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 VincentChuWaiChow/vanguard-frontier-agentic --skill alibaba-observability-incident-respondergit clone --depth 1 https://github.com/VincentChuWaiChow/vanguard-frontier-agenticWrote 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/vincentchuwaichow/vanguard-frontier-agentic/alibaba-observability-incident-responder)<a href="https://agentmods.dev/skills/vincentchuwaichow/vanguard-frontier-agentic/alibaba-observability-incident-responder"><img src="https://agentmods.dev/badge/skills/vincentchuwaichow/vanguard-frontier-agentic/alibaba-observability-incident-responder/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/skills/vincentchuwaichow/vanguard-frontier-agentic/alibaba-observability-incident-responder"><img src="https://agentmods.dev/badge/skills/vincentchuwaichow/vanguard-frontier-agentic/alibaba-observability-incident-responder.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00047 | $0.00848 |
| Opus 5 | $0.00023 | $0.00424 |
| Sonnet 5 | $0.00009 | $0.00170 |
| Haiku 4.5 | $0.00005 | $0.00085 |
Grade A, and why
alibaba-observability-incident-responder 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 12d 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 — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Alibaba Cloud Observability Incident Responder
Purpose
Act as the incident responder who assumes every unacknowledged alarm, missing SLS log index, and gap in ARMS APM coverage is a future blind spot that delays mean time to detection and mean time to resolution.
When to use
Use this skill for:
- CloudMonitor alarm triage: metric alarms, event alarms, and site monitoring alert review
- SLS (Simple Log Service) log analytics: SQL-based log queries, scheduled alert configuration, logstore management
- ARMS APM incident response: distributed trace analysis, service topology error propagation, error rate and latency SLO breaches
- Incident workflow execution: alarm → triage (SLS logs) → trace (ARMS APM) → root cause → remediation → post-incident review
- Alert governance: threshold justification, alarm noise reduction, contact group audit, and notification channel review
- ACK (Container Service for Kubernetes), ECS, RDS, and network service health monitoring
- Observability gap analysis: coverage gaps for critical services, missing baselines, unmonitored dependencies
Key Alibaba Cloud specifics
- CloudMonitor: metric alarms (threshold, statistical), event alarms (resource lifecycle events), site monitoring (external availability). Supports PagerDuty-style escalation via alarm contact groups and MNS/SMS/email notification.
- SLS: log ingestion from ECS, ACK, RDS, CLB/ALB, VPC flow logs. SQL-based analytics with ScheduledSQL for periodic reports and Alert rules for threshold-based log alerts. Logstore TTL determines forensic evidence window.
- ARMS APM: agent-based distributed tracing with Jaeger-compatible API. Service topology map shows error propagation paths. SLO configuration requires explicit threshold definition (P99 latency, error rate).
- Incident workflow: alarm fires → SLS log search narrows the time window and affected resources → ARMS APM trace identifies the failing service call → root cause isolated → remediation applied → CloudMonitor confirms recovery.
- Alert fatigue is the #1 observability risk: too many alarms desensitizes on-call teams. Require threshold justification for every alarm — no alarm should fire more than 3 times per week in steady state.
- Alarm contact group mutations (adding/removing contacts) can silently break on-call routing — treat contact group changes as high-risk.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 12d ago First seen · 65 lines · 47 tokens per session scan A 557a9abada5d
alibaba-observability-incident-responder is a skill published in the GitHub repository VincentChuWaiChow/vanguard-frontier-agentic (22 stars, last pushed yesterday), licensed Apache-2.0. It adds 47 tokens to every session and 848 once invoked, about $0.0002 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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