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 maturity-assessmentgit 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/maturity-assessment)<a href="https://agentmods.dev/skills/liberty91ltd/cti-skills/maturity-assessment"><img src="https://agentmods.dev/badge/skills/liberty91ltd/cti-skills/maturity-assessment/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/maturity-assessment"><img src="https://agentmods.dev/badge/skills/liberty91ltd/cti-skills/maturity-assessment.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 | $0.00048 | $0.01312 |
| Opus 5.5 | $0.00019 | $0.00525 |
| Sonnet 5.5 | $0.00010 | $0.00262 |
| Haiku 4.5 | $0.00005 | $0.00131 |
Grade A, and why
maturity-assessment 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 2mo 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 — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CTI Programme Maturity Assessment
Maturity Levels
| Level | Name | Description |
|---|---|---|
| 1 | Ad-hoc | No formal CTI function. Reactive, incident-driven. Intelligence is incidental. |
| 2 | Emerging | Basic CTI capability. Some collection, limited analysis. Primarily tactical (IOC feeds). |
| 3 | Defined | Formal CTI function with documented processes. Strategic, operational, and tactical output. PIRs defined. |
| 4 | Managed | Mature CTI programme. Stakeholder-driven, measured effectiveness, feedback loops. Proactive intelligence. |
| 5 | Optimized | Intelligence-driven organisation. CTI informs all security decisions. Continuous improvement, advanced analytics, community contribution. |
Assessment Dimensions
1. Collection
| Level | Indicators |
|---|---|
| 1 | No systematic collection. Ad-hoc Google searches when incidents occur. |
| 2 | 1-2 threat feeds (usually free). No collection plan. |
| 3 | Multiple sources across OSINT, commercial feeds, ISACs. Collection plan exists. Source assessment applied. |
| 4 | Comprehensive collection mapped to PIRs. Source quality tracked. Dark web monitoring. Automated collection pipelines. |
| 5 | Full-spectrum collection. HUMINT relationships (ISACs, vendors, government). Predictive collection based on emerging threat indicators. |
2. Analysis
| Level | Indicators |
|---|---|
| 1 | No analysis. Raw data forwarded to SOC. |
| 2 | Basic enrichment (VirusTotal lookups). IOC correlation. No structured techniques. |
| 3 | Structured analysis applied. Threat assessments produced. ATT&CK mapping. Confidence levels used. |
| 4 | Full SAT toolkit (ACH, red team, horizon scanning). Knowledge cells maintained. Alternative hypotheses routinely considered. |
| 5 | Advanced analytics. Machine learning augments human analysis. Predictive threat modelling. Community-leading analysis. |
3. Production
| Level | Indicators |
|---|---|
| 1 | No intelligence products. Maybe an IOC spreadsheet. |
| 2 | Ad-hoc alerts and IOC lists. No standard templates. Inconsistent quality. |
| 3 | Standardised products (flash reports, assessments). TLP applied. Likelihood language used. Quality review process. |
| 4 | Full product suite tailored to stakeholder needs. Detection rules (SIGMA/YARA/KQL). STIX bundles for sharing. |
| 5 | Dynamic intelligence products. Real-time dashboards. Automated tactical output. Strategic foresight publications. |
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.
- 2mo ago First seen · 126 lines · 48 tokens per session scan A c32e4b302786
maturity-assessment is a skill published in the GitHub repository Liberty91LTD/cti-skills (36 stars, last pushed 4d ago), licensed MIT. It adds 48 tokens to every session and 1,312 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.