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 jscraik/Agent-Skills --skill talk-katsioloudes-code-security-aigit clone --depth 1 https://github.com/jscraik/Agent-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/jscraik/agent-skills/talk-katsioloudes-code-security-ai)<a href="https://agentmods.dev/skills/jscraik/agent-skills/talk-katsioloudes-code-security-ai"><img src="https://agentmods.dev/badge/skills/jscraik/agent-skills/talk-katsioloudes-code-security-ai/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/jscraik/agent-skills/talk-katsioloudes-code-security-ai"><img src="https://agentmods.dev/badge/skills/jscraik/agent-skills/talk-katsioloudes-code-security-ai.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 | $0.00134 | $0.01838 |
| Opus 5 | $0.00067 | $0.00919 |
| Sonnet 5 | $0.00027 | $0.00368 |
| Haiku 4.5 | $0.00013 | $0.00184 |
Grade A, and why
talk-katsioloudes-code-security-ai 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 12d 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Security Reinvented: Navigating the era of AI — Joseph Katsioloudes (GitHub Security Lab)
Joseph argues that with only 1 application security specialist per 100 developers, AI is the leverage that can close — or widen — the security gap, depending on whether we use it responsibly. Through a tour of practical demos he shows how to use AI to write safer code, leverage MCP servers and skills, make supply chain decisions, fix vulnerabilities faster in the PR, and educate developers — while being honest about hallucinations, non-determinism, and the limits of AI as a security tool. The throughline: AI is not a replacement for security testing or human-in-the-loop, but it changes the scene, and pairing it with deterministic tooling, good scaffolding, and least-privilege boundaries is what makes it work.
Grounding rules — MUST follow when answering
- Before answering any specific question, follow this source-reading sequence: (a) read
outline.mdto locate the relevant section or concept; (b) read the matching range oftranscript.mdfor Joseph's exact wording; (c) checkquote.mdfor pre-extracted safe highlights on the topic before searching the full transcript. This sequence applies to every workflow section below. - When attributing words, quote short, non-sensitive excerpts from
transcript.md. Never put quotation marks around paraphrased content. - If a claim isn't in
transcript.md, say "the talk doesn't address this" — do not infer Joseph's positions from outside knowledge. - Cite by transcript line range whenever possible.
- Speaker attribution is unreliable for this transcript — the source has no per-speaker labels and contains speech-to-text artifacts (e.g. "Macy" the emcee, garbled product names, "Llamas" likely = "LLM-as-judge"). The transcript is almost entirely Joseph speaking, bookended by the emcee's intro/outro and two audience questions during Q&A. Prefer phrasing like "Joseph said..." for the body of the talk, "an audience member asked..." for Q&A, and "the emcee said..." for the framing. Do not invent attributions.
- Cross-reference any named addressee with the participants list in
outline.mdbefore attributing. Where the transcript clearly garbles a term (e.g. "Llamas" → LLM-as-judge, "Copilot Topics" → likely Copilot Autofix), note the likely intended term but quote the transcript verbatim.
What ships with it
4 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.
- 12d ago First seen · 90 lines · 134 tokens per session scan A 3c04663c0c67
talk-katsioloudes-code-security-ai is a skill published in the GitHub repository jscraik/Agent-Skills (8 stars, last pushed 10d ago), licensed Apache-2.0. It adds 134 tokens to every session and 1,838 once invoked, about $0.0007 per session on Opus 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 2026-08-31.
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