Codex

ioc-extraction

Extract, classify, deduplicate, and enrich IOCs from investigation artifacts; map to STIX 2.1 observables

104 stars

Best use case

ioc-extraction is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

It is a strong fit for teams already working in Codex.

Extract, classify, deduplicate, and enrich IOCs from investigation artifacts; map to STIX 2.1 observables

Teams using ioc-extraction should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/ioc-extraction/SKILL.md --create-dirs "https://raw.githubusercontent.com/jmagly/aiwg/main/.agents/skills/ioc-extraction/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/ioc-extraction/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How ioc-extraction Compares

Feature / Agentioc-extractionStandard Approach
Platform SupportCodexLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Extract, classify, deduplicate, and enrich IOCs from investigation artifacts; map to STIX 2.1 observables

Which AI agents support this skill?

This skill is designed for Codex.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# ioc-extraction

Scans investigation artifacts — log files, memory analysis output, findings documents, and raw captures — to extract indicators of compromise. Classifies each indicator by type, deduplicates, and produces a STIX 2.1 observable bundle alongside a flat IOC list for import into SIEMs and threat intelligence platforms.

## Triggers


Alternate expressions and non-obvious activations (primary phrases are matched automatically from the skill description):

- "IOCs" / "indicators" → Indicator of Compromise extraction
- "STIX" / "STIX 2.1" → structured threat intelligence output
- "pull indicators" → IOC extraction shorthand

## Purpose

IOCs extracted during investigation have value beyond the current case: they feed detection rules, threat intelligence platforms, and network blocklists. Raw extraction without classification and deduplication produces noise. This skill applies consistent extraction patterns and maps output to STIX 2.1 so findings integrate with standard threat intelligence tooling.

## Behavior

When triggered, this skill:

1. **Identify input sources**:
   - Accept a directory path, file path, or glob pattern
   - Default to scanning all files under `.aiwg/forensics/` if no path is specified
   - Supported source types: plain text, Markdown, JSON, JSONL, CSV, raw log files

2. **Extract IP addresses**:
   - IPv4: match `\b(?:\d{1,3}\.){3}\d{1,3}\b`, validate octets are 0-255
   - IPv6: match full and compressed forms
   - Exclude RFC1918 private ranges, loopback (127.0.0.0/8), link-local (169.254.0.0/16), and multicast (224.0.0.0/4) by default (configurable)
   - Exclude IP addresses that appear only in trusted infrastructure context (DNS servers, NTP servers from baseline profile)

3. **Extract domain names and hostnames**:
   - Match FQDNs: `\b(?:[a-zA-Z0-9-]+\.)+[a-zA-Z]{2,}\b`
   - Exclude known-good domains from an allowlist (configurable)
   - Flag domains with high entropy names (DGA indicators): calculate Shannon entropy per label
   - Flag recently registered TLDs and uncommon ccTLDs

4. **Extract file hashes**:
   - MD5: 32 hex characters
   - SHA-1: 40 hex characters
   - SHA-256: 64 hex characters
   - Tag with hash type; flag any MD5 or SHA-1 hashes as weak-algorithm IOCs

5. **Extract URLs**:
   - Match full URLs including scheme, host, path, and query string
   - Defang for safe storage: replace `http` with `hxxp`, `.` with `[.]` in output
   - Classify by scheme: http, https, ftp, smb, ldap

6. **Extract email addresses**:
   - Standard RFC 5321 pattern
   - Flag addresses in suspicious domains or with high-entropy local parts

7. **Extract file paths and registry keys**:
   - Unix absolute paths: `/[a-zA-Z0-9._/-]+`
   - Windows paths: `[A-Za-z]:\\[^\s"]+`
   - Windows registry keys: `HK(LM|CU|CR|U|CC)\\[^\s"]+`

8. **Classify and deduplicate**:
   - Assign STIX 2.1 observable type to each indicator:
     - IP: `ipv4-addr` or `ipv6-addr`
     - Domain: `domain-name`
     - URL: `url`
     - Hash: `file` with `hashes` property
     - Email: `email-addr`
     - File path: `file`
     - Registry key: `windows-registry-key`
   - Deduplicate by value within each type
   - Record source file and line number for each unique indicator

9. **Produce STIX 2.1 bundle**:
   - Generate `observable-objects` entries per STIX 2.1 specification
   - Assign deterministic UUIDs based on type and value (version 5 UUID from SHA-1 namespace)
   - Include `created` and `modified` timestamps
   - Link observables to a STIX `report` object referencing the investigation ID

10. **Write outputs**:
    - Flat IOC list: `.aiwg/forensics/iocs/<investigation>-iocs.txt` (one indicator per line, typed prefix)
    - STIX bundle: `.aiwg/forensics/iocs/<investigation>-stix.json`
    - Summary report: `.aiwg/forensics/iocs/<investigation>-ioc-summary.md`

## Usage Examples

### Example 1 — Scan all forensics artifacts
```
extract iocs
```

### Example 2 — Scan specific file
```
extract indicators from .aiwg/forensics/findings/webserver-01-linux.md
```

### Example 3 — With custom allowlist
```
ioc analysis --allowlist /etc/forensics/trusted-domains.txt
```

## Output Locations

- Flat IOC list: `.aiwg/forensics/iocs/<investigation>-iocs.txt`
- STIX 2.1 bundle: `.aiwg/forensics/iocs/<investigation>-stix.json`
- Summary: `.aiwg/forensics/iocs/<investigation>-ioc-summary.md`

## Configuration

```yaml
ioc_extraction:
  exclude_private_ips: true
  exclude_loopback: true
  exclude_multicast: true
  dga_entropy_threshold: 3.5
  weak_hash_algorithms:
    - md5
    - sha1
  defang_urls: true
  stix_version: "2.1"
  domain_allowlist: []
  ip_allowlist: []
```

## References

- @$AIWG_ROOT/agentic/code/addons/aiwg-utils/rules/research-before-decision.md — Scan investigation artifacts completely before extracting; check baseline and allowlists before flagging
- @$AIWG_ROOT/agentic/code/addons/aiwg-utils/rules/human-authorization.md — Produce IOC lists for analyst review; do not autonomously push indicators to blocking systems
- @$AIWG_ROOT/agentic/code/frameworks/forensics-complete/rules/evidence-integrity.md — IOC extraction must not modify source artifacts; read-only access to evidence
- @$AIWG_ROOT/agentic/code/frameworks/forensics-complete/skills/evidence-preservation/SKILL.md — Evidence must be preserved and hashed before IOC extraction begins
- @$AIWG_ROOT/agentic/code/frameworks/forensics-complete/skills/sigma-hunting/SKILL.md — Sigma hunting cross-references extracted IOCs against log sources for confirmation

Related Skills

Audio Extraction

104
from jmagly/aiwg

ffmpeg patterns for extracting audio from video files and transcoding between formats

aiwg-orchestrate

104
from jmagly/aiwg

Route structured artifact work to AIWG workflows via MCP with zero parent context cost

venv-manager

104
from jmagly/aiwg

Create, manage, and validate Python virtual environments. Use for project isolation and dependency management.

pytest-runner

104
from jmagly/aiwg

Execute Python tests with pytest, supporting fixtures, markers, coverage, and parallel execution. Use for Python test automation.

vitest-runner

104
from jmagly/aiwg

Execute JavaScript/TypeScript tests with Vitest, supporting coverage, watch mode, and parallel execution. Use for JS/TS test automation.

eslint-checker

104
from jmagly/aiwg

Run ESLint for JavaScript/TypeScript code quality and style enforcement. Use for static analysis and auto-fixing.

repo-analyzer

104
from jmagly/aiwg

Analyze GitHub repositories for structure, documentation, dependencies, and contribution patterns. Use for codebase understanding and health assessment.

pr-reviewer

104
from jmagly/aiwg

Review GitHub pull requests for code quality, security, and best practices. Use for automated PR feedback and approval workflows.

YouTube Acquisition

104
from jmagly/aiwg

yt-dlp patterns for acquiring content from YouTube and video platforms

Quality Filtering

104
from jmagly/aiwg

Accept/reject logic and quality scoring heuristics for media content

Provenance Tracking

104
from jmagly/aiwg

W3C PROV-O patterns for tracking media derivation chains and production history

Metadata Tagging

104
from jmagly/aiwg

opustags and ffmpeg patterns for applying metadata to audio and video files