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Agent skill

Processing Stix Taxii Feeds

by mukul975 in mukul975/Anthropic-Cybersecurity-Skills

Processes STIX 2.1 threat intelligence bundles delivered via TAXII 2.1 servers, normalizing objects into platform-native schemas and routing them to appropriate consuming systems.

Apache-2.0Auto-check passedSecurity

Install Processing Stix Taxii Feeds

skills CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill processing-stix-taxii-feeds -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills processing-stix-taxii-feeds --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/mukul975/Anthropic-Cybersecurity-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/processing-stix-taxii-feeds .claude/skills/processing-stix-taxii-feeds && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
processing-stix-taxii-feeds
GitHub stars
34k
Token cost
~1.7k tokens
SKILL.md length
488 words
Files
4 (incl. scripts, references)
Skills in repo
644
Repo updated
First seen
Licence
Apache-2.0

At a glance

Processes STIX 2.1 threat intelligence bundles delivered via TAXII 2.1 servers, normalizing objects into platform-native schemas and routing them to appropriate consuming systems.

  • Works in 5 steps: Discover TAXII Server Collections → Fetch STIX Bundles with Pagination → Parse and Validate STIX Objects → …
  • Onboarding new TAXII collection endpoints
  • SKILL.md covers When to Use, Prerequisites, Workflow and Key Concepts, plus 2 more sections
  • Runs Python scripts from its folder

What it does

Processing Stix Taxii Feeds is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Processes STIX 2.1 threat intelligence bundles delivered via TAXII 2.1 servers, normalizing objects into platform-native schemas and routing them to appropriate consuming systems. Use when onboarding new TAXII collection endpoints, automating bi-directional intelligence sharing with ISACs, or building pipeline validation for malformed STIX bundles. Activates for requests involving OASIS STIX, TAXII server configuration, MISP TAXII, or Cortex XSOAR feed integrations.

Its SKILL.md is about 1.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including scripts and reference files (for example `references/api-reference.md` and `scripts/agent.py`).

It sits in Security, covering OSINT. The repository describes itself as: 817 structured cybersecurity skills for AI agents · Mapped to 6 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND, NIST AI RMF & MITRE F3 (Fight Fraud) · agentskills.io…. The licence is Apache-2.0.

When your agent uses it

  • Onboarding new TAXII collection endpoints
  • Automating bi-directional intelligence sharing with ISACs
  • Building pipeline validation for malformed STIX bundles

Example prompts

  • “Use the processing-stix-taxii-feeds skill to process STIX 2.1 threat intelligence bundles delivered via TAXII 2.1 servers, normalizing objects into…”
  • “/processing-stix-taxii-feeds”

Requirements

  • Python 3

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. Discover TAXII Server Collections
  2. Fetch STIX Bundles with Pagination
  3. Parse and Validate STIX Objects
  4. Route Objects to Consuming Platforms
  5. Publish Back to TAXII (Bi-directional Sharing)

What it can do on your machine

Read from SKILL.md and the folder at commit 54a7988. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Processing Stix Taxii Feeds loads about 1.7k tokens when it runs, and up to ~2.3k if it reads all its reference files. Until then it costs about 125 tokens; SKILL.md has 488 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~125
When it runs · the whole SKILL.md, loaded when a task matches
~1.7k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from mukul975/Anthropic-Cybersecurity-Skills at commit 54a7988, republished under its Apache-2.0 licence (© mukul975). 488 words, ~1,719 tokens.

Download SKILL.mdSave it as .claude/skills/processing-stix-taxii-feeds/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
processing-stix-taxii-feeds
description
Processes STIX 2.1 threat intelligence bundles delivered via TAXII 2.1 servers, normalizing objects into platform-native schemas and routing them to appropriate consuming systems. Use when onboarding new TAXII collection endpoints, automating bi-directional intelligence sharing with ISACs, or building pipeline validation for malformed STIX bundles. Activates for requests involving OASIS STIX, TAXII server configuration, MISP TAXII, or Cortex XSOAR feed integrations.
domain
cybersecurity
subdomain
threat-intelligence
tags
STIX-2.1, TAXII-2.1, OASIS, MISP, CTI, IOC, threat-intelligence, NIST-SP-800-150
version
1.0.0
author
mahipal
license
Apache-2.0
nist_csf
ID.RA-01, ID.RA-05, DE.CM-01, DE.AE-02
mitre_attack
T1591, T1592, T1593, T1589

Processing STIX/TAXII Feeds

When to Use

Use this skill when:

  • Onboarding a new TAXII 2.1 collection from a government feed (CISA AIS, FS-ISAC) or commercial provider
  • Validating that ingested STIX bundles conform to the OASIS STIX 2.1 specification before import
  • Building automated pipelines that parse STIX relationship objects to reconstruct campaign context

Do not use this skill for proprietary vendor feed formats (Recorded Future JSON, CrowdStrike IOC lists) that require vendor-specific parsers rather than STIX processing.

Prerequisites

  • Python 3.9+ with stix2 library (pip install stix2) and taxii2-client library
  • Network access to TAXII 2.1 server endpoint with valid credentials
  • Target TIP or SIEM with import API (MISP, OpenCTI, or Splunk ES)

Workflow

Step 1: Discover TAXII Server Collections
python
from taxii2client.v21 import Server, as_pages

server = Server("https://cti.example.com/taxii/",
                user="apiuser", password="apikey")
api_root = server.api_roots[0]
for collection in api_root.collections:
    print(collection.id, collection.title, collection.can_read)

Select collections relevant to your threat profile. CISA AIS provides collections segmented by sector (financial, energy, healthcare).

Step 2: Fetch STIX Bundles with Pagination
python
from taxii2client.v21 import Collection
from datetime import datetime, timedelta, timezone

collection = Collection(
    "https://cti.example.com/taxii/api1/collections/<id>/objects/",
    user="apiuser", password="apikey")

# Fetch only objects added in the last 24 hours
added_after = datetime.now(timezone.utc) - timedelta(hours=24)
for bundle_page in as_pages(collection.get_objects,
                             added_after=added_after, per_request=100):
    process_bundle(bundle_page)
Step 3: Parse and Validate STIX Objects
python
import stix2

def process_bundle(bundle_dict):
    bundle = stix2.parse(bundle_dict, allow_custom=True)
    for obj in bundle.objects:
        if obj.type == "indicator":
            validate_indicator(obj)
        elif obj.type == "threat-actor":
            upsert_threat_actor(obj)
        elif obj.type == "relationship":
            link_objects(obj)

def validate_indicator(indicator):
    required = ["id", "type", "spec_version", "created",
                "modified", "pattern", "pattern_type", "valid_from"]
    for field in required:
        if not hasattr(indicator, field):
            raise ValueError(f"Missing required field: {field}")
    # Check confidence range
    if hasattr(indicator, "confidence"):
        assert 0 <= indicator.confidence <= 100
Step 4: Route Objects to Consuming Platforms

Map STIX object types to destination systems:

  • indicator objects → SIEM lookup tables and firewall blocklists
  • malware objects → EDR threat intelligence library
  • threat-actor / campaign objects → TIP for analyst context
  • course-of-action objects → Security team wiki or SOAR playbook triggers

Use TLP marking definitions to enforce sharing restrictions:

python
for marking in obj.get("object_marking_refs", []):
    if "tlp-red" in marking:
        route_to_restricted_platform_only(obj)
Step 5: Publish Back to TAXII (Bi-directional Sharing)
python
# Add validated local intelligence back to shared collection
new_indicator = stix2.Indicator(
    name="Malicious C2 Domain",
    pattern="[domain-name:value = 'evil-c2.example.com']",
    pattern_type="stix",
    valid_from="2025-01-15T00:00:00Z",
    confidence=80,
    labels=["malicious-activity"],
    object_marking_refs=["marking-definition--34098fce-860f-479c-ae..."]  # TLP:GREEN
)
collection.add_objects(stix2.Bundle(new_indicator))

Key Concepts

TermDefinition
STIX BundleTop-level STIX container object (type: "bundle") holding any number of STIX Domain Objects (SDOs) and STIX Relationship Objects (SROs)
SDOSTIX Domain Object — core intelligence types: indicator, threat-actor, malware, campaign, attack-pattern, course-of-action
SROSTIX Relationship Object — links two SDOs with a labeled relationship (e.g., "uses", "attributed-to", "indicates")
Pattern LanguageSTIX pattern syntax for indicator conditions: [network-traffic:dst_port = 443 AND ipv4-addr:value = '10.0.0.1']
Marking DefinitionSTIX object encoding TLP or statement restrictions on intelligence sharing
added_afterTAXII 2.1 filter parameter (RFC 3339 timestamp) for incremental polling of new objects
Show full SKILL.md (188 more words)Show less

Tools & Systems

  • stix2 (Python): Official OASIS Python library for creating, parsing, and validating STIX 2.0/2.1 objects
  • taxii2-client (Python): Client library for TAXII 2.0/2.1 server discovery, collection enumeration, and object retrieval
  • MISP: Open-source TIP with native TAXII 2.1 server and client; MISP-TAXII-Server plugin for publishing MISP events
  • OpenCTI: CTI platform with built-in TAXII 2.1 connector; supports STIX 2.1 import/export natively
  • Cabby: Legacy Python TAXII 1.x client for older government feeds still on TAXII 1.1

Common Pitfalls

  • Ignoring spec_version field: STIX 2.0 and 2.1 have incompatible schemas (2.1 adds confidence, object_marking_refs at bundle level). Always check spec_version before parsing.
  • No pagination handling: TAXII servers cap responses at 100–1000 objects per request. Missing pagination (via next link header) causes silent data loss.
  • Clock skew on added_after: Server and client time misalignment causes missed objects at interval boundaries. Use UTC exclusively and add 5-minute overlap windows.
  • Storing raw STIX blobs without indexing: Storing bundles as opaque JSON prevents querying by indicator type or campaign. Parse into relational or graph database.
  • Sharing TLP:RED content inadvertently: Automated pipelines must filter marking definitions before routing to any shared platform or SIEM with broad analyst access.

© mukul975, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (scripts, references) in skills/processing-stix-taxii-feeds of mukul975/Anthropic-Cybersecurity-Skills.

  • SKILL.md
  • LICENSE
  • references/api-reference.md
  • scripts/agent.py

Open the folder on GitHubat commit 54a7988

Compare with similar skills

Processing Stix Taxii Feeds next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Processing Stix Taxii Feeds compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Processing Stix Taxii Feeds this skillmukul975/Anthropic-Cybersecurity-Skills34k—~1.7kAutomated safety check: PassApache-2.0
Metabigor OSINT Reconj3ssie/metabigor1.9k—~2.4kAutomated safety check: PassMIT
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ShadowBroker Intelligence ClientBigBodyCobain/Shadowbroker11k—~8.9kAutomated safety check: WarnAGPL-3.0
Awesome Osint Operatorshoyann/RZK-The-Hunter141—~4.8kAutomated safety check: PassCC-BY-SA-4.0
Run Claude Osintelementalsouls/Claude-OSINT2.8k—~1.2kAutomated safety check: PassMIT

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Categories

Questions about Processing Stix Taxii Feeds

What does Processing Stix Taxii Feeds do?

Processes STIX 2.1 threat intelligence bundles delivered via TAXII 2.1 servers, normalizing objects into platform-native schemas and routing them to appropriate consuming systems. Processing Stix Taxii Feeds is an agent skill from mukul975/Anthropic-Cybersecurity-Skills.1 servers, normalizing objects into platform-native schemas and routing them to appropriate consuming systems.

When should I use Processing Stix Taxii Feeds?

Processing Stix Taxii Feeds fits situations like: onboarding new TAXII collection endpoints; automating bi-directional intelligence sharing with ISACs; building pipeline validation for malformed STIX bundles.

How do I install Processing Stix Taxii Feeds in Claude Code?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill processing-stix-taxii-feeds -a claude-code`. Or copy the skill folder (skills/processing-stix-taxii-feeds in mukul975/Anthropic-Cybersecurity-Skills) into .claude/skills/processing-stix-taxii-feeds in your project. Claude Code loads it when a task matches its description.

How do I install Processing Stix Taxii Feeds in Codex?

Run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill processing-stix-taxii-feeds -a codex`. Or copy the skill folder (skills/processing-stix-taxii-feeds in mukul975/Anthropic-Cybersecurity-Skills) into .agents/skills/processing-stix-taxii-feeds in your project. Codex loads it when a task matches its description.

Can I use Processing Stix Taxii Feeds in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill processing-stix-taxii-feeds -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/processing-stix-taxii-feeds, .gemini/skills/processing-stix-taxii-feeds, .github/skills/processing-stix-taxii-feeds and .opencode/skills/processing-stix-taxii-feeds in your project.

What does Processing Stix Taxii Feeds need to run?

Going by SKILL.md and its folder, Processing Stix Taxii Feeds needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Processing Stix Taxii Feeds access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Processing Stix Taxii Feeds safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Processing Stix Taxii Feeds use?

Processing Stix Taxii Feeds is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Processing Stix Taxii Feeds use?

About 1.7k tokens (SKILL.md is roughly 6.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 571 tokens, read only when the agent opens those files.

What are the alternatives to Processing Stix Taxii Feeds?

Skills that share tags, products or a category with Processing Stix Taxii Feeds: Metabigor OSINT Recon (j3ssie/metabigor, 1.9k stars), Ctf Osint (ljagiello/ctf-skills, 3.4k stars), ShadowBroker Intelligence Client (BigBodyCobain/Shadowbroker, 11k stars) and Awesome Osint Operator (shoyann/RZK-The-Hunter, 141 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Processing Stix Taxii Feeds?

mukul975 (a GitHub user) maintains it in mukul975/Anthropic-Cybersecurity-Skills, which has 34,116 GitHub stars. The repository holds 644 skills in this directory. The repository was last updated on August 31, 2026.

Source: mukul975/Anthropic-Cybersecurity-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.