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 Azure-Samples/eShopLite --skill ralph-two-pass-scangit clone --depth 1 https://github.com/Azure-Samples/eShopLiteWrote 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/azure-samples/eshoplite/ralph-two-pass-scan)<a href="https://agentmods.dev/skills/azure-samples/eshoplite/ralph-two-pass-scan"><img src="https://agentmods.dev/badge/skills/azure-samples/eshoplite/ralph-two-pass-scan.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.00298 |
| Opus 5 | $0.00000 | $0.00149 |
| Sonnet 5 | $0.00000 | $0.00060 |
| Haiku 4.5 | $0.00000 | $0.00030 |
Grade A, and why
ralph-two-pass-scan 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 8d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- ralph-two-pass-scan — 100% identical, 0 lines differ
What it actually says
Skill: Ralph — Two-Pass Issue Scanning
Confidence: high Domain: work-monitoring Last validated: 2026-03-24
Context
Cuts GitHub API calls from N+1 to ~7 per round (~72% reduction) by separating list scanning from full hydration. Addresses the scanning inefficiency described in issue #596.
Pattern
Pass 1 — Lightweight Scan
gh issue list --state open --json number,title,labels,assignees --limit 100
Skip hydration if ANY of these match:
| Condition | Skip reason |
|---|---|
assignees non-empty AND no status:needs-review |
Already owned |
Labels contain status:blocked or status:waiting-external |
Externally gated |
Labels contain status:done or status:postponed |
Closed loop |
Title matches stale/noisy pattern ([chore], [auto]) |
Low-signal |
Pass 2 — Selective Hydration
For each issue surviving Pass 1:
gh issue view <number> --json number,title,body,labels,assignees,comments,state
Then apply normal Ralph triage logic. Rule of thumb: hydrate ≤ 30% of scanned list. If more than 30% survive Pass 1, tighten filter rules.
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.
- 8d ago First seen · 36 lines · 0 tokens per session scan A 9acc41abe110
ralph-two-pass-scan is a skill published in the GitHub repository Azure-Samples/eShopLite (168 stars, last pushed 2mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 298 tokens. 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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