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Agent skill

Analyzing Threat Intelligence Feeds

by mukul975 in mukul975/Anthropic-Cybersecurity-Skills

Analyzes structured and unstructured threat intelligence feeds to extract actionable indicators, adversary tactics, and campaign context.

Apache-2.0Auto-check passedSecurity

Install Analyzing Threat Intelligence Feeds

skills CLI
$ npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill analyzing-threat-intelligence-feeds -a claude-code

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

GitHub CLI
$ gh skill install mukul975/Anthropic-Cybersecurity-Skills analyzing-threat-intelligence-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/analyzing-threat-intelligence-feeds .claude/skills/analyzing-threat-intelligence-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
analyzing-threat-intelligence-feeds
GitHub stars
34k
Token cost
~1.6k tokens
SKILL.md length
656 words
Files
4 (incl. scripts, references)
Skills in repo
644
Repo updated
First seen
Licence
Apache-2.0

At a glance

Analyzes structured and unstructured threat intelligence feeds to extract actionable indicators, adversary tactics, and campaign context.

  • Works in 5 steps: Enumerate and Prioritize Feed Sources → Ingest via TAXII 2.1 or API → Normalize to STIX 2.1 → …
  • Ingesting commercial
  • SKILL.md covers When to Use, Prerequisites, Workflow and Key Concepts, plus 2 more sections
  • Runs Python scripts from its folder

What it does

Analyzing Threat Intelligence Feeds is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. 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. Activates for requests involving ThreatConnect, Recorded Future, Mandiant Advantage, MISP, AlienVault OTX, or automated feed aggregation pipelines.

Its SKILL.md is about 1.6k 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

  • Ingesting commercial
  • Open-source CTI feeds
  • Evaluating feed quality
  • Normalizing data into STIX 2.1 format

Example prompts

  • “Use the analyzing-threat-intelligence-feeds skill to analyz structured and unstructured threat intelligence feeds to extract actionable indicators…”
  • “/analyzing-threat-intelligence-feeds”

Requirements

  • Python 3

Workflow steps

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

  1. Enumerate and Prioritize Feed Sources
  2. Ingest via TAXII 2.1 or API
  3. Normalize to STIX 2.1
  4. Deduplicate and Enrich
  5. Distribute to Consuming Systems

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

Analyzing Threat Intelligence Feeds loads about 1.6k tokens when it runs, and up to ~2.1k if it reads all its reference files. Until then it costs about 124 tokens; SKILL.md has 656 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~124
When it runs · the whole SKILL.md, loaded when a task matches
~1.6k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.1k

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). 656 words, ~1,564 tokens.

Download SKILL.mdSave it as .claude/skills/analyzing-threat-intelligence-feeds/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
analyzing-threat-intelligence-feeds
description
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. Activates for requests involving ThreatConnect, Recorded Future, Mandiant Advantage, MISP, AlienVault OTX, or automated feed aggregation pipelines.
domain
cybersecurity
subdomain
threat-intelligence
tags
STIX, TAXII, MITRE-ATT&CK, IOC, ThreatConnect, Recorded-Future, MISP, CTI, NIST-CSF
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
T1071.001, T1566, T1568, T1583.001, T1102

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/search with risk_score_min=65 to 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 → indicator object with pattern: "[ipv4-addr:value = '...']"
  • Domain → indicator with pattern: "[domain-name:value = '...']"
  • File hash → indicator with pattern: "[file:hashes.SHA-256 = '...']"

Attach relationship objects linking indicators to threat-actor or malware objects. Use confidence field (0–100) based on source fidelity rating.

Step 4: Deduplicate and Enrich

Run deduplication against existing TIP database using normalized value + type as composite key. Enrich surviving IOCs:

  • VirusTotal: detection ratio, sandbox behavior reports
  • PassiveTotal (RiskIQ): WHOIS history, passive DNS, SSL certificate chains
  • Shodan: banner data, open ports, geographic location
Step 5: Distribute to Consuming Systems

Export enriched indicators via TAXII 2.1 push to SIEM (Splunk, Microsoft Sentinel), firewalls (Palo Alto XSOAR playbooks), and EDR platforms. Set TTL (time-to-live) per indicator type: IP addresses 30 days, domains 90 days, file hashes 1 year.

Show full SKILL.md (280 more words)Show less

Key Concepts

TermDefinition
STIX 2.1Structured Threat Information Expression — OASIS standard JSON schema for CTI objects including indicators, threat actors, campaigns, and relationships
TAXII 2.1Trusted Automated eXchange of Intelligence Information — HTTPS-based protocol for sharing STIX content between servers and clients
IOCIndicator of Compromise — observable artifact (IP, domain, hash, URL) that indicates a system may have been breached
TLPTraffic Light Protocol — color-coded classification (RED/AMBER/GREEN/WHITE) defining sharing restrictions for CTI
Confidence ScoreNumeric value (0–100 in STIX) reflecting the producer's certainty about an indicator's malicious attribution
Feed FidelityHistorical accuracy rate of a feed measured by true positive rate in production detections

Tools & Systems

  • ThreatConnect TC Exchange: Aggregates 100+ commercial and OSINT feeds; provides automated playbooks for IOC enrichment
  • MISP (Malware Information Sharing Platform): Open-source TIP supporting STIX/TAXII; widely used by ISACs and government CERTs
  • OpenCTI: Open-source platform with native MITRE ATT&CK integration and graph-based relationship visualization
  • Recorded Future: Commercial feed with AI-powered risk scoring and real-time dark web monitoring
  • taxii2-client: Python library for TAXII 2.0/2.1 client operations (pip install taxii2-client)
  • PyMISP: Python API for MISP feed management and IOC submission

Common Pitfalls

  • IOC age staleness: IP addresses and domains rotate frequently; applying 1-year-old IOCs generates false positives. Enforce TTL policies.
  • Missing context: Blocking an IOC without understanding the associated campaign or adversary can disrupt legitimate business traffic (e.g., CDN IPs shared with malicious actors).
  • Feed overlap without deduplication: Ingesting the same IOC from five feeds without deduplication inflates indicator counts and SIEM rule complexity.
  • TLP violation: Redistributing RED-classified intelligence outside authorized boundaries violates sharing agreements and trust relationships.
  • Over-blocking on low-confidence indicators: Indicators with confidence below 50 should trigger detection-only rules, not blocking, to avoid operational disruption.

© 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/analyzing-threat-intelligence-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

Analyzing Threat Intelligence 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.

Analyzing Threat Intelligence Feeds compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Analyzing Threat Intelligence Feeds this skillmukul975/Anthropic-Cybersecurity-Skills34k—~1.6kAutomated safety check: PassApache-2.0
Metabigor OSINT Reconj3ssie/metabigor1.8k—~2.4kAutomated safety check: PassMIT
Ctf Osintljagiello/ctf-skills3.4k1 repos~2.3kAutomated safety check: NotesMIT
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 Analyzing Threat Intelligence Feeds

What does Analyzing Threat Intelligence Feeds do?

Analyzes structured and unstructured threat intelligence feeds to extract actionable indicators, adversary tactics, and campaign context. Analyzing Threat Intelligence Feeds is an agent skill from mukul975/Anthropic-Cybersecurity-Skills. Analyzes structured and unstructured threat intelligence feeds to extract actionable indicators, adversary tactics, and campaign context.

When should I use Analyzing Threat Intelligence Feeds?

Analyzing Threat Intelligence Feeds fits situations like: ingesting commercial; open-source CTI feeds; evaluating feed quality; normalizing data into STIX 2.1 format.

How do I install Analyzing Threat Intelligence Feeds in Claude Code?

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

How do I install Analyzing Threat Intelligence Feeds in Codex?

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

Can I use Analyzing Threat Intelligence 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 analyzing-threat-intelligence-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/analyzing-threat-intelligence-feeds, .gemini/skills/analyzing-threat-intelligence-feeds, .github/skills/analyzing-threat-intelligence-feeds and .opencode/skills/analyzing-threat-intelligence-feeds in your project.

What does Analyzing Threat Intelligence Feeds need to run?

Going by SKILL.md and its folder, Analyzing Threat Intelligence Feeds needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Analyzing Threat Intelligence 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 Analyzing Threat Intelligence 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 Analyzing Threat Intelligence Feeds use?

Analyzing Threat Intelligence 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 Analyzing Threat Intelligence Feeds use?

About 1.6k tokens (SKILL.md is roughly 6.3k 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 582 tokens, read only when the agent opens those files.

What are the alternatives to Analyzing Threat Intelligence Feeds?

Skills that share tags, products or a category with Analyzing Threat Intelligence Feeds: Metabigor OSINT Recon (j3ssie/metabigor, 1.8k 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 Analyzing Threat Intelligence Feeds?

mukul975 (a GitHub user) maintains it in mukul975/Anthropic-Cybersecurity-Skills, which has 33,993 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.