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 Liberty91LTD/cti-skills --skill iran-cyber-espionagegit clone --depth 1 https://github.com/Liberty91LTD/cti-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/liberty91ltd/cti-skills/iran-cyber-espionage)<a href="https://agentmods.dev/skills/liberty91ltd/cti-skills/iran-cyber-espionage"><img src="https://agentmods.dev/badge/skills/liberty91ltd/cti-skills/iran-cyber-espionage/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/liberty91ltd/cti-skills/iran-cyber-espionage"><img src="https://agentmods.dev/badge/skills/liberty91ltd/cti-skills/iran-cyber-espionage.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00094 | $0.07841 |
| Opus 5.5 | $0.00038 | $0.03136 |
| Sonnet 5.5 | $0.00019 | $0.01568 |
| Haiku 4.5 | $0.00009 | $0.00784 |
Grade A, and why
iran-cyber-espionage 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 233 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Iran Cyber Espionage Knowledge Cell
Executive Summary
Iran's state-sponsored cyber operations are conducted primarily by two organizations: the Islamic Revolutionary Guard Corps (IRGC) and the Ministry of Intelligence and Security (MOIS). IRGC-affiliated groups (APT33, APT35/APT42, Nimbus Manticore/UNC1549, Imperial Kitten, CyberAv3ngers) cover espionage against defense, aerospace and policy targets and attacks on operational technology. MOIS-affiliated groups (APT34/OilRig, MuddyWater, Scarred Manticore, Void Manticore, Cavern Manticore) run sustained espionage against government, telecom and IT providers in the Middle East, and, through Void Manticore's Handala Hack persona, destructive and hack-and-leak operations.
Since this cell was seeded, Iranian cyber activity has been shaped by two wars. During the June 2025 Israel-Iran conflict, US agencies warned of possible Iranian activity against US networks but stated they had not seen a coordinated campaign [10]. Iran itself was the victim of destructive attacks claimed by Predatory Sparrow against Bank Sepah and the Nobitex exchange [11][12]. On 28 February 2026 the United States and Israel began a joint offensive against Iran (Operation Epic Fury / Operation Roaring Lion) [13][14]. A ceasefire announced on 7 April 2026 was declared over on 9 July 2026 [14]. This second conflict brought highly disruptive Iranian-linked activity against US organizations: the 11 March 2026 wipe of Stryker Corporation's managed devices, claimed by Handala [15][16][17], and exploitation of internet-exposed PLCs in US water, energy and government facilities [18][19].
Three shifts stand out. First, destructive operations no longer depend on wiper malware: the Stryker incident used the built-in wipe function of a cloud endpoint management platform [17], and Void Manticore also wipes by hand over RDP or with legitimate disk-encryption software [15]. Second, MOIS actors have retooled. MuddyWater moved from broad abuse of remote management tools to a series of custom backdoors, including Rust implants with Telegram command and control [20][21][22][23]. Third, vendors report AI-assisted malware development by MuddyWater, Void Manticore and Nimbus Manticore [15][23][25]. Long-running social engineering by APT35/APT42 and fake-recruiter operations against aerospace and defense continue [24][25][26][27][28]. US law enforcement and sanctions action against Iranian operators resumed in August 2026 [29][30].
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.
- 1mo ago Changed · +93 lines · +12 tokens per session d4e357ef69f3
- 2mo ago First seen · 140 lines · 82 tokens per session scan A 4c0dd76eb7c5
iran-cyber-espionage is a skill published in the GitHub repository Liberty91LTD/cti-skills (36 stars, last pushed 4d ago), licensed MIT. It adds 94 tokens to every session and 7,841 once invoked, about $0.0004 per session on Opus 5.5. 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
Systematically evaluate cyber-campaign evidence to attribute an operation to a threat actor, using the Diamond Model and Analysis of Competing Hypotheses (ACH) to weigh infrastructure overlaps, TTP consistency, malware code similarity, and timing/language artifacts into confidence-weighted attribution assessments. Use…
analyzing-threat-actor-ttps-with-mitre-attack
MITRE ATT&CK is a globally-accessible knowledge base of adversary tactics, techniques, and procedures (TTPs) based on real-world observations. This skill covers systematically mapping threat actor beh.
analyzing-threat-landscape-with-misp
Analyze the threat landscape using MISP (Malware Information Sharing Platform) by querying event statistics, attribute distributions, threat actor galaxy clusters, and tag trends over time. Uses PyMISP to pull event data, compute IOC type breakdowns, identify top threat actors and malware families, and generate threat…
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.
prompt-injection-auditor
Security audit of LLM system prompts, agent instruction files (SKILL.md, AGENTS.md, CLAUDE.md), and agent configurations against prompt injection attacks. Use when the user wants to (1) audit or harden a system prompt or agent instructions against prompt injection, (2) review an agent skill or system prompt for…
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.