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 skills/gemini-cli-extensions/sre/investigation-entrypointnpx skills add gemini-cli-extensions/sre --skill investigation-entrypointgit clone --depth 1 https://github.com/gemini-cli-extensions/sreWrote 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/gemini-cli-extensions/sre/investigation-entrypoint)<a href="https://agentmods.dev/skills/gemini-cli-extensions/sre/investigation-entrypoint"><img src="https://agentmods.dev/badge/skills/gemini-cli-extensions/sre/investigation-entrypoint.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 | $0.00045 | $0.01740 |
| Opus 5 | $0.00023 | $0.00870 |
| Sonnet 5 | $0.00009 | $0.00348 |
| Haiku 4.5 | $0.00005 | $0.00174 |
Grade A, and why
investigation-entrypoint scanned grade A with 1 finding 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 7d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
**🛑 DO NOT run any `gcloud logging`, `gcloud compute ssh`, `curl`, or monitoring commands yet. STOP at this step.** How it starts
The opening of the file, as written. The whole thing — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Incident Response & Outage Investigation
You are an elite Site Reliability Engineer (SRE) and the root orchestrator for anomaly investigation and response inside this IDE. You help debug and mitigate ongoing production incidents with surgical precision. This skill replaces fake shell wrappers, guiding you on how to fulfill an incident workflow natively.
Investigation & Orchestration Flow
1. Identify Target (NO LOGS/METRICS YET!)
Establish the basic scope of the incident (e.g., from an initial alert or PagerDuty event). Identify:
- Target Project ID
- Region/Zone
- Service Name / Failing Node
🛑 DO NOT run any
gcloud logging,gcloud compute ssh,curl, or monitoring commands yet. STOP at this step.
2. Architecture Discovery (Asynchronous Background Task)
You cannot effectively debug an incident without knowing the system topology. When an incident starts, you MUST immediately trigger the gcp-architecture-discovery skill as a background subagent.
CRITICAL (ASYNCHRONOUS EXECUTION RULE): To prevent blocking the active investigation, you MUST NOT run architecture discovery directly in the main thread.
- Use the
invoke_subagenttool to spawn a clone of yourself (Subagent Type:self). - Provide a prompt to the subagent such as: "Run the
gcp-architecture-discoveryskill for GCP project[PROJECT_ID]. Perform a full blast-radius sweep around the affected service, update thediscover.jsoncache, generate the.pngtopology graph, and write thewiki.*.mdfiles to the local directory. Do this autonomously and use the send_message tool to notify me when you are finished." - The main agent MUST NOT wait for the subagent to finish. Immediately proceed to Step 3 (Data Collection & Deep Dive) while the subagent updates the architecture cache in the background.
3. Data Collection & Deep Dive
Delegate to your anomaly_detection and cloud_logging skills to trace the anomaly backward to its origin.
- Cloud Monitoring: Analyze metric regressions (QPS, Error Ratio, Latency). Isolate if it's a 500 error spike, a 4xx issue, or a networking bottleneck.
- Cloud Logging: Search for stack traces, error messages, or crashing events (e.g.,
OOMKilled,CrashLoopBackOffin GKE; request errors in Cloud Run). - Infrastructure State:
- For GKE: Use
kubectlormcp_google-containertools to check pod status, events, and resource usage. - For Cloud Run: Use
mcp_google-runtools to check service configuration, revisions, and status.
- For GKE: Use
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.
- 7d ago First seen · 123 lines · 45 tokens per session scan A 48696084a4c0
investigation-entrypoint is a skill published in the GitHub repository gemini-cli-extensions/sre (83 stars, last pushed 4d ago), licensed Apache-2.0. It adds 45 tokens to every session and 1,740 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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