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 hamzabellouch/agent-skills --skill detection-engineering-coverage-evaluationgit clone --depth 1 https://github.com/hamzabellouch/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/hamzabellouch/agent-skills/detection-engineering-coverage-evaluation)<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.
<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>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 | $0.00098 | $0.01520 |
| Opus 5 | $0.00049 | $0.00760 |
| Sonnet 5 | $0.00020 | $0.00304 |
| Haiku 4.5 | $0.00010 | $0.00152 |
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.
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_opportunitywith 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.
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.
- 9d ago First seen · 165 lines · 98 tokens per session scan A 633004762dd4
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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