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 marysatasselshaped667/skills-collection-1 --skill analyzing-threat-landscape-with-mispgit clone --depth 1 https://github.com/marysatasselshaped667/skills-collection-1Wrote 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/marysatasselshaped667/skills-collection-1/analyzing-threat-landscape-with-misp)<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.
<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>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 | $0.00075 | $0.00456 |
| Opus 5 | $0.00037 | $0.00228 |
| Sonnet 5 | $0.00015 | $0.00091 |
| Haiku 4.5 | $0.00007 | $0.00046 |
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.
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.
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.
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
- Install dependencies:
pip install pymisp - Configure MISP URL and API key.
- 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)
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.
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.
- 8d ago First seen · 61 lines · 75 tokens per session scan A d8640f79b433
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.
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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…
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