Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/Liberty91LTD/cti-skillsnpx agentmods add skills/liberty91ltd/cti-skills/claim-extractionWrote 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/claim-extraction)<a href="https://agentmods.dev/skills/liberty91ltd/cti-skills/claim-extraction"><img src="https://agentmods.dev/badge/skills/liberty91ltd/cti-skills/claim-extraction/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/claim-extraction"><img src="https://agentmods.dev/badge/skills/liberty91ltd/cti-skills/claim-extraction.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 | $0.00130 | $0.02292 |
| Opus 5.5 | $0.00052 | $0.00917 |
| Sonnet 5.5 | $0.00026 | $0.00458 |
| Haiku 4.5 | $0.00013 | $0.00229 |
Grade A, and why
claim-extraction 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 3d 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 — 181 lines — stays where its author put it; the contents beside it link to each section on GitHub.
claim-extraction
An event is a bundle of claims with very different evidentiary standing. "Akira exploited a SonicWall zero-day to hit a US hospital and exfiltrated 400GB" is at least four claims:
| Claim | Type | Where it comes from |
|---|---|---|
| The hospital suffered an intrusion | victim_disclosure |
The hospital's own notification |
| Initial access was through a SonicWall vulnerability | observation |
A vendor's incident response finding |
| The intrusion was carried out by Akira | attribution |
A leak-site posting plus TTP overlap |
| 400GB of data was taken | actor_claim |
The actor's own statement |
One grade on the event either averages these into something meaningless or takes the strongest and inflates the weakest. This skill splits the document so each claim can be graded on its own.
Composition. Uses the fetcher in /source-provenance. Invoked by /quality-of-information-check. Output is consumed by /ach, /key-assumptions-check, /intelligence-writing, /writing-assessments, /threat-actor-profiling and /campaign-tracking.
Inputs
- Document text, or a URL.
- Optional focus: "attribution only", "IOCs and TTPs only", or the question the claims are meant to serve.
- Optional: the provenance result from
/source-provenance, so that article-only material can be told apart from the primary's claims.
For a URL, get the text through the provenance fetcher, which honours robots.txt and keeps the full text in a local cache:
python3 skills/source-provenance/scripts/resolve_provenance.py text <url>
It prints the path to a local text file. Read that file. If the fetch fails, ask the user to paste the text.
Output
A claim table, and a short note on what was deliberately left out.
**Document:** <title> (<organisation>, <date>)
**URL:** <url>
**Claims extracted:** 8
| ID | Claim | Type | Anchor (verbatim) | Location |
|---|---|---|---|---|
| C01 | ... | observation | "..." | paragraph 3 |
**Not extracted:** background on the actor's 2023 activity (restated from earlier
reporting, not load-bearing); product recommendations; mitigation guidance.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 3d ago Changed · -35 tokens per session 701e62d46fb3
- 1mo ago First seen · 181 lines · 165 tokens per session scan A 0c9e7a72c1ad
claim-extraction is a skill published in the GitHub repository Liberty91LTD/cti-skills (36 stars, last pushed 4d ago), licensed MIT. It adds 130 tokens to every session and 2,292 once invoked, about $0.0005 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 2026-10-01.
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.