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/understand-scrap-issuesnpx skills add dyoshikawa/rulesync --skill understand-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 | $0.00028 | $0.00431 |
| Opus 5 | $0.00014 | $0.00216 |
| Sonnet 5 | $0.00006 | $0.00086 |
| Haiku 4.5 | $0.00003 | $0.00043 |
Grade A, and why
understand-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 3d 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
Fetch the newest scrap issues (GitHub issues labeled maintainer-scrap) and understand what each one is about, so you can catch up on recently jotted-down notes before acting on them.
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.
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.
- 3d ago First seen · 45 lines · 28 tokens per session scan A bc7ad72b867c
understand-scrap-issues is a skill published in the GitHub repository dyoshikawa/rulesync (1,373 stars, last pushed 2d ago), licensed MIT. It adds 28 tokens to every session and 431 once invoked, about $0.0001 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.
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