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 meltedinhex/analyst-ai-pack --skill writing-sigma-detection-rulesgit clone --depth 1 https://github.com/meltedinhex/analyst-ai-packWrote 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/meltedinhex/analyst-ai-pack/writing-sigma-detection-rules)<a href="https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/writing-sigma-detection-rules"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/writing-sigma-detection-rules/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/meltedinhex/analyst-ai-pack/writing-sigma-detection-rules"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/writing-sigma-detection-rules.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 | $0.00073 | $0.00756 |
| Opus 5 | $0.00036 | $0.00378 |
| Sonnet 5 | $0.00015 | $0.00151 |
| Haiku 4.5 | $0.00007 | $0.00076 |
Grade A, and why
writing-sigma-detection-rules 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 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.
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 — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Writing Sigma Detection Rules
When to Use
- A hunt produced reliable logic and you want a portable, SIEM-agnostic detection.
- You need to share or version a detection in a standard format and tag it to ATT&CK.
- You are translating a vendor query into Sigma for reuse across backends.
Do not use Sigma for stateful/correlation logic it cannot express well (complex sequence or statistical detections) — keep those in the SIEM's native correlation engine.
Prerequisites
- The
sigmaCLI /sigma-cli(pysigma) for validation and conversion. - Knowledge of the target log source's field names and the Sigma taxonomy.
Workflow
Step 1: Define the logsource
Pin category/product/service precisely (e.g., product: windows,
category: process_creation) so the rule maps to the right pipeline.
Step 2: Write robust selection logic
Express the detection on stable fields. Prefer multiple ANDed conditions over a single brittle
string; use contains/endswith modifiers thoughtfully to resist evasion.
python scripts/analyst.py scaffold --title "Encoded PowerShell" --category process_creation \
--technique T1059.001 --level high
Step 3: Add filters to cut false positives
Use a filter block (negated in the condition) to exclude known-good processes/paths rather
than narrowing selection until it misses variants.
Step 4: Tag and document
Add tags (ATT&CK technique), level, status, references, and a falsepositives list so
consumers can tune.
Step 5: Validate and convert
Lint the rule, then convert to the target backend and test against true/false-positive data before deploying.
sigma convert -t splunk rules/encoded_powershell.yml
Validation
- The rule passes Sigma schema validation (
sigma check). - It fires on the hunt's true positives and not on the documented benign cases.
- Conversion to the target backend produces a sensible, runnable query.
Pitfalls
- Over-narrow selection that matches one sample and misses the technique.
- Wrong logsource, so the rule never sees the relevant events.
- No
falsepositives/filter, producing a noisy alert that gets ignored.
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 · 96 lines · 73 tokens per session scan A 9d7a387f0adc
writing-sigma-detection-rules is a skill published in the GitHub repository meltedinhex/analyst-ai-pack (22 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 73 tokens to every session and 756 once invoked, about $0.0004 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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