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 trilwu/secskills --skill producing-threat-intelligencegit clone --depth 1 https://github.com/trilwu/secskillsWrote 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/trilwu/secskills/producing-threat-intelligence)<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.
<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>- NVIDIA SkillSpector pass
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 | $0.00159 | $0.03951 |
| Opus 5 | $0.00079 | $0.01975 |
| Sonnet 5 | $0.00032 | $0.00790 |
| Haiku 4.5 | $0.00016 | $0.00395 |
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 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.
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.
- 10d ago First seen · 334 lines · 159 tokens per session scan A d9527990f8d3
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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