareable skill
- User wants to create a new skill for the affiliate-skills repository
- User wants to write a SKILL.md file in the standard format
- User says "make this a skill" or "write a skill for X"
- User wants to package an AI workflow so others can replicate it
Input Schema
{
raw_prompt: string # (required) The prompt, workflow description, or detailed explanation of what the skill does
failure_modes: string # (optional) What goes wrong when the output is bad — helps write better Instructions and Error Handling
niche: string # (optional) Category hint, e.g., "content", "research", "seo"
examples: string # (optional) Example input/output pairs the user already has
}
Workflow
Step 1: Understand What the Prompt Actually Does
Before writing anything, analyze the user's raw prompt or workflow description:
- Task type — Is this content creation, research, analysis, planning, automation, or something else?
- Variable inputs — What changes each time? (product name, URL, audience, topic, etc.)
- Fixed structure — What stays the same? (output format, sections, tone, constraints)
- Quality differentiator — What makes a good output vs. a bad one?
- Failure modes — Where does the AI tend to go wrong without explicit guidance?
If the user gave a vague description instead of an actual prompt, ask:
- "What do you typically paste into ChatGPT/Claude for this?"
- "What does the output look like when it works well?"
- "What goes wrong when it doesn't?"
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.
- 6d ago First seen · 437 lines · 129 tokens per session scan A 43f40d822c59
create-skill is a skill published in the GitHub repository gabrielmoreira/agent-skills-mirror (17 stars, last pushed yesterday), licensed MIT. It adds 129 tokens to every session and 4,143 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to create-skill, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
create-skill
Turn a repeatable AI prompt or workflow into a structured, shareable skill for the affiliate-skills GitHub repository. Use this skill when the user wants to create a new skill, write a SKILL.md, convert a prompt to a skill, share a skill via the GitHub repo, or document an AI workflow. Also trigger for: "create a…
list-affitor-skill
Turn a repeatable AI prompt or workflow into a structured, publish-ready skill for list.affitor.com. Use this skill when the user wants to create a new skill, write a SKILL.md, convert a prompt to a skill, publish a skill to the directory, or document an AI workflow. Also trigger for: "create a skill", "write a…
sentry-fix-issues
Find and fix issues from Sentry using MCP. Use when asked to fix Sentry errors, debug production issues, investigate exceptions, or resolve bugs reported in Sentry. Methodically analyzes stack traces, breadcrumbs, traces, and context to identify root causes.
pentest-playbook
7-phase pentest pipeline from passive recon to exploitation.
recon-playbook
Use when starting or restructuring an authorized external web and API assessment.
Chain Builder
Build and execute multi-step prompt chains for complex tasks.