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 G1Joshi/Agent-Skills --skill sentrygit clone --depth 1 https://github.com/G1Joshi/Agent-SkillsWrote 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/g1joshi/agent-skills/sentry)<a href="https://agentmods.dev/skills/g1joshi/agent-skills/sentry"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/sentry.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 5.1 | $0.00016 | $0.00384 |
| Opus 5 | $0.00008 | $0.00192 |
| Sonnet 5 | $0.00003 | $0.00077 |
| Haiku 4.5 | $0.00002 | $0.00038 |
Grade A, and why
sentry 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 4d 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
Sentry
Sentry provides self-hosted and cloud-based error monitoring. In 2025, it excels at Performance Monitoring and identifying AI Model hallucinations or errors.
When to Use
- Error Tracking: "My app crashed, what is the stack trace?"
- Frontend Performance: Web Vitals monitoring (LCP, FID).
- Release Tracking: Associate errors with specific git commits/releases.
Quick Start
import * as Sentry from "@sentry/node";
Sentry.init({
dsn: "https://[email protected]/0",
tracesSampleRate: 1.0,
});
try {
myFunction();
} catch (e) {
Sentry.captureException(e);
}
Core Concepts
Issues
Aggregated groups of events. Sentry expects 1000 events of "NullPointerException" to be grouped into 1 Issue.
Releases
Tying code versions to errors. Sentry can tell you "This error started in Release v3.4".
Distributed Tracing
Connects frontend errors to backend bottlenecks.
Best Practices (2025)
Do:
- Upload Source Maps: Essential for JS/TS debugging.
- Use
ignoreErrors: Filter out noise (like "Network Error" when user is offline) in the SDK config. - Use Session Replay: Video-like reproduction of user actions leading up to an error.
Don't:
- Don't log PII: Sanitize data before sending. Sentry has scrubbers, but do it client-side too.
References
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.
- 4d ago First seen · 62 lines · 16 tokens per session scan A d5170453251d
sentry is a skill published in the GitHub repository G1Joshi/Agent-Skills (12 stars, last pushed 6mo ago), licensed MIT. It adds 16 tokens to every session and 384 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 skills, from other repositories
performance-optimization
Optimizes application performance across frontend, backend, queries, and databases. Use when performance requirements exist, when you suspect performance regressions, when Core Web Vitals or load times need improvement, when N+1 query patterns need fixing, or when profiling reveals bottlenecks.
doubt-driven-development
Subjects every non-trivial decision to a fresh-context adversarial review before it stands. Use when correctness matters more than speed, when working in unfamiliar code, when stakes are high (production, security-sensitive logic, irreversible operations), or any time a confident output would be cheaper to verify now…
debugging-and-error-recovery
Guides systematic root-cause debugging. Use when tests fail, builds break, behavior doesn't match expectations, or you encounter any unexpected error. Use when you need a systematic approach to finding and fixing the root cause rather than guessing.
graphmind
Before using Grep, find, rg, ag, or reading files to understand code: You MUST query graphmind first. Only fall back to grep/find if graphmind cannot answer (e.g., string literals, config values, non-code patterns).
component-flattening-analysis
Detects misplaced classes and fixes component hierarchy problems — finds code that should belong inside a component but sits at the root level. Use when asking "clean up component structure", "find orphaned classes", "fix module hierarchy", "flatten nested components", or analyzing why namespaces have misplaced code.…
codenavi
Your pathfinder for navigating unknown codebases. Investigates with precision, implements surgically, and never assumes — if it doesn't know, it says so. Maintains a .notebook/ knowledge base that grows across sessions, turning every discovery into lasting intelligence. Summons available skills, MCPs, and docs when…