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 NovaCode37/claude-security-skills --skill prompt-injection-testergit clone --depth 1 https://github.com/NovaCode37/claude-security-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/novacode37/claude-security-skills/prompt-injection-tester)<a href="https://agentmods.dev/skills/novacode37/claude-security-skills/prompt-injection-tester"><img src="https://agentmods.dev/badge/skills/novacode37/claude-security-skills/prompt-injection-tester/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/novacode37/claude-security-skills/prompt-injection-tester"><img src="https://agentmods.dev/badge/skills/novacode37/claude-security-skills/prompt-injection-tester.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00084 | $0.00904 |
| Opus 5.5 | $0.00034 | $0.00362 |
| Sonnet 5.5 | $0.00017 | $0.00181 |
| Haiku 4.5 | $0.00008 | $0.00090 |
Grade A, and why
prompt-injection-tester 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 — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Injection Tester
A defensive red-team harness for evaluating the prompt-injection resistance of LLM applications you own or are authorized to test. It ships a library of well-documented public attack techniques and a canary-based detection engine that decides whether each attack succeeded — then scores overall resilience.
⚠️ Use only against systems you own or have permission to test. The payloads are public hardening techniques, intended to strengthen guardrails.
When to use this skill
- "Is my chatbot vulnerable to prompt injection / jailbreaks?"
- "Red-team / pentest my LLM app's system prompt."
- "Score how well my guardrails resist instruction-override attacks."
- Regression-testing guardrails in CI after a prompt change.
Attack categories covered
instruction-override · system-prompt-leak · role-play (DAN-style) ·
delimiter-escape · encoding (base64/leetspeak) · data-exfiltration
(indirect injection) · refusal-suppression.
How it works
- A unique canary secret is embedded into a guarded system prompt.
- Each payload is sent as the user turn.
- The response is scored: it's vulnerable if it hits an attack success-marker or leaks the canary; resisted if it refuses.
- You get a resilience score (0–100) and a per-category breakdown.
How to run it
List the payload library (no model calls):
python skills/prompt-injection-tester/attacker.py --list
python skills/prompt-injection-tester/attacker.py --list --category role-play
See the harness detect a weak vs. a hardened model:
python skills/prompt-injection-tester/attacker.py --demo # weak model
python skills/prompt-injection-tester/attacker.py --demo --hardened # safe model
python skills/prompt-injection-tester/attacker.py --demo --json
Exit codes: 0 clean (no payload succeeded, or --list) · 1 at least one
payload succeeded · 2 usage error.
Testing a real model (recommended workflow for Claude)
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.
- 1mo ago Changed · +3 lines 9cee101b91ad
- 1mo ago First seen · 102 lines · 84 tokens per session scan A d91d947b583a
prompt-injection-tester is a skill published in the GitHub repository NovaCode37/claude-security-skills (13 stars, last pushed 18d ago), licensed MIT. It adds 84 tokens to every session and 904 once invoked, about $0.0003 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.
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