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 Liberty91LTD/cti-skills --skill intelligence-writinggit clone --depth 1 https://github.com/Liberty91LTD/cti-skillsWrote 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/liberty91ltd/cti-skills/intelligence-writing)<a href="https://agentmods.dev/skills/liberty91ltd/cti-skills/intelligence-writing"><img src="https://agentmods.dev/badge/skills/liberty91ltd/cti-skills/intelligence-writing/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/liberty91ltd/cti-skills/intelligence-writing"><img src="https://agentmods.dev/badge/skills/liberty91ltd/cti-skills/intelligence-writing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00051 | $0.03061 |
| Opus 5.5 | $0.00020 | $0.01224 |
| Sonnet 5.5 | $0.00010 | $0.00612 |
| Haiku 4.5 | $0.00005 | $0.00306 |
Grade A, and why
intelligence-writing 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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 367 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Intelligence Writing Guide
Intelligence writing is not creative writing. It is precise, structured, and audience-driven. Every word must earn its place.
Core Principles
1. Bottom Line Up Front (BLUF)
The most important finding goes in the first paragraph. If the reader stops after two sentences, they should have the key takeaway.
Bad: "Over the past several months, we have observed various indicators that suggest a potential shift in threat actor targeting patterns across multiple sectors..."
Good: "APT41 is actively targeting European pharmaceutical companies using a new loader variant. We assess with high confidence that at least three organisations in this sector have been compromised since January 2026."
2. Active Voice
Use active voice. Name the actor.
- ❌ "The malware was deployed by the threat actor"
- ✅ "The threat actor deployed the malware"
- ❌ "It was assessed that..."
- ✅ "We assess that..."
3. Distinguish Facts, Assessments, and Assumptions
| Type | Signal words | Example |
|---|---|---|
| Fact | "observed", "confirmed", "identified" | "We identified three C2 domains registered in January 2026." |
| Assessment | "we assess", "indicates", "suggests" | "We assess with high confidence that these domains are linked to APT41." |
| Assumption | "assuming", "if", "given that" | "Assuming the registration pattern continues, we expect additional infrastructure in Q2." |
4. Use Mandated Language
- Confidence levels: per the confidence-levels skill
- Likelihood language: per the likelihood-language skill
- Source assessment: per the source-assessment skill (the Admiralty scale, for lookup results and single items) and the quality-of-information-check skill (the evidence grade, access level and claim support, for claims from documents and articles). Neither is converted into the other
- TLP: per the tlp-guide skill
5. Be Specific
- ❌ "Recently, a sophisticated threat actor..."
- ✅ "In March 2026, APT41 (also tracked as Winnti, Barium)..."
- ❌ "Multiple indicators were found"
- ✅ "We identified 14 IP addresses and 3 domain names associated with this campaign"
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.
- 1mo ago Changed · +56 lines ec3153cf19b4
- 2mo ago First seen · 311 lines · 51 tokens per session scan A f39ad1010a5a
intelligence-writing is a skill published in the GitHub repository Liberty91LTD/cti-skills (36 stars, last pushed 4d ago), licensed MIT. It adds 51 tokens to every session and 3,061 once invoked, about $0.0002 per session on Opus 5.5. 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.
Other skills, from other repositories
analyzing-campaign-attribution-evidence
Systematically evaluate cyber-campaign evidence to attribute an operation to a threat actor, using the Diamond Model and Analysis of Competing Hypotheses (ACH) to weigh infrastructure overlaps, TTP consistency, malware code similarity, and timing/language artifacts into confidence-weighted attribution assessments. Use…
analyzing-threat-actor-ttps-with-mitre-attack
MITRE ATT&CK is a globally-accessible knowledge base of adversary tactics, techniques, and procedures (TTPs) based on real-world observations. This skill covers systematically mapping threat actor beh.
analyzing-threat-landscape-with-misp
Analyze the threat landscape using MISP (Malware Information Sharing Platform) by querying event statistics, attribute distributions, threat actor galaxy clusters, and tag trends over time. Uses PyMISP to pull event data, compute IOC type breakdowns, identify top threat actors and malware families, and generate threat…
analyzing-campaign-attribution-evidence
Use when campaign attribution analysis involves systematically evaluating evidence to determine which threat actor or group is responsible for a cyber operation. This skill covers collecting and weighting attr.
prompt-injection-auditor
Security audit of LLM system prompts, agent instruction files (SKILL.md, AGENTS.md, CLAUDE.md), and agent configurations against prompt injection attacks. Use when the user wants to (1) audit or harden a system prompt or agent instructions against prompt injection, (2) review an agent skill or system prompt for…
analyzing-campaign-attribution-evidence
Campaign attribution analysis involves systematically evaluating evidence to determine which threat actor or group is responsible for a cyber operation. This skill covers collecting and weighting attr.