Use when the user wants to test their LLM/agent application for safety and security vulnerabilities — jailbreaks, prompt injection, PII extraction, harmful content generation, or evaluator gaming. Also use when the user mentions security testing, adversarial testing, red teaming, safety evaluation, ASR (Attack Success Rate), or "is my app safe to deploy." Outputs ASR paired with over-refusal rate and an audit document.
SkillScore88Maintained
Automatic
◆Security & AppSec pack
Last verified 8/14/2026
What it changes
Passed 21 of 24 test cases with the skill, and 12 of 24 without it.
Without skill48%
With skill88%
Checks it passed that the model alone missed
The schema includes the vector property.
The schema includes the category property.
The command includes the --score-scale 5 flag.
and 13 more in the full report
10 fixed12 passed either way3 still failing0 broke
Pass-rate lift
+40 pp
Tokens
+135%
Agent turns
0%
Response time
-28%
Model gemini-3.6-flashMeasured 8/14/2026v122 of 25 cases passed with skill · 24 comparable
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 9 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 9
Prompt
We already have attacks.jsonl and over.jsonl from our eval run using 1-5 score ratings where scores below 3 count as violations. Which CLI command should we run using the project's bundled Python script to calculate the scorecard metrics?
What was checked, without and with the skill
Checked
Without skill
With skill
The command invokes python scripts/asr_report.py.
Missed
Passed
The command includes the --score-scale 5 flag.
Missed
Passed
The command includes the --harm-threshold 3 flag.
Missed
Passed
case-07 · gemini-3.6-flash · 8/14/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md335 lines2 filesincludes scripts
Redteam
Test your application's safety boundaries systematically. This skill generates
attack prompts from a policy document, measures what gets through, and pairs
the Attack Success Rate (ASR) with the Over-Refusal Rate so you don't reward
models that simply refuse everything.
When to Activate
Pre-deployment safety audit
Regulatory compliance check
After major model or prompt changes that could affect safety
User reports a jailbreak or injection vulnerability
Periodic (quarterly) safety re-evaluation
Checklist
You MUST create a task for each item and complete them in order:
Derive attack vectors from policy — not a fixed template
Generate attack set + over-refusal set — paired measurement
Run attacks and evaluate — ASR per policy category
Compute over-refusal rate — paired with ASR
Output safety scorecard + audit document
Fast path: run the bundled script
Once attacks and benign prompts have been run against the system and judged, compute the
scorecard with the bundled, tested script (scripts/asr_report.py, standard library only,
no OpenJudge dependency) — it always pairs ASR with over-refusal so you can't reward a
system that just refuses everything:
Cross-tool AI consultation. Use when user asks to 'consult gemini', 'ask codex', 'get second opinion', 'cross-check with claude', 'consult another AI', 'ask opencode', 'copilot opinion', or wants a second opinion from a different AI tool.