Borrowing it
Nothing to install: this file belongs to SCStelz/security-investigator. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/SCStelz/security-investigator/main/.github/skills/threat-intel-campaign/SKILL.mdgit clone --depth 1 https://github.com/SCStelz/security-investigatorWrote 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/scstelz/security-investigator/threat-intel-campaign)<a href="https://agentmods.dev/skills/scstelz/security-investigator/threat-intel-campaign"><img src="https://agentmods.dev/badge/skills/scstelz/security-investigator/threat-intel-campaign/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/scstelz/security-investigator/threat-intel-campaign"><img src="https://agentmods.dev/badge/skills/scstelz/security-investigator/threat-intel-campaign.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 60 Hidden instructions were detected in comments or invisible text. These could contain malicious directives. Manual review is recommended.Fix: Audit all comments and invisible characters. Remove any instructions that direct the agent to perform unauthorized actions. Use plain, reviewable content.
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 | $0.00181 | $0.06747 |
| Opus 5 | $0.00090 | $0.03374 |
| Sonnet 5 | $0.00036 | $0.01349 |
| Haiku 4.5 | $0.00018 | $0.00675 |
Grade A, and why
threat-intel-campaign 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 13d 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.
| **Python 3** (stdlib `xml.etree`, `urllib`) | RSS/Atom parsing — no external dependency required | How it starts
The opening of the file, as written. The whole thing — 435 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Threat Intelligence Campaign Authoring — Instructions
Purpose
This skill converts published threat-intelligence reporting into tested, tuned, publish-ready threat-hunting campaigns that land in queries/threat-intelligence/YYYY-MM/. It exists to be driven either:
- Interactively — a human gives one article URL ("read this article and write/test/tune hunts"), or
- Unattended — a scheduled automation passes a feed URL and the skill triages everything published in a recent window.
It does the authoring (parse → triage → relevance gate → write → test → tune → publish files → regenerate manifest/TOCs). It deliberately does NOT create branches, commits, or pull requests. That orchestration — and the per-article PR isolation — belongs to the calling workflow. This keeps the skill reusable and free of git side effects when a human runs it.
What this skill produces:
| Output | Description |
|---|---|
| Campaign file(s) | queries/threat-intelligence/YYYY-MM/<slug>.md in the standard campaign format |
| Regenerated artifacts | .github/manifests/discovery-manifest.yaml + per-file Quick Reference TOCs |
| Structured result | A JSON array (one entry per article) the calling automation consumes to drive per-article PRs |
| Human summary | A readable per-article decision log |
| In-chat hunt findings summary | A per-article report of what the test runs actually surfaced — real hits, false positives to tune, and follow-up actions. Emitted to chat/run output only; never written to a tracked file. This is where concrete findings live, keeping the committed campaign file PII-free. |
📑 TABLE OF CONTENTS
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.
- 13d ago First seen · 435 lines · 181 tokens per session scan A 7dc66a18df83
threat-intel-campaign is a skill published in the GitHub repository SCStelz/security-investigator (245 stars, last pushed 4d ago), licensed MIT. It adds 181 tokens to every session and 6,747 once invoked, about $0.0009 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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