Agent skill

Analyzing Slack Space And File System Artifacts

by mukul975 in mukul975/Anthropic-Cybersecurity-Skills

Examine NTFS slack space, MFT entries, the USN Change Journal, and Alternate Data Streams (ADS) to recover hidden or residual data, reconstruct deleted-file metadata, and reconstruct available…

Apache-2.0Auto-check passedSecurity

Install Analyzing Slack Space And File System Artifacts

skills CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-slack-space-and-file-system-artifacts -a claude-code

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

GitHub CLI
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills analyzing-slack-space-and-file-system-artifacts --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-slack-space-and-file-system-artifacts .claude/skills/analyzing-slack-space-and-file-system-artifacts && 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-slack-space-and-file-system-artifacts
GitHub stars
34k
Token cost
~3.7k tokens
SKILL.md length
500 words
Files
4 (incl. scripts, references)
Skills in repo
644
Repo updated
First seen
Licence
Apache-2.0

At a glance

Examine NTFS slack space, MFT entries, the USN Change Journal, and Alternate Data Streams (ADS) to recover hidden or residual data, reconstruct deleted-file metadata, and reconstruct available…

  • Works in 5 steps: Identify and Extract NTFS File System… → Analyze the Master File Table (MFT) → Analyze Slack Space for Hidden Data → …
  • Tasks that involve Digital forensics
  • SKILL.md covers When to Use, Prerequisites, Workflow and Key Concepts, plus 3 more sections
  • Runs Python scripts from its folder; calls python3 and pip; reaches malicious-site.com and cdn.malicious-site.com

What it does

Analyzing Slack Space And File System Artifacts is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Examine NTFS slack space, MFT entries, the USN Change Journal, and Alternate Data Streams (ADS) to recover hidden or residual data, reconstruct deleted-file metadata, and reconstruct available file-system change activity from USN records. Use during deep forensic analysis of an NTFS image when standard file recovery is insufficient, such as hunting for data hidden in ADS.

Its SKILL.md is about 3.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts and reference files (for example `references/api-reference.md` and `scripts/agent.py`).

It sits in Security, covering Digital forensics. It works with Slack. 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

  • Tasks that involve Digital forensics

Example prompts

  • “/analyzing-slack-space-and-file-system-artifacts”

Requirements

  • Python 3

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Identify and Extract NTFS File System Artifacts
  2. Analyze the Master File Table (MFT)
  3. Analyze Slack Space for Hidden Data
  4. Parse the USN Change Journal
  5. Detect and Analyze Alternate Data Streams

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.

    Shell commands in SKILL.md call:

    • python3
    • pip

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

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • malicious-site.com
    • cdn.malicious-site.com

    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 Slack Space And File System Artifacts loads about 3.7k tokens when it runs, and up to ~4.2k if it reads all its reference files. Until then it costs about 106 tokens; SKILL.md has 500 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~106
When it runs · the whole SKILL.md, loaded when a task matches
~3.7k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4.2k

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). 500 words, ~3,709 tokens.

Download SKILL.mdSave it as .claude/skills/analyzing-slack-space-and-file-system-artifacts/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
analyzing-slack-space-and-file-system-artifacts
description
Examine NTFS slack space, MFT entries, the USN Change Journal, and Alternate Data Streams (ADS) to recover hidden or residual data, reconstruct deleted-file metadata, and reconstruct available file-system change activity from USN records. Use during deep forensic analysis of an NTFS image when standard file recovery is insufficient, such as hunting for data hidden in ADS.
domain
cybersecurity
subdomain
digital-forensics
tags
forensics, slack-space, ntfs, mft, usn-journal, alternate-data-streams, file-system-analysis
version
1.0
author
mahipal
license
Apache-2.0
nist_csf
RS.AN-03, DE.AE-02, RS.MA-01
mitre_attack
T1070.006, T1564.004, T1070.004, T1005, T1006

Analyzing Slack Space and File System Artifacts

When to Use

  • When searching for hidden or residual data in file system slack space
  • For analyzing NTFS Master File Table (MFT) entries for deleted file metadata
  • When reconstructing file operations from the USN Change Journal
  • For detecting Alternate Data Streams (ADS) used to hide data or malware
  • During deep forensic analysis requiring examination beyond standard file recovery

Prerequisites

  • Forensic disk image with NTFS file system
  • The Sleuth Kit (TSK) tools: istat, icat, fls, blkls, blkstat
  • MFTECmd (Eric Zimmerman) for MFT parsing
  • MFTExplorer for interactive MFT analysis
  • Understanding of NTFS structures (MFT, $UsnJrnl, $LogFile, ADS)
  • Python with analyzeMFT or mft library for automated parsing

Workflow

Step 1: Identify and Extract NTFS File System Artifacts
bash
# Determine partition layout
mmls /cases/case-2024-001/images/evidence.dd

# Extract key NTFS system files
# $MFT - Master File Table
icat -o 2048 /cases/case-2024-001/images/evidence.dd 0 > /cases/case-2024-001/ntfs/MFT

# $UsnJrnl:$J - USN Change Journal
icat -o 2048 /cases/case-2024-001/images/evidence.dd 62-128 > /cases/case-2024-001/ntfs/UsnJrnl_J

# $LogFile - Transaction log
icat -o 2048 /cases/case-2024-001/images/evidence.dd 2 > /cases/case-2024-001/ntfs/LogFile

# Extract all slack space from the volume
blkls -s -o 2048 /cases/case-2024-001/images/evidence.dd > /cases/case-2024-001/ntfs/slack_space.raw

# Get file system information
fsstat -o 2048 /cases/case-2024-001/images/evidence.dd | tee /cases/case-2024-001/ntfs/fs_info.txt
Step 2: Analyze the Master File Table (MFT)
bash
# Parse MFT with MFTECmd (Eric Zimmerman)
MFTECmd.exe -f "C:\cases\ntfs\MFT" --csv "C:\cases\analysis\" --csvf mft_analysis.csv

# Parse with analyzeMFT (Python)
pip install analyzeMFT

analyzeMFT.py -f /cases/case-2024-001/ntfs/MFT \
   -o /cases/case-2024-001/analysis/mft_analysis.csv \
   -c

# Custom MFT analysis with Python
python3 << 'PYEOF'
from mft import PyMft
import csv

mft = PyMft(open('/cases/case-2024-001/ntfs/MFT', 'rb').read())

deleted_files = []
suspicious_files = []

for entry in mft.entries():
    if entry is None:
        continue

    filename = entry.get_filename()
    if filename is None:
        continue

    is_deleted = not entry.is_active()
    is_directory = entry.is_directory()
    created = entry.get_created_timestamp()
    modified = entry.get_modified_timestamp()
    mft_modified = entry.get_mft_modified_timestamp()
    size = entry.get_file_size()

    # Flag deleted files for recovery
    if is_deleted and not is_directory and size > 0:
        deleted_files.append({
            'filename': filename,
            'size': size,
            'created': str(created),
            'modified': str(modified),
            'entry_number': entry.entry_number
        })

    # Detect timestomping (MFT modified time != $SI modified time)
    si_modified = entry.get_si_modified_timestamp()
    fn_modified = entry.get_fn_modified_timestamp()
    if si_modified and fn_modified:
        if abs((si_modified - fn_modified).total_seconds()) > 86400:  # >1 day difference
            suspicious_files.append({
                'filename': filename,
                'si_modified': str(si_modified),
                'fn_modified': str(fn_modified),
                'delta': str(si_modified - fn_modified)
            })

print(f"=== DELETED FILES (recoverable metadata) ===")
print(f"Total: {len(deleted_files)}")
for f in deleted_files[:20]:
    print(f"  [{f['modified']}] {f['filename']} ({f['size']} bytes)")

print(f"\n=== POTENTIAL TIMESTOMPING ===")
print(f"Total suspicious: {len(suspicious_files)}")
for f in suspicious_files[:10]:
    print(f"  {f['filename']}: $SI={f['si_modified']}, $FN={f['fn_modified']} (delta: {f['delta']})")
PYEOF
Step 3: Analyze Slack Space for Hidden Data
bash
# Search slack space for strings
strings -a /cases/case-2024-001/ntfs/slack_space.raw > /cases/case-2024-001/analysis/slack_strings.txt

# Search for specific patterns in slack space
grep -iab "password\|secret\|confidential\|credit.card\|ssn" \
   /cases/case-2024-001/ntfs/slack_space.raw > /cases/case-2024-001/analysis/slack_keywords.txt

# Analyze individual file slack
python3 << 'PYEOF'
import struct

# File slack consists of:
# 1. RAM slack: bytes between file end and next sector boundary (filled with RAM content or zeros)
# 2. Drive slack: remaining sectors in the cluster after the last file sector

# Analyze slack for specific MFT entries
# Using Sleuth Kit to get file slack for a specific file
import subprocess

# Get file details
result = subprocess.run(
    ['istat', '-o', '2048', '/cases/case-2024-001/images/evidence.dd', '14523'],
    capture_output=True, text=True
)
print(result.stdout)

# The output shows data runs - the last cluster may contain slack data
# Calculate slack size: (allocated_size - file_size) bytes
PYEOF

# Search for file signatures in slack space (embedded files)
foremost -t jpg,pdf,zip -i /cases/case-2024-001/ntfs/slack_space.raw \
   -o /cases/case-2024-001/carved/slack_carved/

# Use bulk_extractor to find structured data in slack
bulk_extractor -o /cases/case-2024-001/analysis/bulk_extract/ \
   /cases/case-2024-001/ntfs/slack_space.raw
Step 4: Parse the USN Change Journal
bash
# Parse USN Journal with MFTECmd
MFTECmd.exe -f "C:\cases\ntfs\UsnJrnl_J" --csv "C:\cases\analysis\" --csvf usn_journal.csv

# Python USN Journal parsing
pip install pyusn

python3 << 'PYEOF'
import struct
import csv
from datetime import datetime, timedelta

def parse_usn_record(data, offset):
    """Parse a single USN_RECORD_V2."""
    if offset + 8 > len(data):
        return None, offset

    record_len = struct.unpack_from('<I', data, offset)[0]
    if record_len < 56 or record_len > 65536 or offset + record_len > len(data):
        return None, offset + 8

    major_ver = struct.unpack_from('<H', data, offset + 4)[0]
    if major_ver != 2:
        return None, offset + record_len

    mft_ref = struct.unpack_from('<Q', data, offset + 8)[0] & 0xFFFFFFFFFFFF
    parent_ref = struct.unpack_from('<Q', data, offset + 16)[0] & 0xFFFFFFFFFFFF
    usn = struct.unpack_from('<Q', data, offset + 24)[0]
    timestamp = struct.unpack_from('<Q', data, offset + 32)[0]
    reason = struct.unpack_from('<I', data, offset + 40)[0]
    source_info = struct.unpack_from('<I', data, offset + 44)[0]
    security_id = struct.unpack_from('<I', data, offset + 48)[0]
    file_attrs = struct.unpack_from('<I', data, offset + 52)[0]
    filename_len = struct.unpack_from('<H', data, offset + 56)[0]
    filename_off = struct.unpack_from('<H', data, offset + 58)[0]

    name = data[offset + filename_off:offset + filename_off + filename_len].decode('utf-16-le', errors='ignore')

    # Convert Windows FILETIME to datetime
    ts = datetime(1601, 1, 1) + timedelta(microseconds=timestamp // 10)

    # Decode reason flags
    reasons = []
    reason_flags = {
        0x01: 'DATA_OVERWRITE', 0x02: 'DATA_EXTEND', 0x04: 'DATA_TRUNCATION',
        0x10: 'NAMED_DATA_OVERWRITE', 0x20: 'NAMED_DATA_EXTEND',
        0x100: 'FILE_CREATE', 0x200: 'FILE_DELETE', 0x400: 'EA_CHANGE',
        0x800: 'SECURITY_CHANGE', 0x1000: 'RENAME_OLD_NAME', 0x2000: 'RENAME_NEW_NAME',
        0x4000: 'INDEXABLE_CHANGE', 0x8000: 'BASIC_INFO_CHANGE',
        0x10000: 'HARD_LINK_CHANGE', 0x20000: 'COMPRESSION_CHANGE',
        0x40000: 'ENCRYPTION_CHANGE', 0x80000: 'OBJECT_ID_CHANGE',
        0x100000: 'REPARSE_POINT_CHANGE', 0x200000: 'STREAM_CHANGE',
        0x80000000: 'CLOSE'
    }
    for flag, desc in reason_flags.items():
        if reason & flag:
            reasons.append(desc)

    record = {
        'timestamp': ts.strftime('%Y-%m-%d %H:%M:%S'),
        'filename': name,
        'mft_entry': mft_ref,
        'parent_entry': parent_ref,
        'reasons': '|'.join(reasons),
        'usn': usn
    }

    return record, offset + record_len

# Parse the journal
with open('/cases/case-2024-001/ntfs/UsnJrnl_J', 'rb') as f:
    data = f.read()

records = []
offset = 0
while offset < len(data) - 8:
    record, offset = parse_usn_record(data, offset)
    if record:
        records.append(record)
    else:
        offset += 8  # Skip zeros

# Filter for deletion events
deletions = [r for r in records if 'FILE_DELETE' in r['reasons']]
creations = [r for r in records if 'FILE_CREATE' in r['reasons']]
renames = [r for r in records if 'RENAME_NEW_NAME' in r['reasons']]

print(f"Total USN records: {len(records)}")
print(f"File creations: {len(creations)}")
print(f"File deletions: {len(deletions)}")
print(f"File renames: {len(renames)}")

print("\n=== RECENT DELETIONS ===")
for r in deletions[-20:]:
    print(f"  [{r['timestamp']}] DELETED: {r['filename']} (MFT#{r['mft_entry']})")

# Write full journal to CSV
with open('/cases/case-2024-001/analysis/usn_journal.csv', 'w', newline='') as f:
    writer = csv.DictWriter(f, fieldnames=['timestamp', 'filename', 'mft_entry', 'parent_entry', 'reasons', 'usn'])
    writer.writeheader()
    writer.writerows(records)
PYEOF
Step 5: Detect and Analyze Alternate Data Streams
bash
# List all Alternate Data Streams in the image
find /mnt/evidence -exec getfattr -d {} \; 2>/dev/null | grep -i "ads\|zone\|stream"

# Using Sleuth Kit to find ADS
fls -r -o 2048 /cases/case-2024-001/images/evidence.dd | grep ":" | \
   tee /cases/case-2024-001/analysis/ads_list.txt

# Extract specific ADS content
# Format: icat image inode:ads_name
icat -o 2048 /cases/case-2024-001/images/evidence.dd 14523:hidden_stream \
   > /cases/case-2024-001/analysis/extracted_ads.bin

# Check Zone.Identifier streams (download origin tracking)
fls -r -o 2048 /cases/case-2024-001/images/evidence.dd | grep "Zone.Identifier" | \
   while read line; do
       inode=$(echo "$line" | awk '{print $2}' | tr -d ':')
       echo "=== $line ==="
       icat -o 2048 /cases/case-2024-001/images/evidence.dd "${inode}:Zone.Identifier" 2>/dev/null
       echo ""
   done > /cases/case-2024-001/analysis/zone_identifiers.txt

# Zone.Identifier content reveals:
# [ZoneTransfer]
# ZoneId=3          (3 = Internet, indicating file was downloaded)
# ReferrerUrl=https://malicious-site.com/payload.exe
# HostUrl=https://cdn.malicious-site.com/payload.exe

Key Concepts

ConceptDescription
File slackUnused space between file end and cluster boundary containing residual data
RAM slackPortion of slack from file end to sector boundary (historically filled with RAM)
MFT ($MFT)Master File Table - NTFS metadata database with entries for every file
USN Journal ($UsnJrnl)Change journal recording all file/directory modifications on NTFS
Alternate Data StreamsNTFS feature allowing multiple data streams per file (hidden storage)
$STANDARD_INFORMATIONMFT attribute with timestamps modifiable by user-mode applications
$FILE_NAMEMFT attribute with timestamps only modifiable by the kernel
TimestompingAnti-forensic technique modifying file timestamps to avoid detection

Tools & Systems

ToolPurpose
MFTECmdEric Zimmerman MFT and USN Journal parser with CSV output
MFTExplorerInteractive GUI tool for MFT analysis
analyzeMFTPython MFT parser with CSV/JSON output
The Sleuth KitFile system forensics toolkit (fls, icat, blkls, istat)
bulk_extractorFeature extraction from raw data including slack space
NTFS Log TrackerTool for parsing $LogFile transaction records
streams.exeSysinternals tool for listing NTFS Alternate Data Streams
PlasoSuper-timeline tool parsing MFT and USN Journal
Show full SKILL.md (176 more words)Show less

Common Scenarios

Scenario 1: Anti-Forensics Detection via Timestomping Compare $STANDARD_INFORMATION timestamps with $FILE_NAME timestamps in MFT entries, flag files where $SI timestamps predate $FN timestamps (impossible in normal operation), identify timestomped files as evidence of deliberate manipulation, correlate with other timeline evidence.

Scenario 2: Hidden Data in Alternate Data Streams Scan for ADS attached to files beyond the standard Zone.Identifier, extract ADS content for analysis, check for hidden executables or documents stored in ADS, correlate ADS creation with user activity timeline, document findings for evidence.

Scenario 3: Deleted File Reconstruction from MFT Parse MFT for inactive (deleted) entries, extract filenames, sizes, and timestamps of deleted files, recover file content using icat if data clusters are not overwritten, build list of deleted evidence files, correlate with USN Journal delete events.

Scenario 4: File Activity Reconstruction from USN Journal Parse the USN Change Journal for the investigation period, identify file creation, modification, rename, and deletion events, reconstruct the sequence of file operations, detect evidence of data staging (create, copy, compress, delete pattern), identify anti-forensic file wiping.

Output Format

File System Artifact Analysis:
  Volume: NTFS (Partition 2, 465 GB)
  Cluster Size: 4096 bytes

  MFT Analysis:
    Total Entries: 456,789
    Active Files: 234,567
    Deleted Entries: 12,345 (8,901 with recoverable metadata)
    Timestomped Files: 23 (SI/FN mismatch detected)

  USN Journal:
    Records Parsed: 2,345,678
    Date Range: 2024-01-01 to 2024-01-20
    File Creations: 45,678
    File Deletions: 23,456
    File Renames: 12,345

  Alternate Data Streams:
    Total ADS Found: 1,234
    Zone.Identifier: 890 (downloaded files)
    Custom/Suspicious ADS: 5 (hidden data detected)

  Slack Space:
    Total Slack: 12.3 GB
    Keyword Hits: 45 (passwords, credit cards)
    Carved Files: 23 from slack space

  Suspicious Findings:
    - 23 files with timestomped timestamps
    - 5 files with hidden ADS containing data
    - USN shows mass deletion on 2024-01-18 (anti-forensics)
    - Slack space contains residual email fragments

  Reports: /cases/case-2024-001/analysis/

© 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 3 other files (scripts, references) in skills/analyzing-slack-space-and-file-system-artifacts of mukul975/Anthropic-Cybersecurity-Skills.

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

Open the folder on GitHubat commit 54a7988

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Works with

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Questions about Analyzing Slack Space And File System Artifacts

What does Analyzing Slack Space And File System Artifacts do?

Examine NTFS slack space, MFT entries, the USN Change Journal, and Alternate Data Streams (ADS) to recover hidden or residual data, reconstruct deleted-file metadata, and reconstruct available…. Analyzing Slack Space And File System Artifacts is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Examine NTFS slack space, MFT entries, the USN Change Journal, and Alternate Data Streams (ADS) to recover hidden or residual data, reconstruct deleted-file metadata, and reconstruct available file-system change activity from USN records.

When should I use Analyzing Slack Space And File System Artifacts?

Analyzing Slack Space And File System Artifacts fits situations like: tasks that involve Digital forensics.

How do I install Analyzing Slack Space And File System Artifacts in Claude Code?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-slack-space-and-file-system-artifacts -a claude-code`. Or copy the skill folder (skills/analyzing-slack-space-and-file-system-artifacts in mukul975/Anthropic-Cybersecurity-Skills) into .claude/skills/analyzing-slack-space-and-file-system-artifacts in your project. Claude Code loads it when a task matches its description.

How do I install Analyzing Slack Space And File System Artifacts in Codex?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-slack-space-and-file-system-artifacts -a codex`. Or copy the skill folder (skills/analyzing-slack-space-and-file-system-artifacts in mukul975/Anthropic-Cybersecurity-Skills) into .agents/skills/analyzing-slack-space-and-file-system-artifacts in your project. Codex loads it when a task matches its description.

Can I use Analyzing Slack Space And File System Artifacts 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-slack-space-and-file-system-artifacts -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-slack-space-and-file-system-artifacts, .gemini/skills/analyzing-slack-space-and-file-system-artifacts, .github/skills/analyzing-slack-space-and-file-system-artifacts and .opencode/skills/analyzing-slack-space-and-file-system-artifacts in your project.

What does Analyzing Slack Space And File System Artifacts need to run?

Going by SKILL.md and its folder, Analyzing Slack Space And File System Artifacts needs Python for the scripts in its folder and the command-line tools its instructions call (python3 and pip). Our summary lists: Python 3.

Does Analyzing Slack Space And File System Artifacts access the network?

SKILL.md names 2 domains. In commands or code: malicious-site.com and cdn.malicious-site.com; the agent is likely to contact these when it follows the instructions. This is read from the text; nothing was executed.

Is Analyzing Slack Space And File System Artifacts 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 Slack Space And File System Artifacts use?

Analyzing Slack Space And File System Artifacts 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 Slack Space And File System Artifacts use?

About 3.7k tokens (SKILL.md is roughly 15k 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 484 tokens, read only when the agent opens those files.

What are the alternatives to Analyzing Slack Space And File System Artifacts?

Skills that share tags, products or a category with Analyzing Slack Space And File System Artifacts: Oss Forensics (Tommy-yw/RunbookHermes, 546 stars), Ctf Malware (ljagiello/ctf-skills, 3.4k stars), Dfir (transilienceai/communitytools, 563 stars) and TShark Traffic Analysis (AgentSecOps/SecOpsAgentKit, 220 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Analyzing Slack Space And File System Artifacts?

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.