analyzing-threat-landscape-with-misp

analyzing-threat-landscape-with-misp is a skill for Claude Code, Codex from marysatasselshaped667/skills-collection-1. It costs 75 tokens per session (456 once invoked), scanned A, a copy of analyzing-threat-landscape-with-misp, MIT.

A guide for analyzing threat activity in MISP, a platform for sharing and organizing cyber threat information. It examines events, indicator types, threat-actor groups, malware families, techniques, and changes over time.

In plain words
What is it for?
Use it to pull MISP statistics, break down indicators such as IPs and hashes, track threat-actor activity, analyze ATT&CK tags, and create time-based reports.
Why use it?
Large collections of shared threat data are difficult to summarize by hand. This helps reveal which indicators, actors, malware, and techniques appear most often.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to pull MISP statistics, break down indicators such as IPs and hashes, track threat-actor activity, analyze ATT&CK tags, and create time-based reports.

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Install with agentmods
npx agentmods add skills/marysatasselshaped667/skills-collection-1/analyzing-threat-landscape-with-misp
Install

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.

Any agent
npx skills add marysatasselshaped667/skills-collection-1 --skill analyzing-threat-landscape-with-misp
Clone the repo
git clone --depth 1 https://github.com/marysatasselshaped667/skills-collection-1

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for analyzing-threat-landscape-with-misp

README.md
[![agentmods](https://agentmods.dev/badge/skills/marysatasselshaped667/skills-collection-1/analyzing-threat-landscape-with-misp/github.svg)](https://agentmods.dev/skills/marysatasselshaped667/skills-collection-1/analyzing-threat-landscape-with-misp)
Your own site
<a href="https://agentmods.dev/skills/marysatasselshaped667/skills-collection-1/analyzing-threat-landscape-with-misp"><img src="https://agentmods.dev/badge/skills/marysatasselshaped667/skills-collection-1/analyzing-threat-landscape-with-misp/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.

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Your own site · 80×15
<a href="https://agentmods.dev/skills/marysatasselshaped667/skills-collection-1/analyzing-threat-landscape-with-misp"><img src="https://agentmods.dev/badge/skills/marysatasselshaped667/skills-collection-1/analyzing-threat-landscape-with-misp.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 75 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 456 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 86% copy Near-identical to another mod in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00075 $0.00456
Opus 5 $0.00037 $0.00228
Sonnet 5 $0.00015 $0.00091
Haiku 4.5 $0.00007 $0.00046

Measured 8d ago against content hash d8640f79b433, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

analyzing-threat-landscape-with-misp 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 8d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/agent.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Origin

This is a copy

86% identical to analyzing-threat-landscape-with-misp — 38 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

SKILLS/analyzing-threat-landscape-with-misp/SKILL.md · 61 lines

What it actually says

Analyzing Threat Landscape with MISP

When to Use

  • When investigating security incidents that require analyzing threat landscape with misp
  • 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

  • Familiarity with threat intelligence concepts and tools
  • Access to a test or lab environment for safe execution
  • Python 3.8+ with required dependencies installed
  • Appropriate authorization for any testing activities

Instructions

  1. Install dependencies: pip install pymisp
  2. Configure MISP URL and API key.
  3. Run the agent to generate threat landscape analysis:
    • Pull event statistics by threat level and date range
    • Analyze attribute type distributions (IP, domain, hash, URL)
    • Identify top MITRE ATT&CK techniques from event tags
    • Track threat actor activity via galaxy clusters
    • Generate temporal trend analysis of IOC submissions
python scripts/agent.py --misp-url https://misp.local --api-key YOUR_KEY --days 90 --output landscape_report.json

Examples

Threat Landscape Summary

Period: Last 90 days
Events analyzed: 1,247
Top threat level: High (43%)
Top attribute type: ip-dst (31%), domain (22%), sha256 (18%)
Top MITRE technique: T1566 Phishing (89 events)
Top threat actor: APT28 (34 events)
Files

What ships with it

3 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.

Changes

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.

  1. 8d ago First seen · 61 lines · 75 tokens per session scan A d8640f79b433

Subscribe to this mod's changes

analyzing-threat-landscape-with-misp is a skill published in the GitHub repository marysatasselshaped667/skills-collection-1 (1 stars, last pushed yesterday), licensed MIT. It adds 75 tokens to every session and 456 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to analyzing-threat-landscape-with-misp, differing in 38 lines, and is treated as a copy.

Related

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