Prose Style
avelikiy/great_cto
Reusable writing-style contract for agent outputs (reports, ARCH docs, verdicts, threat models).
Agent skill
Detecting Business Email Compromise With AI
Deploy AI and NLP-powered detection systems to identify business email compromise attacks by analyzing writing style, behavioral patterns, and contextual anomalies that evade traditional rule-based…
Install Detecting Business Email Compromise With AI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-business-email-compromise-with-ai -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills detecting-business-email-compromise-with-ai --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/detecting-business-email-compromise-with-ai .claude/skills/detecting-business-email-compromise-with-ai && rm -rf skills-srcUse ~/.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/
Install the "detecting-business-email-compromise-with-ai" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/detecting-business-email-compromise-with-ai into .claude/skills/detecting-business-email-compromise-with-ai/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "detecting-business-email-compromise-with-ai", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/detecting-business-email-compromise-with-aiType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-business-email-compromise-with-ai -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills detecting-business-email-compromise-with-ai --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/detecting-business-email-compromise-with-ai .agents/skills/detecting-business-email-compromise-with-ai && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "detecting-business-email-compromise-with-ai" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/detecting-business-email-compromise-with-ai into .agents/skills/detecting-business-email-compromise-with-ai/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "detecting-business-email-compromise-with-ai", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-business-email-compromise-with-ai -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills detecting-business-email-compromise-with-ai --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/detecting-business-email-compromise-with-ai .cursor/skills/detecting-business-email-compromise-with-ai && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "detecting-business-email-compromise-with-ai" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/detecting-business-email-compromise-with-ai into .cursor/skills/detecting-business-email-compromise-with-ai/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "detecting-business-email-compromise-with-ai", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git --path skills/detecting-business-email-compromise-with-ai--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-business-email-compromise-with-ai -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills detecting-business-email-compromise-with-ai --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/detecting-business-email-compromise-with-ai .gemini/skills/detecting-business-email-compromise-with-ai && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "detecting-business-email-compromise-with-ai" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/detecting-business-email-compromise-with-ai into .gemini/skills/detecting-business-email-compromise-with-ai/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "detecting-business-email-compromise-with-ai", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills detecting-business-email-compromise-with-aiInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-business-email-compromise-with-ai -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/detecting-business-email-compromise-with-ai .github/skills/detecting-business-email-compromise-with-ai && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "detecting-business-email-compromise-with-ai" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/detecting-business-email-compromise-with-ai into .github/skills/detecting-business-email-compromise-with-ai/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "detecting-business-email-compromise-with-ai", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-business-email-compromise-with-ai -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills detecting-business-email-compromise-with-ai --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/detecting-business-email-compromise-with-ai .opencode/skills/detecting-business-email-compromise-with-ai && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "detecting-business-email-compromise-with-ai" agent skill from https://github.com/mukul975/Anthropic-Cybersecurity-Skills/tree/main/skills/detecting-business-email-compromise-with-ai into .opencode/skills/detecting-business-email-compromise-with-ai/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "detecting-business-email-compromise-with-ai", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
detecting-business-email-compromise-with-ai- GitHub stars
- 34k
- Token cost
- ~1.4k tokens
- SKILL.md length
- 521 words
- Files
- 8 (incl. scripts, references, assets)
- Skills in repo
- 644
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Deploy AI and NLP-powered detection systems to identify business email compromise attacks by analyzing writing style, behavioral patterns, and contextual anomalies that evade traditional rule-based…
- Works in 5 steps: Deploy AI Email Security Platform → Configure Behavioral Baselines → Train NLP Models for BEC Detection → …
- Tasks that involve Natural language processing
- SKILL.md covers Overview, When to Use, Prerequisites and Workflow, plus 2 more sections
- Runs Python scripts from its folder
What it does
Detecting Business Email Compromise With AI is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Deploy AI and NLP-powered detection systems to identify business email compromise attacks by analyzing writing style, behavioral patterns, and contextual anomalies that evade traditional rule-based filters.
Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files, including scripts, reference files and assets (for example `assets/template.md`, `references/api-reference.md` and `references/standards.md`).
It sits in Security, covering Natural language processing and Brand voice and tone. 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 Natural language processing
- Tasks that involve Brand voice and tone
Example prompts
- “/detecting-business-email-compromise-with-ai”
Requirements
- Python 3
Workflow steps
5 steps, taken from the step headings in SKILL.md.
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 2 files in scripts/ (Python), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
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
Detecting Business Email Compromise With AI loads about 1.4k tokens when it runs, and up to ~2.7k if it reads all its reference files. Until then it costs about 63 tokens; SKILL.md has 521 words of instructions outside code blocks.
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). 521 words, ~1,424 tokens.
Download SKILL.mdSave it as
.claude/skills/detecting-business-email-compromise-with-ai/SKILL.md (or your agent's skills folder). This skill also uses 7 other files; get the full folder from GitHub.- name
- detecting-business-email-compromise-with-ai
- description
- Deploy AI and NLP-powered detection systems to identify business email compromise attacks by analyzing writing style, behavioral patterns, and contextual anomalies that evade traditional rule-based filters.
- domain
- cybersecurity
- subdomain
- phishing-defense
- tags
- bec, ai, nlp, machine-learning, email-security, behavioral-analytics, impersonation, fraud-detection
- version
- 1.0
- author
- mahipal
- license
- Apache-2.0
- atlas_techniques
- AML.T0073, AML.T0052, AML.T0088
- nist_ai_rmf
- GOVERN-6.2, MAP-5.2, GOVERN-6.1, MEASURE-2.7, MEASURE-2.5
- d3fend_techniques
- Sender MTA Reputation Analysis, Email Filtering, Sender Reputation Analysis, Homoglyph Detection, Message Analysis
- nist_csf
- PR.AT-01, DE.CM-09, RS.CO-02, DE.AE-02
Detecting Business Email Compromise with AI
Overview
AI-powered BEC detection uses machine learning, NLP, and behavioral analytics to identify sophisticated impersonation attacks that contain no malicious links or attachments. Traditional rule-based filters miss these attacks because BEC relies purely on social engineering. Modern AI approaches analyze writing style, tone, vocabulary, grammatical patterns, and behavioral context to determine if an email genuinely comes from the stated sender. BERT-based models achieve 98.65% accuracy in BEC detection, and AI-enhanced platforms show a 25% increase in phishing identification over keyword-based rules.
When to Use
- When investigating security incidents that require detecting business email compromise with ai
- 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
- AI-powered email security platform (Abnormal Security, Tessian, Microsoft Defender)
- Historical email data for baseline training (minimum 30 days)
- Integration with email platform (Microsoft 365 or Google Workspace)
- SIEM for alert correlation and investigation
- Understanding of BEC attack types (FBI IC3 classification)
Workflow
Step 1: Deploy AI Email Security Platform
- Select API-based solution (Abnormal Security, Tessian, Ironscales) or enhance existing SEG
- Connect to Microsoft Graph API or Google Workspace API
- Allow 48-hour baseline learning period on historical email data
- Configure integration to scan inbound, outbound, and internal email
- Verify API permissions for message access and remediation
Step 2: Configure Behavioral Baselines
- AI learns normal communication patterns: who emails whom, frequency, tone
- Establish writing style profiles for each user (vocabulary, sentence structure)
- Map typical request types per role (finance processes payments, HR handles PII)
- Baseline email metadata: typical sending times, devices, locations
- Flag deviations from established baselines as anomalous
Step 3: Train NLP Models for BEC Detection
- Deploy transformer-based models (BERT, GPT) for email content analysis
- Detect urgency and manipulation language patterns
- Identify mismatches between sender identity and writing style
- Analyze sentiment shifts indicating social engineering pressure
- Classify email intent: information request, payment request, credential request
Show full SKILL.md (194 more words)Show less
Step 4: Configure Detection Policies
- VIP impersonation: AI compares new email against known executive communication patterns
- Vendor impersonation: detect payment change requests from vendor lookalike domains
- Account compromise: detect sudden changes in employee email behavior
- Supply chain BEC: monitor for impersonation of trusted partners
- Configure confidence thresholds for auto-block vs. warning banner vs. analyst review
Step 5: Integrate with Response Workflow
- Auto-quarantine high-confidence BEC detections
- Add warning banners for moderate-confidence detections
- Route suspicious emails to SOC analyst queue for review
- Integrate with SOAR for automated response playbooks
- Feed BEC verdicts back into training data for model improvement
Tools & Resources
- Abnormal Security: API-based AI email security with behavioral analysis
- Microsoft Defender for O365: Built-in AI anti-BEC with Impostor Classifier
- Tessian (Proofpoint): AI-powered email security with human layer protection
- Ironscales: AI + human-in-the-loop BEC detection
- Darktrace Email: Self-learning AI for email threat detection
Validation
- AI detects test BEC email with no malicious indicators (pure social engineering)
- Writing style analysis identifies impersonation of known executive
- Behavioral baseline flags unusual payment request from compromised account
- NLP correctly classifies urgency manipulation in test scenario
- False positive rate below 0.05% after baseline training
- Detection rate exceeds traditional rule-based filters by 25%+
© 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 7 other files (scripts, references, assets) in skills/detecting-business-email-compromise-with-ai of mukul975/Anthropic-Cybersecurity-Skills.
- SKILL.md
- LICENSE
- assets/template.md
- references/api-reference.md
- references/standards.md
- references/workflows.md
- scripts/agent.py
- scripts/process.py
Open the folder on GitHubat commit 54a7988
Compare with similar skills
Detecting Business Email Compromise With AI next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Detecting Business Email Compromise With AI this skillmukul975/Anthropic-Cybersecurity-Skills | 34k | — | ~1.4k | Automated safety check: Pass | Apache-2.0 | |
| Prose Styleavelikiy/great_cto | 102 | — | ~1k | Automated safety check: Pass | MIT | |
| Content Analysisliangdabiao/claude-data-analysis-ultra-main | 290 | — | ~1.7k | Automated safety check: Notes | None | |
| Natural Languagedpearson2699/swift-ios-skills | 1.2k | — | ~3.5k | Automated safety check: Pass | Custom licence | |
| Psychbull Writing Stylefranklee16/academic-research-skills | 223 | 1 repos | ~880 | Automated safety check: Pass | None | |
| Pseudonymization Riskmukul975/Privacy-Data-Protection-Skills | 301 | — | ~3.2k | Automated safety check: Pass | Apache-2.0 |
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Related
Questions about Detecting Business Email Compromise With AI
What does Detecting Business Email Compromise With AI do?
Deploy AI and NLP-powered detection systems to identify business email compromise attacks by analyzing writing style, behavioral patterns, and contextual anomalies that evade traditional rule-based…. Detecting Business Email Compromise With AI is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Deploy AI and NLP-powered detection systems to identify business email compromise attacks by analyzing writing style, behavioral patterns, and contextual anomalies that evade traditional rule-based filters.
When should I use Detecting Business Email Compromise With AI?
Detecting Business Email Compromise With AI fits situations like: tasks that involve Natural language processing; tasks that involve Brand voice and tone.
How do I install Detecting Business Email Compromise With AI in Claude Code?
Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-business-email-compromise-with-ai -a claude-code`. Or copy the skill folder (skills/detecting-business-email-compromise-with-ai in mukul975/Anthropic-Cybersecurity-Skills) into .claude/skills/detecting-business-email-compromise-with-ai in your project. Claude Code loads it when a task matches its description.
How do I install Detecting Business Email Compromise With AI in Codex?
Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-business-email-compromise-with-ai -a codex`. Or copy the skill folder (skills/detecting-business-email-compromise-with-ai in mukul975/Anthropic-Cybersecurity-Skills) into .agents/skills/detecting-business-email-compromise-with-ai in your project. Codex loads it when a task matches its description.
Can I use Detecting Business Email Compromise With AI 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 detecting-business-email-compromise-with-ai -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/detecting-business-email-compromise-with-ai, .gemini/skills/detecting-business-email-compromise-with-ai, .github/skills/detecting-business-email-compromise-with-ai and .opencode/skills/detecting-business-email-compromise-with-ai in your project.
What does Detecting Business Email Compromise With AI need to run?
Going by SKILL.md and its folder, Detecting Business Email Compromise With AI needs Python for the scripts in its folder. Our summary lists: Python 3.
Does Detecting Business Email Compromise With AI access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Detecting Business Email Compromise With AI 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 Detecting Business Email Compromise With AI use?
Detecting Business Email Compromise With AI 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 Detecting Business Email Compromise With AI use?
About 1.4k tokens (SKILL.md is roughly 5.7k 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 1.2k tokens, read only when the agent opens those files.
What are the alternatives to Detecting Business Email Compromise With AI?
Skills that share tags, products or a category with Detecting Business Email Compromise With AI: Prose Style (avelikiy/great_cto, 102 stars), Content Analysis (liangdabiao/claude-data-analysis-ultra-main, 290 stars), Natural Language (dpearson2699/swift-ios-skills, 1.2k stars) and Psychbull Writing Style (franklee16/academic-research-skills, 223 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Detecting Business Email Compromise With AI?
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.