Agent skill

Analyzing Outlook Pst For Email Forensics

by mukul975 in mukul975/Anthropic-Cybersecurity-Skills

Parse Microsoft Outlook PST and OST files using libpff and pst-utils to extract message content, headers, attachments, deleted items, and MAPI metadata, including recovery of items from the…

Apache-2.0Auto-check passedSecurity

Install Analyzing Outlook Pst For Email Forensics

skills CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-outlook-pst-for-email-forensics -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills analyzing-outlook-pst-for-email-forensics --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/analyzing-outlook-pst-for-email-forensics .claude/skills/analyzing-outlook-pst-for-email-forensics && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
analyzing-outlook-pst-for-email-forensics
GitHub stars
34k
Token cost
~3.2k tokens
SKILL.md length
291 words
Files
6 (incl. scripts, references)
Skills in repo
644
Repo updated
First seen
Licence
Apache-2.0

At a glance

Parse Microsoft Outlook PST and OST files using libpff and pst-utils to extract message content, headers, attachments, deleted items, and MAPI metadata, including recovery of items from the…

  • Conducting email forensic investigations
  • SKILL.md covers Overview, When to Use, Prerequisites and PST File Locations, plus 4 more sections
  • Runs Python scripts from its folder
  • Legal e-discovery

What it does

Analyzing Outlook Pst For Email Forensics is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Parse Microsoft Outlook PST and OST files using libpff and pst-utils to extract message content, headers, attachments, deleted items, and MAPI metadata, including recovery of items from the Recoverable Items folder. Use when conducting email forensic investigations, legal e-discovery, or incident response that requires reconstructing communication patterns or tracing message routing from Outlook archives.

Its SKILL.md is about 3.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including scripts and reference files (for example `references/api-reference.md`, `references/standards.md` and `references/workflows.md`).

It sits in Security, covering Digital forensics and Incident response. It works with Microsoft Outlook. The repository describes itself as: 817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io…. The licence is Apache-2.0.

When your agent uses it

  • Conducting email forensic investigations
  • Legal e-discovery
  • Incident response that requires reconstructing communication patterns
  • Tracing message routing from Outlook archives

Example prompts

  • “/analyzing-outlook-pst-for-email-forensics”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 54a7988. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • mailxaminer.com
    • github.com
    • docs.microsoft.com
    • sans.org

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Analyzing Outlook Pst For Email Forensics loads about 3.2k tokens when it runs, and up to ~4.1k if it reads all its reference files. Until then it costs about 113 tokens; SKILL.md has 291 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~113
When it runs · the whole SKILL.md, loaded when a task matches
~3.2k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from mukul975/Anthropic-Cybersecurity-Skills at commit 54a7988, republished under its Apache-2.0 licence (© mukul975). 291 words, ~3,203 tokens.

Download SKILL.mdSave it as .claude/skills/analyzing-outlook-pst-for-email-forensics/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
analyzing-outlook-pst-for-email-forensics
description
Parse Microsoft Outlook PST and OST files using libpff and pst-utils to extract message content, headers, attachments, deleted items, and MAPI metadata, including recovery of items from the Recoverable Items folder. Use when conducting email forensic investigations, legal e-discovery, or incident response that requires reconstructing communication patterns or tracing message routing from Outlook archives.
domain
cybersecurity
subdomain
digital-forensics
tags
email-forensics, pst, ost, outlook, mapi, email-headers, attachments, deleted-emails, libpff, eml-extraction
version
1.0
author
mahipal
license
Apache-2.0
nist_ai_rmf
MANAGE-2.4, MANAGE-3.1, MEASURE-3.1
nist_csf
RS.AN-03, DE.AE-02, RS.MA-01
mitre_attack
T1114.001, T1564.008, T1070.008

Analyzing Outlook PST for Email Forensics

Overview

Microsoft Outlook PST (Personal Storage Table) and OST (Offline Storage Table) files are critical evidence sources in digital forensics investigations. PST files store email messages, calendar events, contacts, tasks, and notes in a proprietary binary format based on the MAPI (Messaging Application Programming Interface) property system. Forensic analysis of these files enables recovery of deleted emails (from the Recoverable Items folder), extraction of email headers for tracing message routes, analysis of attachments for malware or exfiltrated data, and reconstruction of communication patterns. Modern PST files use Unicode format with 4KB pages and can grow up to 50GB, while legacy ANSI format is limited to 2GB.

When to Use

  • When investigating security incidents that require analyzing outlook pst for email forensics
  • When building detection rules or threat hunting queries for this domain
  • When SOC analysts need structured procedures for this analysis type
  • When validating security monitoring coverage for related attack techniques

Prerequisites

  • libpff/pffexport (open-source PST parser)
  • Python 3.8+ with pypff or libratom libraries
  • MailXaminer, Forensic Email Collector, or SysTools PST Forensics (commercial)
  • Microsoft Outlook (optional, for native PST access)
  • Sufficient disk space for extracted content

PST File Locations

SourcePath
Outlook 2016+ Default%USERPROFILE%\Documents\Outlook Files*.pst
Outlook Legacy%LOCALAPPDATA%\Microsoft\Outlook*.pst
OST Cache%LOCALAPPDATA%\Microsoft\Outlook*.ost
Archive%USERPROFILE%\Documents\Outlook Files\archive.pst

Analysis with Open-Source Tools

libpff / pffexport
bash
# Export all items from PST file
pffexport -m all evidence.pst -t exported_pst

# Export only email messages
pffexport -m items evidence.pst -t exported_emails

# Export recovered/deleted items
pffexport -m recovered evidence.pst -t recovered_items

# Get PST file information
pffinfo evidence.pst
Python PST Analysis
python
import pypff
import os
import json
import hashlib
import email
import sys
from datetime import datetime
from collections import defaultdict


class PSTForensicAnalyzer:
    """Forensic analysis of Outlook PST/OST files."""

    def __init__(self, pst_path: str, output_dir: str):
        self.pst_path = pst_path
        self.output_dir = output_dir
        os.makedirs(output_dir, exist_ok=True)
        self.pst = pypff.file()
        self.pst.open(pst_path)
        self.messages = []
        self.attachments = []
        self.stats = defaultdict(int)

    def process_folder(self, folder, folder_path: str = ""):
        """Recursively process PST folders and extract messages."""
        folder_name = folder.name or "Root"
        current_path = f"{folder_path}/{folder_name}" if folder_path else folder_name

        for i in range(folder.number_of_sub_messages):
            try:
                message = folder.get_sub_message(i)
                msg_data = self.extract_message(message, current_path)
                if msg_data:
                    self.messages.append(msg_data)
                    self.stats["total_messages"] += 1
            except Exception as e:
                self.stats["parse_errors"] += 1

        for i in range(folder.number_of_sub_folders):
            try:
                subfolder = folder.get_sub_folder(i)
                self.process_folder(subfolder, current_path)
            except Exception:
                continue

    def extract_message(self, message, folder_path: str) -> dict:
        """Extract forensic metadata from a single email message."""
        msg_data = {
            "folder": folder_path,
            "subject": message.subject or "",
            "sender": message.sender_name or "",
            "sender_email": "",
            "creation_time": str(message.creation_time) if message.creation_time else None,
            "delivery_time": str(message.delivery_time) if message.delivery_time else None,
            "modification_time": str(message.modification_time) if message.modification_time else None,
            "has_attachments": message.number_of_attachments > 0,
            "attachment_count": message.number_of_attachments,
            "body_size": len(message.plain_text_body or b""),
            "html_size": len(message.html_body or b""),
        }

        # Extract transport headers for routing analysis
        headers = message.transport_headers
        if headers:
            msg_data["headers_present"] = True
            msg_data["headers_size"] = len(headers)
            # Parse key headers
            parsed = email.message_from_string(headers)
            msg_data["from_header"] = parsed.get("From", "")
            msg_data["to_header"] = parsed.get("To", "")
            msg_data["date_header"] = parsed.get("Date", "")
            msg_data["message_id"] = parsed.get("Message-ID", "")
            msg_data["x_originating_ip"] = parsed.get("X-Originating-IP", "")
            msg_data["received_headers"] = parsed.get_all("Received", [])

        # Process attachments
        for j in range(message.number_of_attachments):
            try:
                attachment = message.get_attachment(j)
                att_data = {
                    "message_subject": msg_data["subject"],
                    "name": attachment.name or f"attachment_{j}",
                    "size": attachment.size,
                    "content_type": "",
                }
                self.attachments.append(att_data)
                self.stats["total_attachments"] += 1
            except Exception:
                continue

        return msg_data

    def save_attachments(self, max_size_mb: int = 100):
        """Export attachments to disk for analysis."""
        att_dir = os.path.join(self.output_dir, "attachments")
        os.makedirs(att_dir, exist_ok=True)

        root = self.pst.get_root_folder()
        self._save_attachments_recursive(root, att_dir, max_size_mb)

    def _save_attachments_recursive(self, folder, att_dir, max_size_mb):
        for i in range(folder.number_of_sub_messages):
            try:
                message = folder.get_sub_message(i)
                for j in range(message.number_of_attachments):
                    att = message.get_attachment(j)
                    if att.size and att.size < max_size_mb * 1024 * 1024:
                        name = att.name or f"unknown_{i}_{j}"
                        safe_name = "".join(c if c.isalnum() or c in ".-_" else "_" for c in name)
                        path = os.path.join(att_dir, safe_name)
                        try:
                            data = att.read_buffer(att.size)
                            with open(path, "wb") as f:
                                f.write(data)
                        except Exception:
                            continue
            except Exception:
                continue

        for i in range(folder.number_of_sub_folders):
            try:
                self._save_attachments_recursive(folder.get_sub_folder(i), att_dir, max_size_mb)
            except Exception:
                continue

    def generate_report(self) -> str:
        """Generate comprehensive PST forensic analysis report."""
        root = self.pst.get_root_folder()
        self.process_folder(root)

        report = {
            "analysis_timestamp": datetime.now().isoformat(),
            "pst_file": self.pst_path,
            "pst_size_bytes": os.path.getsize(self.pst_path),
            "statistics": dict(self.stats),
            "messages": self.messages[:500],
            "attachments": self.attachments[:200],
        }

        report_path = os.path.join(self.output_dir, "pst_forensic_report.json")
        with open(report_path, "w") as f:
            json.dump(report, f, indent=2, default=str)

        print(f"[*] Total messages: {self.stats['total_messages']}")
        print(f"[*] Total attachments: {self.stats['total_attachments']}")
        print(f"[*] Parse errors: {self.stats['parse_errors']}")
        return report_path

    def close(self):
        self.pst.close()


def main():
    if len(sys.argv) < 3:
        print("Usage: python process.py <pst_file> <output_dir>")
        sys.exit(1)
    analyzer = PSTForensicAnalyzer(sys.argv[1], sys.argv[2])
    analyzer.generate_report()
    analyzer.close()


if __name__ == "__main__":
    main()

Email Header Analysis

Key headers for forensic investigation:

HeaderForensic Value
ReceivedMessage routing chain (read bottom to top)
X-Originating-IPSender's actual IP address
Message-IDUnique identifier for correlation
DateSend timestamp
Return-PathBounce address (may differ from From)
DKIM-SignatureDomain authentication signature
Authentication-ResultsSPF, DKIM, DMARC verification results
X-MailerEmail client used

References

Example Output

text
$ pffexport /evidence/jsmith_archive.pst -t /analysis/pst_output

pffexport 20231205 - libpff PST/OST Export Tool
=================================================
Input: /evidence/jsmith_archive.pst (2.3 GB)

Exporting PST contents...
  Folders:       45
  Messages:      12,456
  Attachments:   3,234
  Contacts:      567
  Calendar:      234
  Tasks:         89

Export completed in 3m 42s.

$ python3 pst_analyzer.py /analysis/pst_output /analysis/email_report

PST Forensic Analysis Report
==============================
Source: jsmith_archive.pst (john.smith@corporate.com)
Date Range: 2023-06-01 to 2024-01-18

--- Mailbox Statistics ---
  Total Emails:       12,456
  Sent:               4,567
  Received:           7,889
  With Attachments:   3,234
  Deleted (recovered): 234

--- Phishing / Suspicious Emails ---
Email #8923
  Date:        2024-01-15 14:30:22 UTC
  From:        "IT Support" <it-support@c0rporate-help.com>
  To:          john.smith@corporate.com
  Subject:     Urgent: Password Reset Required
  Headers:
    Return-Path:    bounce@mail-relay.c0rporate-help.com
    X-Originating-IP: 203.0.113.55
    Received:       from mail-relay.c0rporate-help.com (203.0.113.55)
    SPF:            FAIL (domain c0rporate-help.com)
    DKIM:           NONE
    DMARC:          FAIL
  Attachments:
    - Password_Reset_Form.xlsm (245 KB) SHA-256: 7a3b8c9d...e1f2a3b4
  Body Preview:  "Dear Employee, Your password will expire in 24 hours.
                  Please open the attached form to reset your credentials..."

--- Data Exfiltration Indicators ---
Email #9102
  Date:        2024-01-16 03:15:45 UTC
  From:        john.smith@corporate.com
  To:          j.smith.personal8842@protonmail.com
  Subject:     (no subject)
  Attachments:
    - archive_part1.7z (24.5 MB) - encrypted
    - archive_part2.7z (24.5 MB) - encrypted

Email #9103
  Date:        2024-01-16 03:18:22 UTC
  From:        john.smith@corporate.com
  To:          j.smith.personal8842@protonmail.com
  Subject:     Re:
  Attachments:
    - archive_part3.7z (18.2 MB) - encrypted

--- Keyword Hits ---
  "confidential":     45 emails
  "password":         23 emails
  "transfer":         12 emails
  "resign":           3 emails
  "delete evidence":  1 email (Email #9200, 2024-01-17 22:30:00 UTC)

Summary:
  Phishing emails detected:    1 (initial compromise vector)
  Suspicious sent emails:      5 (to personal accounts with attachments)
  Encrypted attachments:       3 (67.2 MB total - possible exfiltration)
  Report: /analysis/email_report/pst_forensic_report.json

© mukul975, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 5 other files (scripts, references) in skills/analyzing-outlook-pst-for-email-forensics of mukul975/Anthropic-Cybersecurity-Skills.

  • SKILL.md
  • LICENSE
  • references/api-reference.md
  • references/standards.md
  • references/workflows.md
  • scripts/agent.py

Open the folder on GitHubat commit 54a7988

Compare with similar skills

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Analyzing Outlook Pst For Email Forensics compared with similar skills
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Digital Forensicssickn33/agentic-awesome-skills47k1 repos~495Automated safety check: PassMIT

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Questions about Analyzing Outlook Pst For Email Forensics

What does Analyzing Outlook Pst For Email Forensics do?

Parse Microsoft Outlook PST and OST files using libpff and pst-utils to extract message content, headers, attachments, deleted items, and MAPI metadata, including recovery of items from the…. Analyzing Outlook Pst For Email Forensics is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Parse Microsoft Outlook PST and OST files using libpff and pst-utils to extract message content, headers, attachments, deleted items, and MAPI metadata, including recovery of items from the Recoverable Items folder.

When should I use Analyzing Outlook Pst For Email Forensics?

Analyzing Outlook Pst For Email Forensics fits situations like: conducting email forensic investigations; legal e-discovery; incident response that requires reconstructing communication patterns; tracing message routing from Outlook archives.

How do I install Analyzing Outlook Pst For Email Forensics in Claude Code?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-outlook-pst-for-email-forensics -a claude-code`. Or copy the skill folder (skills/analyzing-outlook-pst-for-email-forensics in mukul975/Anthropic-Cybersecurity-Skills) into .claude/skills/analyzing-outlook-pst-for-email-forensics in your project. Claude Code loads it when a task matches its description.

How do I install Analyzing Outlook Pst For Email Forensics in Codex?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-outlook-pst-for-email-forensics -a codex`. Or copy the skill folder (skills/analyzing-outlook-pst-for-email-forensics in mukul975/Anthropic-Cybersecurity-Skills) into .agents/skills/analyzing-outlook-pst-for-email-forensics in your project. Codex loads it when a task matches its description.

Can I use Analyzing Outlook Pst For Email Forensics in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-outlook-pst-for-email-forensics -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/analyzing-outlook-pst-for-email-forensics, .gemini/skills/analyzing-outlook-pst-for-email-forensics, .github/skills/analyzing-outlook-pst-for-email-forensics and .opencode/skills/analyzing-outlook-pst-for-email-forensics in your project.

What does Analyzing Outlook Pst For Email Forensics need to run?

Going by SKILL.md and its folder, Analyzing Outlook Pst For Email Forensics needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Analyzing Outlook Pst For Email Forensics access the network?

SKILL.md names 4 domains. As links in the text: mailxaminer.com, github.com, docs.microsoft.com and sans.org. This is read from the text; nothing was executed.

Is Analyzing Outlook Pst For Email Forensics safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Analyzing Outlook Pst For Email Forensics use?

Analyzing Outlook Pst For Email Forensics is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Analyzing Outlook Pst For Email Forensics use?

About 3.2k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 900 tokens, read only when the agent opens those files.

What are the alternatives to Analyzing Outlook Pst For Email Forensics?

Skills that share tags, products or a category with Analyzing Outlook Pst For Email Forensics: Incident Response (hypnguyen1209/offensive-claude, 388 stars), Forensics Osquery (AgentSecOps/SecOpsAgentKit, 220 stars), Ir Velociraptor (AgentSecOps/SecOpsAgentKit, 220 stars) and Incident Response Network (LeoYeAI/openclaw-master-skills, 2.2k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Analyzing Outlook Pst For Email Forensics?

mukul975 (a GitHub user) maintains it in mukul975/Anthropic-Cybersecurity-Skills, which has 34,116 GitHub stars. The repository holds 644 skills in this directory. The repository was last updated on August 31, 2026.

Source: mukul975/Anthropic-Cybersecurity-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.