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.
git clone --depth 1 https://github.com/ThibautBaissac/rails_ai_agentsWrote 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/commands/thibautbaissac/rails_ai_agents/report)<a href="https://agentmods.dev/commands/thibautbaissac/rails_ai_agents/report"><img src="https://agentmods.dev/badge/commands/thibautbaissac/rails_ai_agents/report/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/commands/thibautbaissac/rails_ai_agents/report"><img src="https://agentmods.dev/badge/commands/thibautbaissac/rails_ai_agents/report.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00021 | $0.00720 |
| Opus 5 | $0.00010 | $0.00360 |
| Sonnet 5 | $0.00004 | $0.00144 |
| Haiku 4.5 | $0.00002 | $0.00072 |
Grade A, and why
report 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.
How it starts
The opening of the file, as written. The whole thing — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Report: Sentry Error Summary
Generate a markdown report summarizing production error status for a given time period.
Input
$ARGUMENTS — optional period and environment filter.
Examples:
/sentry:report— default 14-day report, all environments/sentry:report 24h— last 24 hours/sentry:report 14d production— last 14 days, production only
Supported periods: 24h, 14d.
Workflow
-
Parse arguments: Extract the period (first argument, defaults to
7d) and optional environment (second argument). -
Gather data by calling the
list_issuesMCP tool three times:a. New/recent errors:
list_issues(query="is:unresolved", sort_by="new", date_range=period, environment=env, page_size=10)b. Top recurring errors:
list_issues(query="is:unresolved", sort_by="freq", date_range=period, environment=env, page_size=5)c. Recently resolved:
list_issues(query="is:resolved", sort_by="date", date_range=period, environment=env, page_size=10) -
Format the report as markdown:
## Sentry Error Report — {period} **Environment:** {environment or "all"} | **Generated:** {date} ### Overview - **Unresolved errors:** {count from new errors query} - **Resolved in period:** {count from resolved query} ### New Errors (most recent first) | Error | Level | Count | First Seen | Last Seen | |-------|-------|-------|------------|-----------| | [{short_id}]({permalink}) {title} | {level} | {count} | {first_seen} | {last_seen} | | ... | ... | ... | ... | ... | ### Top Recurring (by frequency) | Error | Count | Users Affected | |-------|-------|----------------| | [{short_id}]({permalink}) {title} | {count} | {user_count} | | ... | ... | ... | ### Recently Resolved | Error | Count | Resolved | |-------|-------|----------| | [{short_id}]({permalink}) {title} | {count} | {last_seen} | | ... | ... | ... | -
Output the report directly to the developer. Format dates as relative where helpful (e.g., "2 hours ago", "3 days ago").
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 · 72 lines · 21 tokens per session scan A 5ed8272ac28b
report is a command published in the GitHub repository ThibautBaissac/rails_ai_agents (663 stars, last pushed 3mo ago), licensed MIT. It adds 21 tokens to every session and 720 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 2026-09-03.
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