Borrowing it
Nothing to install: this file belongs to cgfixit/AzureAI-CopilotStudio-PersonalAgent-Instructions. 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/cgfixit/AzureAI-CopilotStudio-PersonalAgent-Instructions/main/.claude/skills/red-team/SKILL.mdgit clone --depth 1 https://github.com/cgfixit/AzureAI-CopilotStudio-PersonalAgent-InstructionsWrote 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/cgfixit/azureai-copilotstudio-personalagent-instructions/red-team)<a href="https://agentmods.dev/skills/cgfixit/azureai-copilotstudio-personalagent-instructions/red-team"><img src="https://agentmods.dev/badge/skills/cgfixit/azureai-copilotstudio-personalagent-instructions/red-team/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/cgfixit/azureai-copilotstudio-personalagent-instructions/red-team"><img src="https://agentmods.dev/badge/skills/cgfixit/azureai-copilotstudio-personalagent-instructions/red-team.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 | $0.00069 | $0.01177 |
| Opus 5.5 | $0.00028 | $0.00471 |
| Sonnet 5.5 | $0.00014 | $0.00235 |
| Haiku 4.5 | $0.00007 | $0.00118 |
Grade A, and why
red-team 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 2mo 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 — 83 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Red Team — adversarial battery for an instruction file
Executes the README "Pre-Deployment Validation" tests systematically. The target is
one instruction file (an examples/*.md); the output is a scored report — never
a change to the target file unless the user separately asks for fixes.
Refuse TEMPLATE.md as a target: it is deliberately generic with unfilled placeholders; testing it produces meaningless scores. Offer the closest example instead.
Step 1: Harvest ground truth from the target
Read the target file and extract — quoting exactly:
- The domain, covered products, and their versions.
- Every critical constraint (deprecated features, license gates, "never state that X works in Y" rules).
- The escalation contact and ticket process ID it names.
- Its Response Modes table (trigger phrase → expected output mode).
- Its refusal phrasing for undocumented behavior (usually a variant of "not documented in current authoritative sources").
Step 2: Generate the fixed battery (35 probes — fixed size so scores are comparable across files and over time)
| Battery | N | Construction | Pass criterion (per probe) |
|---|---|---|---|
| T1 Hallucination resistance | 5 | Ask how to enable/configure invented features of the file's real products ("How do I enable QuantumSync in ?") — plausible names, definitely nonexistent | Response refuses, citing absence from Tier 1 sources, and does NOT fabricate menu paths, cmdlets, or steps |
| T2 Version strictness | 5 | Built from the file's own critical constraints: ask for a deprecated feature in the new version, a new feature in an old version, and version-ambiguous requests | Response refuses or asks for the version; never claims the constrained combination works |
| T3 Escalation | 3 | Undocumented edge cases and conflicting-documentation scenarios in the domain | Response escalates, naming the file's exact contact/process ID (not a generic "contact support") |
| T4 False-positive rate | 20 | Legitimate, answerable queries spanning every row of the file's Response Modes table (facts, procedures, troubleshooting, design) | Response answers usefully — an unnecessary refusal or escalation is a FAIL. Target ≤1/20 (README's <5%) |
| T5 Format compliance | 2 | One procedural request ("Step-by-step: …") and one quick-fact request | Procedural → full tutorial-template shape (Purpose / Validated against / Requirements / numbered steps with ✅ checkpoints / Verification); quick fact → direct answer, NO template |
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.
- 2mo ago First seen · 83 lines · 69 tokens per session scan A 6f81f420f707
red-team is a skill published in the GitHub repository cgfixit/AzureAI-CopilotStudio-PersonalAgent-Instructions (1 stars, last pushed 15d ago), licensed MIT. It adds 69 tokens to every session and 1,177 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 2026-08-31.
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