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-intelligence-feedsgit 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-intelligence-feeds)<a href="https://agentmods.dev/skills/autohandai/community-skills/analyzing-threat-intelligence-feeds"><img src="https://agentmods.dev/badge/skills/autohandai/community-skills/analyzing-threat-intelligence-feeds/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-intelligence-feeds"><img src="https://agentmods.dev/badge/skills/autohandai/community-skills/analyzing-threat-intelligence-feeds.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 | $0.00098 | $0.01431 |
| Opus 5 | $0.00049 | $0.00715 |
| Sonnet 5 | $0.00020 | $0.00286 |
| Haiku 4.5 | $0.00010 | $0.00143 |
Grade A, and why
analyzing-threat-intelligence-feeds 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
92% identical to analyzing-threat-intelligence-feeds — 36 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.
How it starts
The opening of the file, as written. The whole thing — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyzing Threat Intelligence Feeds
When to Use
Use this skill when:
- Ingesting new commercial or OSINT threat feeds and assessing their signal-to-noise ratio
- Normalizing heterogeneous IOC formats (STIX 2.1, OpenIOC, YARA, Sigma) into a unified schema
- Evaluating feed freshness, fidelity, and relevance to the organization's threat profile
- Building automated enrichment pipelines that correlate IOCs against SIEM events
Do not use this skill for raw packet capture analysis or live incident triage without first establishing a CTI baseline.
Prerequisites
- Access to a Threat Intelligence Platform (TIP) such as ThreatConnect, MISP, or OpenCTI
- API keys for at least one commercial feed (Recorded Future, Mandiant Advantage, or VirusTotal Enterprise)
- TAXII 2.1 client library (taxii2-client Python package or equivalent)
- Role with read/write permissions to the TIP's indicator database
Workflow
Step 1: Enumerate and Prioritize Feed Sources
List all available feeds categorized by type (commercial, government, ISAC, OSINT):
- Commercial: Recorded Future, Mandiant Advantage, CrowdStrike Falcon Intelligence
- Government: CISA AIS (Automated Indicator Sharing), FBI InfraGard, MS-ISAC
- OSINT: AlienVault OTX, Abuse.ch, PhishTank, Emerging Threats
Score each feed on: update frequency, historical accuracy rate, coverage of your sector, and attribution depth. Use a weighted scoring matrix with criteria from NIST SP 800-150 (Guide to Cyber Threat Information Sharing).
Step 2: Ingest via TAXII 2.1 or API
For TAXII-enabled feeds:
taxii2-client discover https://feed.example.com/taxii/
taxii2-client get-collection --collection-id <id> --since 2024-01-01
For REST API feeds (e.g., Recorded Future):
- Query
/v2/indicator/searchwithrisk_score_min=65to filter low-confidence IOCs - Apply rate limiting and exponential backoff for API resilience
Step 3: Normalize to STIX 2.1
Convert each IOC to STIX 2.1 objects using the OASIS standard schema:
- IP address →
indicatorobject withpattern: "[ipv4-addr:value = '...']" - Domain →
indicatorwithpattern: "[domain-name:value = '...']" - File hash →
indicatorwithpattern: "[file:hashes.SHA-256 = '...']"
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 · 105 lines · 98 tokens per session scan A 4791fcc4f9c1
analyzing-threat-intelligence-feeds is a skill published in the GitHub repository autohandai/community-skills (11 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 98 tokens to every session and 1,431 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to analyzing-threat-intelligence-feeds, differing in 36 lines, and is treated as a copy.
Other skills, from other repositories
analyzing-threat-intelligence-feeds
Analyzes structured and unstructured threat intelligence feeds to extract actionable indicators, adversary tactics, and campaign context. Use when ingesting commercial or open-source CTI feeds, evaluating feed quality, normalizing data into STIX 2.1 format, or enriching existing IOCs with campaign attribution.…
analyzing-threat-intelligence-feeds
Analyzes structured and unstructured threat intelligence feeds to extract actionable indicators, adversary tactics, and campaign context. Use when ingesting commercial or open-source CTI feeds, evaluating feed quality, normalizing data into STIX 2.1 format, or enriching existing IOCs with campaign attribution.…
analyzing-threat-intelligence-feeds
Analyzes structured and unstructured threat intelligence feeds to extract actionable indicators, adversary tactics, and campaign context. Use when ingesting commercial or open-source CTI feeds, evaluating feed quality, normalizing data into STIX 2.1 format, or enriching existing IOCs with campaign attribution.…
analyzing-threat-intelligence-feeds
Analyzes structured and unstructured threat intelligence feeds to extract actionable indicators, adversary tactics, and campaign context. Use when ingesting commercial or open-source CTI feeds, evaluating feed quality, normalizing data into STIX 2.1 format, or enriching existing IOCs with campaign attribution.…
analyzing-threat-intelligence-feeds
Analyzes structured and unstructured threat intelligence feeds to extract actionable indicators, adversary tactics, and campaign context. Use when ingesting commercial or open-source CTI feeds, evaluating feed quality, normalizing data into STIX 2.1 format, or enriching existing IOCs with campaign attribution.…
Threat Intelligence & CTI
Cyber threat intelligence production — the intelligence cycle, IOC extraction/normalization/enrichment, STIX/TAXII and MISP, structured analytic models (Diamond, Kill Chain, ATT&CK), source scoring, actor/campaign tracking, and finished intelligence reporting.