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 building-ioc-defanging-and-sharing-pipelinegit 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/building-ioc-defanging-and-sharing-pipeline)<a href="https://agentmods.dev/skills/autohandai/community-skills/building-ioc-defanging-and-sharing-pipeline"><img src="https://agentmods.dev/badge/skills/autohandai/community-skills/building-ioc-defanging-and-sharing-pipeline/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/building-ioc-defanging-and-sharing-pipeline"><img src="https://agentmods.dev/badge/skills/autohandai/community-skills/building-ioc-defanging-and-sharing-pipeline.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 | $0.00049 | $0.03442 |
| Opus 5 | $0.00024 | $0.01721 |
| Sonnet 5 | $0.00010 | $0.00688 |
| Haiku 4.5 | $0.00005 | $0.00344 |
Grade A, and why
building-ioc-defanging-and-sharing-pipeline scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
from urllib.parse import urlparse, unquote How it starts
The opening of the file, as written. The whole thing — 351 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Building IOC Defanging and Sharing Pipeline
Overview
IOC defanging modifies potentially malicious indicators (URLs, IP addresses, domains, email addresses) to prevent accidental clicks or execution while preserving readability for analysis and sharing. This skill covers building an automated pipeline that ingests raw IOCs from multiple sources, normalizes and deduplicates them, applies defanging for safe human consumption, converts them to STIX 2.1 format for machine consumption, and distributes through TAXII servers, MISP instances, and email reports.
Prerequisites
- Python 3.9+ with
defang,ioc-fanger,stix2,requests,validatorslibraries - MISP instance or TAXII server for automated sharing
- Understanding of IOC types: IPv4/IPv6, domains, URLs, email addresses, file hashes
- Familiarity with STIX 2.1 Indicator patterns and TLP marking definitions
- Access to threat intelligence feeds for IOC ingestion
Key Concepts
IOC Defanging Standards
Defanging replaces active protocol and domain components to prevent execution: http:// becomes hxxp://, https:// becomes hxxps://, dots in domains/IPs become [.], @ in emails becomes [@]. This is critical for sharing IOCs in reports, emails, Slack channels, and paste sites where auto-linking could trigger network connections to malicious infrastructure.
IOC Normalization
Raw IOCs from different sources come in inconsistent formats. Normalization involves converting to lowercase, removing trailing slashes and whitespace, extracting domains from URLs, resolving URL encoding, validating format correctness, and deduplicating across sources.
STIX 2.1 Indicator Patterns
STIX patterns express IOCs in a standardized format: [ipv4-addr:value = '203.0.113.1'], [domain-name:value = 'malicious.example.com'], [url:value = 'http://evil.com/payload'], [file:hashes.'SHA-256' = 'abc123...']. Each indicator includes valid_from, indicator_types, confidence, and optional TLP markings.
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 · 351 lines · 49 tokens per session scan A 09a69fbb1f69
building-ioc-defanging-and-sharing-pipeline is a skill published in the GitHub repository autohandai/community-skills (11 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 49 tokens to every session and 3,442 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
building-ioc-defanging-and-sharing-pipeline
Build an automated pipeline to defang indicators of compromise (URLs, IPs, domains, emails) for safe sharing and distribute them in STIX format through TAXII feeds and threat intelligence platforms.
building-ioc-defanging-and-sharing-pipeline
Build an automated pipeline that ingests raw IOCs (URLs, IPs, domains, emails), normalizes and deduplicates them, then produces defanged renderings for safe human reading alongside canonical STIX 2.1 bundles distributed via TAXII servers, MISP, or email reports. Use when preparing indicators of compromise for safe…
building-ioc-defanging-and-sharing-pipeline
Build an automated pipeline to defang indicators of compromise (URLs, IPs, domains, emails) for safe sharing and distribute them in STIX format through TAXII feeds and threat intelligence platforms.
collecting-threat-intelligence-with-misp
MISP (Malware Information Sharing Platform) is an open-source threat intelligence platform for gathering, sharing, storing, and correlating Indicators of Compromise (IOCs) of targeted attacks, threat.
collecting-threat-intelligence-with-misp
Deploy MISP, configure threat feeds (MISP community, freetext, TAXII, CSV), and use the PyMISP API to programmatically fetch, add, and search events and IOCs, building automated collection pipelines that aggregate indicators from community and commercial sources. Use when gathering, storing, or correlating IOCs and…
analyzing-indicators-of-compromise
Analyzes indicators of compromise (IOCs) including IP addresses, domains, file hashes, URLs, and email artifacts to determine maliciousness confidence, campaign attribution, and blocking priority. Use when triaging IOCs from phishing emails, security alerts, or external threat feeds; enriching raw IOCs with…