RRepoGEO

REPOGEO REPORT · LITE

trustedsec/hate_crack

Default branch main · commit 1b28bdbd · scanned 5/16/2026, 4:32:04 PM

GitHub: 1,828 stars · 280 forks

AI VISIBILITY SCORE
28 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface trustedsec/hate_crack, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    hashcat, password-cracking, security-auditing, penetration-testing, red-team, automation, wordlist-generation, hash-cracking
  • highlicense#2
    Add a LICENSE file to the repository root

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a LICENSE file in the repository root. Choose an appropriate open-source license (e.g., MIT, Apache-2.0, GPL-3.0) and paste its full text into this new file.
  • highreadme#3
    Add a concise, benefit-oriented opening paragraph to the README

    Why:

    COPY-PASTE FIX
    hate_crack is an advanced automation framework designed to streamline and enhance password cracking methodologies using Hashcat. It provides a simplified interface for complex Hashcat workflows, automates wordlist generation, and manages cracking sessions, making it an indispensable tool for penetration testers, red teamers, and security auditors looking to efficiently test password strength and identify vulnerabilities.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface trustedsec/hate_crack
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Cain & Abel
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Cain & Abel · recommended 2×
  2. Hashcat · recommended 1×
  3. John the Ripper (JtR) · recommended 1×
  4. Hydra · recommended 1×
  5. Aircrack-ng · recommended 1×
  • CATEGORY QUERY
    How can I automate common password cracking techniques for security auditing?
    you: not recommended
    AI recommended (in order):
    1. Hashcat
    2. John the Ripper (JtR)
    3. Hydra
    4. Aircrack-ng
    5. Cain & Abel
    6. Medusa

    AI recommended 6 alternatives but never named trustedsec/hate_crack. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What's a good utility to streamline various hash cracking attacks and wordlist management?
    you: not recommended
    AI recommended (in order):
    1. Hashcat (hashcat/hashcat)
    2. John the Ripper (openwall/john)
    3. Crunch (crunch/crunch)
    4. CeWL (digininja/CeWL)
    5. Packer
    6. Ophcrack
    7. Cain & Abel

    AI recommended 7 alternatives but never named trustedsec/hate_crack. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of trustedsec/hate_crack?
    pass
    AI did not name trustedsec/hate_crack — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts trustedsec/hate_crack in production, what risks or prerequisites should they evaluate first?
    pass
    AI named trustedsec/hate_crack explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo trustedsec/hate_crack solve, and who is the primary audience?
    pass
    AI named trustedsec/hate_crack explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of trustedsec/hate_crack. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/trustedsec/hate_crack.svg)](https://repogeo.com/en/r/trustedsec/hate_crack)
HTML
<a href="https://repogeo.com/en/r/trustedsec/hate_crack"><img src="https://repogeo.com/badge/trustedsec/hate_crack.svg" alt="RepoGEO" /></a>
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trustedsec/hate_crack — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite