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 TechNickAI/ai-coding-config --skill skill-creatorgit clone --depth 1 https://github.com/TechNickAI/ai-coding-configWrote 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/technickai/ai-coding-config/skill-creator)<a href="https://agentmods.dev/skills/technickai/ai-coding-config/skill-creator"><img src="https://agentmods.dev/badge/skills/technickai/ai-coding-config/skill-creator/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/technickai/ai-coding-config/skill-creator"><img src="https://agentmods.dev/badge/skills/technickai/ai-coding-config/skill-creator.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 | $0.00029 | $0.01227 |
| Opus 5.5 | $0.00012 | $0.00491 |
| Sonnet 5.5 | $0.00006 | $0.00245 |
| Haiku 4.5 | $0.00003 | $0.00123 |
Grade A, and why
skill-creator 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 25d 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 — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Core principle: Trust the LLM's intelligence. Describe what needs to happen and why, not step-by-step how.
Skip skills for one-off solutions, well-documented standard practices, or project-specific conventions (use CLAUDE.md instead).
---
name: skill-name-with-hyphens
description: Use when [triggering conditions] - [what it does and how it helps]
---
# Skill Name
## Overview
What is this? Core principle in 1-2 sentences.
## When to Use
Clear triggers and symptoms. When NOT to use.
## Core Pattern
Show desired approach with examples. Describe alternatives in prose.
## Common Pitfalls
What goes wrong and how to avoid it.
Frontmatter requirements:
name: Letters, numbers, hyphens only. Use verb-first active voice (e.g., creating-skills not skill-creation).
description: Third-person, under 500 characters. Start with "Use when..." to describe triggering conditions, then explain what it does. Include concrete symptoms and situations, not just abstract concepts.
Good: "Use when tests have race conditions or pass/fail inconsistently - replaces arbitrary timeouts with condition polling for reliable async tests"
Good pattern:
// Use condition-based waiting for reliable async tests
await waitFor(() => element.textContent === "loaded");
await waitFor(() => user.isAuthenticated === true);
await waitFor(() => data.length > 0);
Then in prose: "Avoid arbitrary timeouts like setTimeout() which make tests brittle and slow."
Focus on goals, not process: Describe outcomes and constraints. Let the LLM figure out how to achieve them.
Good: "Ensure each test has a clear failure mode that identifies what's wrong. Tests should verify behavior, not implementation details."
Positive framing: Frame as "do this" not "avoid that." Focus on what success looks like.
Good: "Write minimal code to pass the test. Add features only when tests require them."
Trust intelligence: Assume the LLM can handle edge cases and variations. Specify boundaries, not decision trees.
Good: "Check if files exist before copying. If they differ, show changes and ask the user what to do."
With supporting files (when needed):
skill-name/
SKILL.md # Overview + patterns
reference.md # Heavy API docs (100+ lines)
tool-example.ts # Reusable code to adapt
Only separate files for heavy reference material (comprehensive API docs) or reusable tools (actual code to copy/adapt).
Keep inline: Principles and concepts, code patterns under 50 lines, everything else.
Put searchable terms in the description and throughout the content.
What ships with it
3 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.
- 25d ago First seen · 169 lines · 29 tokens per session scan A e547fd69f9a0
skill-creator is a skill published in the GitHub repository TechNickAI/ai-coding-config (24 stars, last pushed 25d ago), licensed MIT. It adds 29 tokens to every session and 1,227 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 2026-09-22.
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