Agent skill

Analyzing Certificate Transparency For Phishing

by mukul975 in mukul975/Anthropic-Cybersecurity-Skills

Monitor Certificate Transparency logs using crt.sh and Certstream to detect phishing domains, lookalike certificates, and unauthorized certificate issuance targeting your organization.

Apache-2.0Auto-check passedSecurity

Install Analyzing Certificate Transparency For Phishing

skills CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-certificate-transparency-for-phishing -a claude-code

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

GitHub CLI
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills analyzing-certificate-transparency-for-phishing --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-certificate-transparency-for-phishing .claude/skills/analyzing-certificate-transparency-for-phishing && 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-certificate-transparency-for-phishing
GitHub stars
34k
Token cost
~3.5k tokens
SKILL.md length
397 words
Files
4 (incl. scripts, references)
Skills in repo
644
Repo updated
First seen
Licence
Apache-2.0

At a glance

Monitor Certificate Transparency logs using crt.sh and Certstream to detect phishing domains, lookalike certificates, and unauthorized certificate issuance targeting your organization.

  • Works in 4 steps: Query crt.sh for Certificate History → Real-Time Monitoring with Certstream → Enumerate Subdomains from CT Logs → …
  • Security work in your project
  • SKILL.md covers Overview, When to Use, Prerequisites and Key Concepts, plus 3 more sections
  • Runs Python scripts from its folder; reaches crt.sh

What it does

Analyzing Certificate Transparency For Phishing is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Monitor Certificate Transparency logs using crt.sh and Certstream to detect phishing domains, lookalike certificates, and unauthorized certificate issuance targeting your organization.

Its SKILL.md is about 3.5k 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. 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

  • Security work in your project

Example prompts

  • “/analyzing-certificate-transparency-for-phishing”

Requirements

  • Python 3

Workflow steps

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

  1. Query crt.sh for Certificate History
  2. Real-Time Monitoring with Certstream
  3. Enumerate Subdomains from CT Logs
  4. Generate CT Intelligence Report

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

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

    • crt.sh

    Also links to:

    • certstream.calidog.io
    • riversecurity.eu
    • letsencrypt.org
    • sslmate.com
    • cybersierra.co

    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 Certificate Transparency For Phishing loads about 3.5k tokens when it runs, and up to ~4.2k if it reads all its reference files. Until then it costs about 58 tokens; SKILL.md has 397 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~58
When it runs · the whole SKILL.md, loaded when a task matches
~3.5k
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). 397 words, ~3,522 tokens.

Download SKILL.mdSave it as .claude/skills/analyzing-certificate-transparency-for-phishing/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
analyzing-certificate-transparency-for-phishing
description
Monitor Certificate Transparency logs using crt.sh and Certstream to detect phishing domains, lookalike certificates, and unauthorized certificate issuance targeting your organization.
domain
cybersecurity
subdomain
threat-intelligence
tags
certificate-transparency, ct-logs, phishing, crt-sh, certstream, ssl, domain-monitoring, threat-intelligence
version
1.0
author
mahipal
license
Apache-2.0
atlas_techniques
AML.T0052
nist_csf
ID.RA-01, ID.RA-05, DE.CM-01, DE.AE-02
mitre_attack
T1583.001, T1583.004, T1566.002, T1608.005, T1596.003
mitre_f3.version
1.1

Analyzing Certificate Transparency for Phishing

Overview

Certificate Transparency (CT) is an Internet security standard that creates a public, append-only log of all issued SSL/TLS certificates. Monitoring CT logs enables early detection of phishing domains that register certificates mimicking legitimate brands, unauthorized certificate issuance for owned domains, and certificate-based attack infrastructure. This skill covers querying CT logs via crt.sh, real-time monitoring with Certstream, building automated alerting for suspicious certificates, and integrating findings into threat intelligence workflows.

When to Use

  • When investigating security incidents that require analyzing certificate transparency for phishing
  • 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

  • Python 3.9+ with requests, certstream, tldextract, Levenshtein libraries
  • Access to crt.sh (https://crt.sh/) for historical CT log queries
  • Certstream (https://certstream.calidog.io/) for real-time monitoring
  • List of organization domains and brand keywords to monitor
  • Understanding of SSL/TLS certificate structure and issuance process

Key Concepts

Certificate Transparency Logs

CT logs are cryptographically assured, publicly auditable, append-only records of TLS certificate issuance. Major CAs (Let's Encrypt, DigiCert, Sectigo, Google Trust Services) submit all issued certificates to multiple CT logs. As of 2025, Chrome and Safari require CT for all publicly trusted certificates.

Phishing Detection via CT

Attackers register lookalike domains and obtain free certificates (often from Let's Encrypt) to make phishing sites appear legitimate with HTTPS. CT monitoring detects these early because the certificate appears in logs before the phishing campaign launches, providing a window for proactive blocking.

Show full SKILL.md (144 more words)Show less
crt.sh Database

crt.sh is a free web interface and PostgreSQL database operated by Sectigo that indexes CT logs. It supports wildcard searches (%.example.com), direct SQL queries, and JSON API responses. It tracks certificate issuance, expiration, and revocation across all major CT logs.

Workflow

Step 1: Query crt.sh for Certificate History
python
import requests
import json
from datetime import datetime
import tldextract

class CTLogMonitor:
    CRT_SH_URL = "https://crt.sh"

    def __init__(self, monitored_domains, brand_keywords):
        self.monitored_domains = monitored_domains
        self.brand_keywords = [k.lower() for k in brand_keywords]

    def query_crt_sh(self, domain, include_expired=False):
        """Query crt.sh for certificates matching a domain."""
        params = {
            "q": f"%.{domain}",
            "output": "json",
        }
        if not include_expired:
            params["exclude"] = "expired"

        resp = requests.get(self.CRT_SH_URL, params=params, timeout=30)
        if resp.status_code == 200:
            certs = resp.json()
            print(f"[+] crt.sh: {len(certs)} certificates for *.{domain}")
            return certs
        return []

    def find_suspicious_certs(self, domain):
        """Find certificates that may be phishing attempts."""
        certs = self.query_crt_sh(domain)
        suspicious = []

        for cert in certs:
            common_name = cert.get("common_name", "").lower()
            name_value = cert.get("name_value", "").lower()
            issuer = cert.get("issuer_name", "")
            not_before = cert.get("not_before", "")
            not_after = cert.get("not_after", "")

            # Check for exact domain matches (legitimate)
            extracted = tldextract.extract(common_name)
            cert_domain = f"{extracted.domain}.{extracted.suffix}"
            if cert_domain == domain:
                continue  # Legitimate certificate

            # Flag suspicious patterns
            flags = []
            if domain.replace(".", "") in common_name.replace(".", ""):
                flags.append("contains target domain string")
            if any(kw in common_name for kw in self.brand_keywords):
                flags.append("contains brand keyword")
            if "let's encrypt" in issuer.lower():
                flags.append("free CA (Let's Encrypt)")

            if flags:
                suspicious.append({
                    "common_name": cert.get("common_name", ""),
                    "name_value": cert.get("name_value", ""),
                    "issuer": issuer,
                    "not_before": not_before,
                    "not_after": not_after,
                    "serial": cert.get("serial_number", ""),
                    "flags": flags,
                    "crt_sh_id": cert.get("id", ""),
                    "crt_sh_url": f"https://crt.sh/?id={cert.get('id', '')}",
                })

        print(f"[+] Found {len(suspicious)} suspicious certificates")
        return suspicious

monitor = CTLogMonitor(
    monitored_domains=["mycompany.com", "mycompany.org"],
    brand_keywords=["mycompany", "mybrand", "myproduct"],
)
suspicious = monitor.find_suspicious_certs("mycompany.com")
for cert in suspicious[:5]:
    print(f"  [{cert['common_name']}] Flags: {cert['flags']}")
Step 2: Real-Time Monitoring with Certstream
python
import certstream
import Levenshtein
import re
from datetime import datetime

class CertstreamMonitor:
    def __init__(self, watched_domains, brand_keywords, similarity_threshold=0.8):
        self.watched_domains = [d.lower() for d in watched_domains]
        self.brand_keywords = [k.lower() for k in brand_keywords]
        self.threshold = similarity_threshold
        self.alerts = []

    def start_monitoring(self, max_alerts=100):
        """Start real-time CT log monitoring."""
        print("[*] Starting Certstream monitoring...")
        print(f"    Watching: {self.watched_domains}")
        print(f"    Keywords: {self.brand_keywords}")

        def callback(message, context):
            if message["message_type"] == "certificate_update":
                data = message["data"]
                leaf = data.get("leaf_cert", {})
                all_domains = leaf.get("all_domains", [])

                for domain in all_domains:
                    domain_lower = domain.lower().strip("*.")
                    if self._is_suspicious(domain_lower):
                        alert = {
                            "domain": domain,
                            "all_domains": all_domains,
                            "issuer": leaf.get("issuer", {}).get("O", ""),
                            "fingerprint": leaf.get("fingerprint", ""),
                            "not_before": leaf.get("not_before", ""),
                            "detected_at": datetime.now().isoformat(),
                            "reason": self._get_reason(domain_lower),
                        }
                        self.alerts.append(alert)
                        print(f"  [ALERT] {domain} - {alert['reason']}")

                        if len(self.alerts) >= max_alerts:
                            raise KeyboardInterrupt

        try:
            certstream.listen_for_events(callback, url="wss://certstream.calidog.io/")
        except KeyboardInterrupt:
            print(f"\n[+] Monitoring stopped. {len(self.alerts)} alerts collected.")
        return self.alerts

    def _is_suspicious(self, domain):
        """Check if domain is suspicious relative to watched domains."""
        for watched in self.watched_domains:
            # Exact keyword match
            watched_base = watched.split(".")[0]
            if watched_base in domain and domain != watched:
                return True

            # Levenshtein distance (typosquatting detection)
            domain_base = tldextract.extract(domain).domain
            similarity = Levenshtein.ratio(watched_base, domain_base)
            if similarity >= self.threshold and domain_base != watched_base:
                return True

        # Brand keyword match
        for keyword in self.brand_keywords:
            if keyword in domain:
                return True

        return False

    def _get_reason(self, domain):
        """Determine why domain was flagged."""
        reasons = []
        for watched in self.watched_domains:
            watched_base = watched.split(".")[0]
            if watched_base in domain:
                reasons.append(f"contains '{watched_base}'")
            domain_base = tldextract.extract(domain).domain
            similarity = Levenshtein.ratio(watched_base, domain_base)
            if similarity >= self.threshold and domain_base != watched_base:
                reasons.append(f"similar to '{watched}' ({similarity:.0%})")
        for kw in self.brand_keywords:
            if kw in domain:
                reasons.append(f"brand keyword '{kw}'")
        return "; ".join(reasons) if reasons else "unknown"

cs_monitor = CertstreamMonitor(
    watched_domains=["mycompany.com"],
    brand_keywords=["mycompany", "mybrand"],
    similarity_threshold=0.75,
)
alerts = cs_monitor.start_monitoring(max_alerts=50)
Step 3: Enumerate Subdomains from CT Logs
python
def enumerate_subdomains_ct(domain):
    """Discover all subdomains from Certificate Transparency logs."""
    params = {"q": f"%.{domain}", "output": "json"}
    resp = requests.get("https://crt.sh", params=params, timeout=30)

    if resp.status_code != 200:
        return []

    certs = resp.json()
    subdomains = set()
    for cert in certs:
        name_value = cert.get("name_value", "")
        for name in name_value.split("\n"):
            name = name.strip().lower()
            if name.endswith(f".{domain}") or name == domain:
                name = name.lstrip("*.")
                subdomains.add(name)

    sorted_subs = sorted(subdomains)
    print(f"[+] CT subdomain enumeration for {domain}: {len(sorted_subs)} subdomains")
    return sorted_subs

subdomains = enumerate_subdomains_ct("example.com")
for sub in subdomains[:20]:
    print(f"  {sub}")
Step 4: Generate CT Intelligence Report
python
def generate_ct_report(suspicious_certs, certstream_alerts, domain):
    report = f"""# Certificate Transparency Intelligence Report
## Target Domain: {domain}
## Generated: {datetime.now().isoformat()}

## Summary
- Suspicious certificates found: {len(suspicious_certs)}
- Real-time alerts triggered: {len(certstream_alerts)}

## Suspicious Certificates (crt.sh)
| Common Name | Issuer | Flags | crt.sh Link |
|------------|--------|-------|-------------|
"""
    for cert in suspicious_certs[:20]:
        flags = "; ".join(cert.get("flags", []))
        report += (f"| {cert['common_name']} | {cert['issuer'][:30]} "
                   f"| {flags} | [View]({cert['crt_sh_url']}) |\n")

    report += f"""
## Real-Time Certstream Alerts
| Domain | Issuer | Reason | Detected |
|--------|--------|--------|----------|
"""
    for alert in certstream_alerts[:20]:
        report += (f"| {alert['domain']} | {alert['issuer']} "
                   f"| {alert['reason']} | {alert['detected_at'][:19]} |\n")

    report += """
## Recommendations
1. Add flagged domains to DNS sinkhole / web proxy blocklist
2. Submit takedown requests for confirmed phishing domains
3. Monitor CT logs continuously for new certificate registrations
4. Implement CAA DNS records to restrict certificate issuance for your domains
5. Deploy DMARC to prevent email spoofing from lookalike domains
"""
    with open(f"ct_report_{domain.replace('.','_')}.md", "w") as f:
        f.write(report)
    print(f"[+] CT report saved")
    return report

generate_ct_report(suspicious, alerts if 'alerts' in dir() else [], "mycompany.com")

Validation Criteria

  • crt.sh queries return certificate data for target domains
  • Suspicious certificates identified based on lookalike patterns
  • Certstream real-time monitoring detects new phishing certificates
  • Subdomain enumeration produces comprehensive list from CT logs
  • Alerts generated with reason classification
  • CT intelligence report created with actionable recommendations

References

© 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-certificate-transparency-for-phishing 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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Categories

Questions about Analyzing Certificate Transparency For Phishing

What does Analyzing Certificate Transparency For Phishing do?

Monitor Certificate Transparency logs using crt.sh and Certstream to detect phishing domains, lookalike certificates, and unauthorized certificate issuance targeting your organization. Analyzing Certificate Transparency For Phishing is an agent skill from mukul975/Anthropic-Cybersecurity-Skills.sh and Certstream to detect phishing domains, lookalike certificates, and unauthorized certificate issuance targeting your organization.

When should I use Analyzing Certificate Transparency For Phishing?

Analyzing Certificate Transparency For Phishing fits situations like: security work in your project.

How do I install Analyzing Certificate Transparency For Phishing in Claude Code?

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

How do I install Analyzing Certificate Transparency For Phishing in Codex?

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

Can I use Analyzing Certificate Transparency For Phishing 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-certificate-transparency-for-phishing -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-certificate-transparency-for-phishing, .gemini/skills/analyzing-certificate-transparency-for-phishing, .github/skills/analyzing-certificate-transparency-for-phishing and .opencode/skills/analyzing-certificate-transparency-for-phishing in your project.

What does Analyzing Certificate Transparency For Phishing need to run?

Going by SKILL.md and its folder, Analyzing Certificate Transparency For Phishing needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Analyzing Certificate Transparency For Phishing access the network?

SKILL.md names 6 domains. In commands or code: crt.sh; the agent is likely to contact it when it follows the instructions. As links in the text: certstream.calidog.io, riversecurity.eu, letsencrypt.org, sslmate.com and cybersierra.co. This is read from the text; nothing was executed.

Is Analyzing Certificate Transparency For Phishing 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 Certificate Transparency For Phishing use?

Analyzing Certificate Transparency For Phishing 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 Certificate Transparency For Phishing use?

About 3.5k tokens (SKILL.md is roughly 14k 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 713 tokens, read only when the agent opens those files.

What are the alternatives to Analyzing Certificate Transparency For Phishing?

Skills that share tags, products or a category with Analyzing Certificate Transparency For Phishing: Deepsec Documentation Guide (vercel-labs/deepsec, 8.1k stars), Skill Scanner (getsentry/skills, 1k stars), Serenity Aleabitoreddit (yan-labs/serenity-aleabitoreddit, 481 stars) and Security Alert Triage (elastic/agent-skills, 592 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Analyzing Certificate Transparency For Phishing?

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.