detection-engineering-coverage-evaluation

detection-engineering-coverage-evaluation is a skill for Claude Code, Codex from hamzabellouch/agent-skills. It costs 98 tokens per session (1,520 once invoked), scanned A, original, MIT.

A workflow for testing and improving threat-detection rules in Google SecOps, Google's security operations platform. It turns threat information into simulated security events, checks which rules detect them, and identifies gaps.

In plain words
What is it for?
It helps extract threat intelligence, evaluate rule coverage, check whether rules alert correctly, and draft rules for uncovered behavior.
Why use it?
A security team may have rules that look complete but miss realistic attacker behavior. Simulated events reveal what is and is not covered before relying on the rules.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit It helps extract threat intelligence, evaluate rule coverage, check whether rules alert correctly, and draft rules for uncovered behavior.

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Install with agentmods
npx agentmods add skills/hamzabellouch/agent-skills/detection-engineering-coverage-evaluation
Install

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.

Any agent
npx skills add hamzabellouch/agent-skills --skill detection-engineering-coverage-evaluation
Clone the repo
git clone --depth 1 https://github.com/hamzabellouch/agent-skills

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for detection-engineering-coverage-evaluation

README.md
[![agentmods](https://agentmods.dev/badge/skills/hamzabellouch/agent-skills/detection-engineering-coverage-evaluation/github.svg)](https://agentmods.dev/skills/hamzabellouch/agent-skills/detection-engineering-coverage-evaluation)
Your own site
<a href="https://agentmods.dev/skills/hamzabellouch/agent-skills/detection-engineering-coverage-evaluation"><img src="https://agentmods.dev/badge/skills/hamzabellouch/agent-skills/detection-engineering-coverage-evaluation/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.

agentmods 80×15 button for detection-engineering-coverage-evaluation

Your own site · 80×15
<a href="https://agentmods.dev/skills/hamzabellouch/agent-skills/detection-engineering-coverage-evaluation"><img src="https://agentmods.dev/badge/skills/hamzabellouch/agent-skills/detection-engineering-coverage-evaluation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 98 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,520 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00098 $0.01520
Opus 5 $0.00049 $0.00760
Sonnet 5 $0.00020 $0.00304
Haiku 4.5 $0.00010 $0.00152

Measured 9d ago against content hash 633004762dd4, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

detection-engineering-coverage-evaluation 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 9d 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.

Google Cloud and GKE/detection-engineering-coverage-evaluation/SKILL.md · 165 lines

How it starts

The opening of the file, as written. The whole thing — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.

SecOps Detection Coverage Skill

This skill guides the agent through an end-to-end detection engineering lifecycle using Google SecOps MCP tools. It handles multiple Threat Detection Opportunities (TDOs) and ensures exhaustive coverage evaluation for all generated synthetic events.

Workflow Execution Checklist

Copy this checklist and track progress for each iteration:

  • Step 1: Extract raw text content from a source (for example, blog URL).
  • Step 2: Generate Threat Detection Opportunities (TDOs).
  • Step 3: Loop through ALL TDOs to generate synthetic events.
  • Step 4: Loop through ALL UDM events to evaluate rule coverage.
  • Step 5: For identified rules, check enablement and alerting status.
  • Step 6: Generate new rules for identified gaps.
  • Step 7: Provide a structured summary of findings and gaps.
  • Step 8: Ask the user to approve adding newly generated rules to their SecOps environment and create them.

Detailed Steps

1. Extract Threat Intelligence

  • Use the following prompt to extract all text content from a URL: - "Fetch the blog text from {url}. You need to extract and output the entire text content of the page, exactly as it appears in the HTML, without any summarization, modification, or omission."

  • Summary of Step: Report only that the text was successfully extracted from the provided URL. Do not output the full raw text.

  • Next Step: The extracted text will be used to generate Threat Detection Opportunities (TDOs).

2. Generate TDOs

  • Call generate_threat_detection_opportunity with the extracted full blog threat raw text. You must not summarize. This tool returns one or more TDOs.

  • Summary of Step: Report the number of TDOs generated and provide a brief, high-level summary for each TDO (for example, the key threat or attacker technique identified). Do not output the full TDO JSON.

  • Next Step: The process will now loop through each generated TDO to create synthetic events.

Read the full file on GitHub · 165 lines

Changes

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.

  1. 9d ago First seen · 165 lines · 98 tokens per session scan A 633004762dd4

Subscribe to this mod's changes

detection-engineering-coverage-evaluation is a skill published in the GitHub repository hamzabellouch/agent-skills (4 stars, last pushed 1mo ago), licensed MIT. It adds 98 tokens to every session and 1,520 once invoked, about $0.0005 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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