hunt-ioc

hunt-ioc is a skill for Claude Code, Codex from dandye/ai-runbooks. It costs 53 tokens per session (1,024 once invoked), scanned A, original, Apache-2.0.

A threat-hunting workflow for checking a list of Indicators of Compromise (IOCs)—such as suspicious IP addresses, domains, file hashes, or web addresses—across security logs.

In plain words
What is it for?
Use it to investigate threat-intelligence alerts, recent incidents, or emerging threats by finding where specific indicators appear in your environment.
Why use it?
It replaces manual, inconsistent searches with a repeatable check that validates indicators, searches the SIEM (security information and event management system), and records the reason and results.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to investigate threat-intelligence alerts, recent incidents, or emerging threats by finding where specific indicators appear in your environment.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/dandye/ai-runbooks/hunt-ioc
Install

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.

Any agent
npx skills add dandye/ai-runbooks --skill hunt-ioc
Clone the repo
git clone --depth 1 https://github.com/dandye/ai-runbooks

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for hunt-ioc

README.md
[![agentmods](https://agentmods.dev/badge/skills/dandye/ai-runbooks/hunt-ioc/github.svg)](https://agentmods.dev/skills/dandye/ai-runbooks/hunt-ioc)
Your own site
<a href="https://agentmods.dev/skills/dandye/ai-runbooks/hunt-ioc"><img src="https://agentmods.dev/badge/skills/dandye/ai-runbooks/hunt-ioc/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.

agentmods 80×15 button for hunt-ioc

Your own site · 80×15
<a href="https://agentmods.dev/skills/dandye/ai-runbooks/hunt-ioc"><img src="https://agentmods.dev/badge/skills/dandye/ai-runbooks/hunt-ioc.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 53 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,024 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00053 $0.01024
Opus 5 $0.00026 $0.00512
Sonnet 5 $0.00011 $0.00205
Haiku 4.5 $0.00005 $0.00102

Measured 12d ago against content hash 0f7917f90a67, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

hunt-ioc 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 12d 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.

skills/hunt-ioc/SKILL.md · 151 lines

How it starts

The opening of the file, as written. The whole thing — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.

IOC Threat Hunt Skill

Proactively hunt for specific Indicators of Compromise (IOCs) across the environment based on threat intelligence feeds, recent incidents, or emerging threats.

Inputs

  • IOC_LIST - Comma-separated list of IOC values to hunt
  • IOC_TYPES - Corresponding types (e.g., "IP Address, Domain, File Hash")
  • HUNT_TIMEFRAME_HOURS - Lookback period (default: 96)
  • (Optional) HUNT_CASE_ID - SOAR case for tracking
  • (Optional) REASON_FOR_HUNT - Why these IOCs are being hunted

Workflow

Step 1: Parse and Validate IOCs

Parse IOC_LIST and IOC_TYPES into structured list. Validate IOC formats (IP regex, hash length, etc.).

Step 2: Initial IOC Match Check

secops-mcp.get_ioc_matches(hours_back=HUNT_TIMEFRAME_HOURS)

Check if any IOCs appear in integrated threat feeds.

Step 3: Iterative SIEM Search

For each IOC, construct appropriate UDM query:

IP Address:

(principal.ip = "IOC" OR target.ip = "IOC" OR network.ip = "IOC")

Domain:

(principal.hostname = "IOC" OR target.hostname = "IOC" OR network.dns.questions.name = "IOC")

File Hash:

(target.file.sha256 = "IOC" OR target.file.md5 = "IOC" OR target.file.sha1 = "IOC")

URL:

target.url = "IOC"

Execute each search:

secops-mcp.search_security_events(text=query, hours_back=HUNT_TIMEFRAME_HOURS)

Step 4: Analyze Results

For each search result:

  • Identify affected hosts, users, processes
  • Note event types (login, network connection, file execution)
  • Assess if activity is suspicious or expected

Step 5: Enrich Hits

If hits found for an IOC:

Use /enrich-ioc for the IOC itself.

For involved entities (hosts, users):

secops-mcp.lookup_entity(entity_value=ENTITY)

Step 6: Document Hunt

Use /document-in-case (if HUNT_CASE_ID provided):

IOC Hunt Summary:
- IOCs Hunted: [list]
- Timeframe: [hours]
- Queries Used: [list with results summary]
- IOCs with Hits: [list with details]
- IOCs with No Hits: [list - confirms environment is clean]
- Enrichment: [for hits]
- Recommendations: [next steps]

Read the full file on GitHub · 151 lines

Changes

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.

  1. 12d ago First seen · 151 lines · 53 tokens per session scan A 0f7917f90a67

Subscribe to this mod's changes

hunt-ioc is a skill published in the GitHub repository dandye/ai-runbooks (124 stars, last pushed 28d ago), licensed Apache-2.0. It adds 53 tokens to every session and 1,024 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

insight-error-page

Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…

vercel/next.js · 83 tokens

next-cache-components-optimizer

Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…

vercel/next.js · 170 tokens

next-partial-prefetching-adoption

Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…

vercel/next.js · 103 tokens