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/goal-prnpx skills add dyoshikawa/rulesync --skill goal-prgit 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 | $0.00074 | $0.01324 |
| Opus 5 | $0.00037 | $0.00662 |
| Sonnet 5 | $0.00015 | $0.00265 |
| Haiku 4.5 | $0.00007 | $0.00132 |
Grade A, and why
goal-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 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.
How it starts
The opening of the file, as written. The whole thing — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Goal PR
target_pr = the user's request
This skill drives a pull request all the way to merge. It repeatedly runs
the review-pr skill, fixes every finding of severity mid or above, and merges the PR
once a review round reports no mid-or-above findings.
0. Determine and Prepare the Target PR
-
If
target_pris provided (e.g.123,#123, or a PR URL), use it. -
Otherwise, look for the PR of the current branch:
gh pr view --json number,title,state,headRefName 2>/dev/null -
If no PR exists yet, create one (this satisfies the "PR is the goal" intent):
- Use the
commit-push-prskill to commit the current changes, push the branch, and open a PR. - Then resolve
target_prto the freshly created PR number.
- Use the
Confirm that the current local branch is the PR's head branch, because the fix phase below must commit and push fixes onto that branch. If they differ, ask the user how to proceed and stop.
1. Exit Condition
The loop exits when a single review round satisfies both of:
- 0 findings of severity
mid,high, orcritical(onlylowfindings, or none at all, may remain). - The PR's GitHub Actions checks are green — no check is
failorpending.
Set a hard safety cap of 10 iterations. If the exit condition is still not met at the cap, stop the loop and report the remaining findings (and any failing CI) to the user for a manual decision instead of merging.
2. Iteration Loop
Repeat the following until the exit condition is satisfied or the cap is hit.
2-1. Review Phase
Use the review-pr skill with target_pr. It assigns each finding a
severity (low / mid / high / critical) and a sequential number, and also
reports the GitHub Actions workflow status.
Note: the review-pr skill only reads remote state and must not switch the local branch.
Keep that constraint intact during the review phase.
2-2. Evaluate the Exit Condition
Use both the findings and the GitHub Actions status from the review result.
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 · 130 lines · 74 tokens per session scan A 4175774cea3c
goal-pr is a skill published in the GitHub repository dyoshikawa/rulesync (1,373 stars, last pushed 2d ago), licensed MIT. It adds 74 tokens to every session and 1,324 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 2026-08-30.
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