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 commands/thibautbaissac/rails_ai_agents/monitorgit 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/monitor)<a href="https://agentmods.dev/commands/thibautbaissac/rails_ai_agents/monitor"><img src="https://agentmods.dev/badge/commands/thibautbaissac/rails_ai_agents/monitor.svg" alt="Measured on agentmods" 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 | $0.00012 | $0.00547 |
| Opus 5 | $0.00006 | $0.00273 |
| Sonnet 5 | $0.00002 | $0.00109 |
| Haiku 4.5 | $0.00001 | $0.00055 |
Grade A, and why
monitor 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 yesterday.
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 — 56 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Monitor: Single Monitoring Cycle
Execute one monitoring cycle: check for new errors, analyze them, and propose fixes.
Input
$ARGUMENTS — optional environment filter (e.g., /sentry:monitor production).
Workflow
-
Check for new errors: Call the
check_new_errorsMCP tool.- If
$ARGUMENTSis not empty, pass it as theenvironmentparameter. - If the response contains a
warningabout state file corruption, inform the developer.
- If
-
If no new errors: Report "No new errors detected since last check" and stop.
-
For each new error (process up to 5 per cycle):
a. Call
get_issue_detailwithinclude_pii=Falseto get the error details.b. Call
map_stacktraceto find which local files correspond to the stack trace.c. For each mapped local file with confidence "exact" or "partial", read the file around the relevant line number to understand the code context.
d. Analyze the error: Based on the exception type, error message, stack trace, and local code context, identify the likely root cause and propose a fix.
e. If the error context seems insufficient due to PII redaction (e.g., the error message references user input that was stripped), note: "Additional context may be available by re-querying with
get_issue_detailusinginclude_pii=True." -
Present fix proposals: For each analyzed error, output a structured proposal:
### Error: [title] (Sentry [short_id]) **Root cause**: [explanation] **Affected files**: [list of local files] **Suggested fix**: [description of what to change] **Code change**: [show the specific code diff or change needed] > Launch a background agent to implement this fix? Use: `/sentry:fix-error [issue_id] [brief fix description]` -
Summary: Report total errors checked, proposals generated, and remind the developer they can use
/sentry:fix-errorto launch isolated fix experiments.
Notes
- Never use
print()in any MCP tool calls — use the Context logging methods. - Limit analysis to 5 errors per cycle to avoid overwhelming the developer.
- Always check stack trace mapping confidence before reading local files — skip "unmapped" frames.
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.
- yesterday First seen · 56 lines · 12 tokens per session scan A f0bbddfe3e2c
monitor is a command published in the GitHub repository ThibautBaissac/rails_ai_agents (659 stars, last pushed 3mo ago), licensed MIT. It adds 12 tokens to every session and 547 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.