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 autohandai/community-skills --skill analyzing-threat-landscape-with-mispgit clone --depth 1 https://github.com/autohandai/community-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/autohandai/community-skills/analyzing-threat-landscape-with-misp)<a href="https://agentmods.dev/skills/autohandai/community-skills/analyzing-threat-landscape-with-misp"><img src="https://agentmods.dev/badge/skills/autohandai/community-skills/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/autohandai/community-skills/analyzing-threat-landscape-with-misp"><img src="https://agentmods.dev/badge/skills/autohandai/community-skills/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 | $0.00343 |
| Opus 5 | $0.00037 | $0.00171 |
| Sonnet 5 | $0.00015 | $0.00069 |
| Haiku 4.5 | $0.00007 | $0.00034 |
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 9d 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
84% identical to analyzing-threat-landscape-with-misp — 56 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
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.
- 9d ago First seen · 43 lines · 75 tokens per session scan A a1884dd843fa
analyzing-threat-landscape-with-misp is a skill published in the GitHub repository autohandai/community-skills (11 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 75 tokens to every session and 343 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 84% identical to analyzing-threat-landscape-with-misp, differing in 56 lines, and is treated as a copy.
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