Anthropic Cybersecurity Skills is a library of structured cybersecurity procedures for AI agents, covering security domains and mappings to established security frameworks. It is for authorized security analysis, penetration testing, incident response, research, defense, and education across compatible AI platforms. The catalogue entries package parts of this library as agent skills, instructions, or a plugin.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-campaign-attribution-evidencegit clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-SkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/mukul975/anthropic-cybersecurity-skills/analyzing-campaign-attribution-evidence)<a href="https://agentmods.dev/skills/mukul975/anthropic-cybersecurity-skills/analyzing-campaign-attribution-evidence"><img src="https://agentmods.dev/badge/skills/mukul975/anthropic-cybersecurity-skills/analyzing-campaign-attribution-evidence/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/mukul975/anthropic-cybersecurity-skills/analyzing-campaign-attribution-evidence"><img src="https://agentmods.dev/badge/skills/mukul975/anthropic-cybersecurity-skills/analyzing-campaign-attribution-evidence.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00079 | $0.02026 |
| Opus 5 | $0.00039 | $0.01013 |
| Sonnet 5 | $0.00016 | $0.00405 |
| Haiku 4.5 | $0.00008 | $0.00203 |
Grade A, and why
analyzing-campaign-attribution-evidence scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 13d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
7 near-identical copies found in the catalogue:
- analyzing-campaign-attribution-evidence — 100% identical, 0 lines differ
- analyzing-campaign-attribution-evidence — 89% identical, 23 lines differ
- analyzing-campaign-attribution-evidence — 89% identical, 23 lines differ
- analyzing-campaign-attribution-evidence — 89% identical, 23 lines differ
- analyzing-campaign-attribution-evidence — 88% identical, 8 lines differ
- analyzing-campaign-attribution-evidence — 88% identical, 4 lines differ
- analyzing-campaign-attribution-evidence — 86% identical, 33 lines differ
How it starts
The opening of the file, as written. The whole thing — 242 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyzing Campaign Attribution Evidence
Overview
Campaign attribution analysis involves systematically evaluating evidence to determine which threat actor or group is responsible for a cyber operation. This skill covers collecting and weighting attribution indicators using the Diamond Model and ACH (Analysis of Competing Hypotheses), analyzing infrastructure overlaps, TTP consistency, malware code similarities, operational timing patterns, and language artifacts to build confidence-weighted attribution assessments.
When to Use
- When investigating security incidents that require analyzing campaign attribution evidence
- 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
attackcti,stix2,networkxlibraries - Access to threat intelligence platforms (MISP, OpenCTI)
- Understanding of Diamond Model of Intrusion Analysis
- Familiarity with MITRE ATT&CK threat group profiles
- Knowledge of malware analysis and infrastructure tracking techniques
Key Concepts
Attribution Evidence Categories
- Infrastructure Overlap: Shared C2 servers, domains, IP ranges, hosting providers
- TTP Consistency: Matching ATT&CK techniques and sub-techniques across campaigns
- Malware Code Similarity: Shared code bases, compilers, PDB paths, encryption routines
- Operational Patterns: Timing (working hours, time zones), targeting patterns, operational tempo
- Language Artifacts: Embedded strings, variable names, error messages in specific languages
- Victimology: Target sector, geography, and organizational profile consistency
Confidence Levels
- High Confidence: Multiple independent evidence categories converge on same actor
- Moderate Confidence: Several evidence categories match, some ambiguity remains
- Low Confidence: Limited evidence, possible false flags or shared tooling
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 13d ago First seen · 242 lines · 79 tokens per session scan A 6b03f30f7b1f
analyzing-campaign-attribution-evidence is a skill published in the GitHub repository mukul975/Anthropic-Cybersecurity-Skills (32,631 stars, last pushed 12d ago), licensed Apache-2.0. It adds 79 tokens to every session and 2,026 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
analyzing-campaign-attribution-evidence
Use when campaign attribution analysis involves systematically evaluating evidence to determine which threat actor or group is responsible for a cyber operation. This skill covers collecting and weighting attr.
analyzing-campaign-attribution-evidence
Campaign attribution analysis involves systematically evaluating evidence to determine which threat actor or group is responsible for a cyber operation. This skill covers collecting and weighting attr.
analyzing-campaign-attribution-evidence
A skill for analysing evidence to estimate which threat actor or organisation is responsible for a cyberattack. It uses structured methods such as the Diamond Model and Analysis of Competing Hypotheses.
analyzing-campaign-attribution-evidence
Campaign attribution analysis involves systematically evaluating evidence to determine which threat actor or group is responsible for a cyber operation. This skill covers collecting and weighting attr.
analyzing-campaign-attribution-evidence
Campaign attribution analysis involves systematically evaluating evidence to determine which threat actor or group is responsible for a cyber operation. This skill covers collecting and weighting attr.
analyzing-campaign-attribution-evidence
Campaign attribution analysis involves systematically evaluating evidence to determine which threat actor or group is responsible for a cyber operation. This skill covers collecting and weighting attr.