producing-threat-intelligence

producing-threat-intelligence is a skill for Claude Code from trilwu/secskills. It costs 159 tokens per session (3,951 once invoked), scanned A, original, MIT.

A guide to producing cyber threat intelligence: analysis about attackers, campaigns, and technical clues such as domains, IP addresses, hashes, certificates, or registrants. It connects evidence to a decision and records how confident the analysis is.

In plain words
What is it for?
Investigating related infrastructure, tracking actors and campaigns, enriching indicators, assessing outside reports, building threat models, and preparing intelligence products.
Why use it?
A list of suspicious indicators without context does not explain what happened or what to do. This helps turn raw clues into defensible intelligence for a specific audience.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the secskills-defense plugin — 22 skills shipped together

Good fit Investigating related infrastructure, tracking actors and campaigns, enriching indicators, assessing outside reports, building threat models, and preparing intelligence products.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/trilwu/secskills/producing-threat-intelligence
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 trilwu/secskills --skill producing-threat-intelligence
Clone the repo
git clone --depth 1 https://github.com/trilwu/secskills

Made for: Claude Code.

Or install secskills-defense, the plugin that ships this one along with the rest of its 22 skills.

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 producing-threat-intelligence

README.md
[![agentmods](https://agentmods.dev/badge/skills/trilwu/secskills/producing-threat-intelligence/github.svg)](https://agentmods.dev/skills/trilwu/secskills/producing-threat-intelligence)
Your own site
<a href="https://agentmods.dev/skills/trilwu/secskills/producing-threat-intelligence"><img src="https://agentmods.dev/badge/skills/trilwu/secskills/producing-threat-intelligence/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 producing-threat-intelligence

Your own site · 80×15
<a href="https://agentmods.dev/skills/trilwu/secskills/producing-threat-intelligence"><img src="https://agentmods.dev/badge/skills/trilwu/secskills/producing-threat-intelligence.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 159 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,951 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. 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 $0.00159 $0.03951
Opus 5 $0.00079 $0.01975
Sonnet 5 $0.00032 $0.00790
Haiku 4.5 $0.00016 $0.00395

Measured 10d ago against content hash d9527990f8d3, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

producing-threat-intelligence 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 10d 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.

curl -s "https://crt.sh/?q=%25.example.com&output=json" | jq -r '.[].name_value' | sort -u
secskills-defense/skills/producing-threat-intelligence/SKILL.md · 334 lines

How it starts

The opening of the file, as written. The whole thing — 334 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Producing Threat Intelligence

Intelligence is not a pile of indicators — it is analysis that reduces a decision-maker's uncertainty. An IOC with no context, no confidence, and no recommended action is data, not intelligence. Attribution is a claim you must be able to defend from evidence, not a guess dressed in a threat-actor name. The test of a finished product is simple: did someone decide something differently because of it?

When to Use

  • Pivoting from a domain, IP, hash, TLS certificate, or registrant to related infrastructure
  • Tracking a threat actor or campaign over time
  • Enriching and contextualizing raw indicators into usable intelligence
  • Producing a finished intelligence product for a defined consumer
  • Assessing whether an external vendor or government report is relevant to your organization
  • Building and curating a threat model of the adversaries that actually matter to you

When NOT to Use

  • Searching your OWN telemetry for the activity — use hunting-threats
  • Reversing a specific sample — use analyzing-malware
  • Resolving an ATT&CK technique ID to a skill — use mapping-attack-techniques
  • An active, confirmed incident — use responding-to-incidents
  • Turning intel into deployed detection rules — use engineering-detections

The Intelligence Lifecycle

Every product moves through the same loop. Naming the stages is not bureaucracy — it is where you catch the two failures that make CTI worthless.

1. Direction     — whose decision, which question (a PIR)
2. Collection    — gather against the requirement, not everything reachable
3. Processing     — normalize, deduplicate, translate, enrich
4. Analysis      — assess, weigh hypotheses, assign confidence
5. Dissemination — deliver in a form the consumer can act on
6. Feedback      — did it help; refine the next requirement

The two failures that account for most wasted CTI effort are at the ends of the loop, not the middle:

  • Skipping direction produces intelligence nobody asked for. Without a Priority Intelligence Requirement (PIR) naming the consumer and the decision, you collect what is easy and report what is interesting, and it lands on no one's desk. Start from the question, not the feed.
  • Skipping dissemination produces analysis that never reaches a decision. A brilliant assessment sitting in a wiki nobody reads changed nothing. The product is not done when it is written; it is done when it is in front of the person who acts on it, in the form and at the time they need it.

Read the full file on GitHub · 334 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. 10d ago First seen · 334 lines · 159 tokens per session scan A d9527990f8d3

Subscribe to this mod's changes

producing-threat-intelligence is a skill published in the GitHub repository trilwu/secskills (137 stars, last pushed 5d ago), licensed MIT. It adds 159 tokens to every session and 3,951 once invoked, about $0.0008 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 2026-08-30.

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