Rulesync is a command-line tool that generates configuration for different AI development tools from a shared set of rules. Developers use it to keep instructions, commands, MCP settings, ignore files, subagents, and skills consistent across coding agents. Catalogue skills support workflows built around Rulesync.
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 dyoshikawa/rulesync --skill explain-issuegit clone --depth 1 https://github.com/dyoshikawa/rulesyncWrote 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/dyoshikawa/rulesync/explain-issue)<a href="https://agentmods.dev/skills/dyoshikawa/rulesync/explain-issue"><img src="https://agentmods.dev/badge/skills/dyoshikawa/rulesync/explain-issue/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/dyoshikawa/rulesync/explain-issue"><img src="https://agentmods.dev/badge/skills/dyoshikawa/rulesync/explain-issue.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00018 | $0.00260 |
| Opus 5.5 | $0.00007 | $0.00104 |
| Sonnet 5.5 | $0.00004 | $0.00052 |
| Haiku 4.5 | $0.00002 | $0.00026 |
Grade A, and why
explain-issue 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 1mo 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.
What it actually says
target_issue = the user's request
If target_issue is not provided, ask the user which issue to explain.
Step 1: Gather Issue Information
Run the following in parallel:
- Get the issue description and metadata:
gh issue view <issue_number> - Get the issue comments:
gh issue view <issue_number> --comments
If the issue references related pull requests, commits, or files that are needed to understand the solution, gather that context as well.
Step 2: Analyze and Explain
Based on the issue content, explain the following two aspects:
- Background (Problem/Motivation): What problem, limitation, or user need does this issue describe? Why does it matter?
- Proposed Solution: What solution, direction, or next step is suggested in the issue discussion? Summarize the expected approach, scope, and important constraints if they are mentioned.
If the issue does not contain enough information about the solution, explicitly say that the solution is still undecided or unspecified.
Keep the explanation concise and focused. Use the language of the current conversation (follow the user's language).
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.
- 1mo ago First seen · 31 lines · 18 tokens per session scan A ec7240498c8a
explain-issue is a skill published in the GitHub repository dyoshikawa/rulesync (1,535 stars, last pushed today), licensed MIT. It adds 18 tokens to every session and 260 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 2026-08-30.
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resolve-publish
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code-task
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resolve-inputs
Select the Resolve mode, recover its input, check the workspace, and discover existing fixes.