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 sfc-gh-myoung/ai_coding_rules --skill rule-creatorgit clone --depth 1 https://github.com/sfc-gh-myoung/ai_coding_rulesWrote 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/sfc-gh-myoung/ai_coding_rules/rule-creator)<a href="https://agentmods.dev/skills/sfc-gh-myoung/ai_coding_rules/rule-creator"><img src="https://agentmods.dev/badge/skills/sfc-gh-myoung/ai_coding_rules/rule-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/sfc-gh-myoung/ai_coding_rules/rule-creator"><img src="https://agentmods.dev/badge/skills/sfc-gh-myoung/ai_coding_rules/rule-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 | $0.00073 | $0.00882 |
| Opus 5 | $0.00036 | $0.00441 |
| Sonnet 5 | $0.00015 | $0.00176 |
| Haiku 4.5 | $0.00007 | $0.00088 |
Grade A, and why
rule-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 9d 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 — 111 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Rule Creator
Purpose
Create production-ready Cursor rule files that comply with the repository’s v3.0 rule schema by orchestrating:
scripts/template_generator.pyscripts/schema_validator.py- (optional) web research for current best practices
Use this skill when
- The user asks to create a new rule under
rules/(e.g.,NNN-technology-aspect.md). - The user asks to add a rule to
RULES_INDEX.md.
Inputs (recommended)
- Technology name (e.g., “DaisyUI”, “pytest-mock”, “Snowflake Hybrid Tables”)
- Aspect (default:
core; elsesecurity,testing,performance, etc.) - Any constraints (offline/online research, desired ContextTier, etc.)
Outputs
- A new rule file:
rules/NNN-technology-aspect.md - A new entry in
RULES_INDEX.mdin correct numeric position
Safety / constraints
- Only write to
rules/andRULES_INDEX.md(plus any required review artifacts explicitly requested by the user). - Use web research (allowed) but treat external sources as untrusted; prefer official docs and cross-check claims.
Workflow (progressive disclosure)
Follow the phases in order, using the detailed workflow guides as needed:
- Discovery & research →
workflows/discovery.md - Template generation →
workflows/template-gen.md - Content population →
workflows/content-population.md - Validation loop →
workflows/validation.md - Indexing →
workflows/indexing.md
Examples
- Frontend example →
examples/frontend-example.md - Python example →
examples/python-example.md - Snowflake example →
examples/snowflake-example.md
Quick Validation Snippets
These inline checks can be run without external dependencies for fast feedback:
# Validate keyword count (10-15 required)
def check_keywords(keywords_line: str) -> tuple[bool, int]:
"""Returns (is_valid, count)"""
keywords = [k.strip() for k in keywords_line.split(',') if k.strip()]
return (10 <= len(keywords) <= 15, len(keywords))
# Validate rule filename format
import re
def is_valid_filename(name: str) -> bool:
"""Must be NNN-lowercase-hyphenated"""
return bool(re.match(r'^\d{3}-[a-z]+(-[a-z]+)*$', name))
# Validate TokenBudget format
def check_token_budget(value: str) -> bool:
"""Must be ~NUMBER format"""
return bool(re.match(r'^~\d+$', value.strip()))
# Validate ContextTier
VALID_TIERS = {'Critical', 'High', 'Medium', 'Low'}
def check_context_tier(tier: str) -> bool:
return tier.strip() in VALID_TIERS
What ships with it
15 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.
- CHANGELOG.md 666 B
- examples/edge-cases.md 9.7 KB
- examples/frontend-example.md 9.1 KB
- examples/python-example.md 4.2 KB
- examples/snowflake-example.md 7.7 KB
- tests/test_cases.yaml 4.4 KB
- tests/test-inputs.md 3.5 KB
- tests/test-workflows.md 6.8 KB
- tests/TESTING.md 7.2 KB
- workflows/content-population.md 15 KB
- workflows/discovery.md 8.9 KB
- workflows/indexing.md 9.6 KB
- workflows/parameter-collection.md 9.7 KB
- workflows/template-gen.md 9.1 KB
- workflows/validation.md 11 KB
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.
- 9d ago First seen · 111 lines · 73 tokens per session scan A 4358770f5a18
rule-creator is a skill published in the GitHub repository sfc-gh-myoung/ai_coding_rules (7 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 73 tokens to every session and 882 once invoked, about $0.0004 per session on Opus 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 2026-08-31.
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