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detection-engineering

Skill1 starsChanged 3 months agoMITView on GitHub

Detection engineering skill for writing, testing, and tuning Sigma, YARA, YARA-L, Suricata, Snort, Zeek, KQL, SPL, EQL, Chronicle, and Elastic detection rules. Covers ATT&CK coverage mapping, telemetry gap analysis, false-positive tuning, detection-as-code pipelines, and validation with Atomic Red Team and Caldera. Use to design durable detections backed by tests.

6 other projects use a version of this, 1 of them edited.

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The file

We split this skill into the rules it gives an agent

2 rules. 0 are common, 2 are distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 5 edited versions we read keep these.

  • 2

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Start from a threat behavior, not a single IOC.

    line 10
  2. Distinctive:

    Every rule ships with: hypothesis, data source, ATT&CK mapping, false-positive notes, response action, and a test.

    line 11

“Common” is measured: kept by at least 6 in 10 of the 5 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Detection Engineering

Operating Rules

  • Start from a threat behavior, not a single IOC. Rules expire; behaviors persist.
  • Every rule ships with: hypothesis, data source, ATT&CK mapping, false-positive notes, response action, and a test.
  • Tune for analyst time, not raw event volume. A noisy critical is a broken critical.

Authoring Workflow

  1. State hypothesis: actor, technique, observable, data source, confidence.
  2. Identify telemetry: process, file, registry, network, auth, cloud, email, identity, EDR fields.
  3. Draft rule in the platform's native DSL; keep selectors readable; comment non-obvious filters.
  4. Generate matched and unmatched test events; run in a dry-run pipeline.
  5. Validate with Atomic Red Team / Caldera tests; record true positive evidence.
  6. Tune: add allow-lists with reasons and review dates; never silently broaden.
  7. Ship with a runbook: triage steps, expected next queries, containment.

Coverage Strategy

  • Maintain a matrix of ATT&CK techniques × data sources × rule IDs × last-tested date.
  • Track gaps as backlog items with prerequisite telemetry (e.g., enable Sysmon 1/3/10/11).
  • Score detections on durability, fidelity, and response cost.

Output Contract

  • rules/<platform>/<id>.yml with metadata block.
  • tests/<id>/ with positive and negative pcaps, EVTX, or JSON.
  • coverage.csv: ATT&CK × rule × status × last_validated.
  • runbooks/<id>.md: analyst playbook.
  • release-notes.md: what changed, why, expected volume delta.

Example: Sigma skeleton

title: Suspicious encoded PowerShell
status: experimental
logsource:
  product: windows
  category: process_creation
detection:
  selection:
    Image|endswith: '\\powershell.exe'
    CommandLine|contains: ' -enc '
  condition: selection
falsepositives:
  - admin scripts, SCCM
level: medium

KQL equivalent (untested):

DeviceProcessEvents
| where FileName =~ "powershell.exe"
| where ProcessCommandLine has " -enc "

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