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 agentmods add skills/dyoshikawa/rulesync/resolve-scrap-issuesnpx skills add dyoshikawa/rulesync --skill resolve-scrap-issuesgit clone --depth 1 https://github.com/dyoshikawa/rulesyncWhat 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 | $0.00044 | $0.01585 |
| Opus 5 | $0.00022 | $0.00792 |
| Sonnet 5 | $0.00009 | $0.00317 |
| Haiku 4.5 | $0.00004 | $0.00159 |
Grade A, and why
resolve-scrap-issues 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 2d 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 — 132 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fetch the newest scrap issues (GitHub issues labeled maintainer-scrap), understand each one, validate whether it is still a real and actionable problem using web research and codebase inspection, and then drive each issue to closure: close the issues that need no action, and resolve the actionable ones — bundling them into a single pull request when there is more than one.
This skill extends understand-scrap-issues: Steps 1–3 are the same intake flow, and Steps 4 onward add validation and resolution.
Step 1: Fetch the Latest Scrap Issues
List the most recent open issues that carry the maintainer-scrap label. gh issue list returns issues in creation order (newest first), so limiting to 3 yields the 3 newest:
gh issue list --label maintainer-scrap --state open --limit 3 --json number,title,url,createdAt
If the result is empty, report that there are no open scrap issues and stop.
Step 2: Gather Each Issue's Content
For each scrap issue returned, read both the body and the discussion. Run these in parallel across the issues where practical:
gh issue view <issue_number>
gh issue view <issue_number> --comments
If an issue references related pull requests, commits, or files that are needed to understand it, gather that context as well.
Step 3: Understand and Summarize
For each scrap issue, explain the following based on its content:
- Topic: A one-line summary of what the scrap note is about.
- Background: The context, motivation, or problem the note captures and why it matters.
- Details / Findings: The specific observations, problems, or content recorded in the note.
- Proposed Solution / Next Steps: Any solution or actionable next step mentioned. If none is recorded, state explicitly that it is still undecided.
Keep each summary concise and focused. Present the issues in the order returned (newest first), with the issue number, title, and URL as a heading for each.
Step 4: Validate Each Issue
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.
- 2d ago First seen · 132 lines · 44 tokens per session scan A c38b66e0bc2b
resolve-scrap-issues is a skill published in the GitHub repository dyoshikawa/rulesync (1,373 stars, last pushed yesterday), licensed MIT. It adds 44 tokens to every session and 1,585 once invoked, about $0.0002 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 2026-08-30.
Other skills, from other repositories
issue-triage
Issue triage: audit open issues, categorize, detect duplicates, cross-ref PRs, risk assessment, post comments. Args: "all" for deep analysis of all, issue numbers to focus (e.g. "42 57"), "en"/"fr" for language, no arg = audit only in French.
repo-recap
Generate a comprehensive repo recap (PRs, issues, releases) for sharing with team. Pass "en" or "fr" as argument for language (default fr).
create-issue
Draft and submit a GitHub issue from a user idea or bug description, with bilingual body and correct labels.
coordinate
Coordinate a small team of Qwen Code teammates with enforced read-only workers, an optional worktree-pinned writer, shared tasks, peer messages, and existing Agent View tabs. Invoke explicitly with /coordinate.
gh-issues
Use when creating, triaging, or commenting on GitHub issues for the Kilo VS Code extension or JetBrains plugin via gh. Covers issue templates, project board assignment, title conventions, and required gh scopes.
run-task
Execute a single Todo task through In Progress to Review, meeting every acceptance criterion with tests and vibe-lint checks. Refuses Planning-status tasks. Invoked as /agiflow:run-task . Uses gettask, updatetask, createtaskcomment.