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 review-prgit 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/review-pr)<a href="https://agentmods.dev/skills/dyoshikawa/rulesync/review-pr"><img src="https://agentmods.dev/badge/skills/dyoshikawa/rulesync/review-pr/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/review-pr"><img src="https://agentmods.dev/badge/skills/dyoshikawa/rulesync/review-pr.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 | $0.00061 | $0.00591 |
| Opus 5.5 | $0.00024 | $0.00236 |
| Sonnet 5.5 | $0.00012 | $0.00118 |
| Haiku 4.5 | $0.00006 | $0.00059 |
Grade A, and why
review-pr 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.
How it starts
The opening of the file, as written. The whole thing — 46 lines — stays where its author put it; the contents beside it link to each section on GitHub.
target_pr = the user's request
If target_pr is not provided, use the PR of the current branch.
Important: Do Not Switch the Local Branch
- Do NOT check out, switch, or otherwise move the local branch (e.g.,
git checkout,git switch,gh pr checkout). - To inspect the PR's changes, use
gitandghcommands that read remote state without moving the working tree. For example:gh pr view $target_prto read PR metadata and description.gh pr diff $target_prto read the diff.gh pr view $target_pr --json filesorgh apito list changed files.git fetch origin pull/<PR_NUMBER>/head:refs/remotes/origin/pr-<PR_NUMBER>andgit diff origin/main...origin/pr-<PR_NUMBER>if a local read-only ref is needed.
- When invoking subagents, explicitly instruct them that switching the local branch is forbidden and that they must inspect changes via
git/ghcommands only.
Execute the following in parallel:
- Call code-reviewer subagent to review the code changes in $target_pr. Pass along the rule that the local branch must not be switched and that diffs must be obtained via
git/ghcommands. - Call security-reviewer subagent to review the security issues in $target_pr. Pass along the rule that the local branch must not be switched and that diffs must be obtained via
git/ghcommands.
Integrate and report the execution results from each subagent. Additionaly, please output PR number in the result so that the user can easily find the PR.
Reporting Rules
- Assign a severity level to each finding: low, mid, high, or critical.
- Assign a sequential number to each finding (e.g., #1, #2, #3, ...).
After the Review: Check GitHub Actions Workflows
After completing the content review, check the status of the GitHub Actions workflows for $target_pr (for example, using gh pr checks or gh run list).
- Report the status of every workflow run to the user, including runs that are still in progress.
- For each failing workflow, also report:
- The likely cause of the failure (based on the relevant logs or job output).
- Candidate solutions or next steps to resolve it.
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 · 46 lines · 61 tokens per session scan A 43374b4848be
review-pr is a skill published in the GitHub repository dyoshikawa/rulesync (1,535 stars, last pushed today), licensed MIT. It adds 61 tokens to every session and 591 once invoked, about $0.0002 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.
Other skills, from other repositories
review-this-branch
Run the nohuman review gate (fresh-session adversarial reviewer + tamper guard) over the current branch or a GitHub pull request, with no server, no database, and no onboarding, and relay the pass/fail checklist.
arrow-maintenance
Navigation and audit overlay for linked-intent development. Use when working with docs/arrows/ — orienting via index.yaml, auditing spec-to-code coherence, detecting reverse orphans and drift, splitting/merging/renaming/re-parenting segments. Dual-mode: ambient guidance when the overlay is present…
review-depth
Experimental declared review depth for linked-intent development. Use when a LID project's instruction file declares a review depth in prose (e.g. "Review depth - I review through LLD; below that, consolidate to one review") or the user asks to batch phase reviews, consolidate stops, or let go below a phase depth.…
bidirectional-differential
Audit coherence across an arrow of intent by running two parallel fresh Claude sessions — one reconstructs code from a single EARS, the other reconstructs the EARS from stripped code — then classifies the drift between them. Use when the user invokes /differential-audit, asks to audit EARS-to-code drift for a feature…
python-style
Validate Python code against style and architectural conventions.
go-proverbs
Validate Go code changes against Go Proverbs.