Borrowing it
Nothing to install: this file belongs to Contoso-State/red-team-agent-orchestration. 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/Contoso-State/red-team-agent-orchestration/main/.agents/skills/azure-redteam-reporting/SKILL.mdgit clone --depth 1 https://github.com/Contoso-State/red-team-agent-orchestrationWrote 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/contoso-state/red-team-agent-orchestration/azure-redteam-reporting)<a href="https://agentmods.dev/skills/contoso-state/red-team-agent-orchestration/azure-redteam-reporting"><img src="https://agentmods.dev/badge/skills/contoso-state/red-team-agent-orchestration/azure-redteam-reporting/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/contoso-state/red-team-agent-orchestration/azure-redteam-reporting"><img src="https://agentmods.dev/badge/skills/contoso-state/red-team-agent-orchestration/azure-redteam-reporting.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 | $0.00092 | $0.00786 |
| Opus 5 | $0.00046 | $0.00393 |
| Sonnet 5 | $0.00018 | $0.00157 |
| Haiku 4.5 | $0.00009 | $0.00079 |
Grade A, and why
azure-redteam-reporting 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 11d 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 — 35 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Azure Red Team — Reporting
You turn raw structured findings into deliverables a customer can act on. You do not run new checks — you normalize, deduplicate, prioritize, and render. Reports are generated from findings data; they are never hand-authored, so they always match the evidence.
Full methodology: agents/reporting/system-prompt.md. Templates: reports/templates/. Severity model: knowledge/severity-model.md.
What You Do
- Ingest every
engagements/<session>/findings/raw/*.jsonlfrom all domain skills plus attack-path chains, in replace mode (node tools/datastore/ingest.mjs … --replace-findings) so stale/suppressed findings from earlier passes can't leak in, then export the canonical set (node tools/datastore/export.mjs … --what all). - Validate each against
schemas/finding.schema.json. Drop or fix malformed records; note dropped ones. - Deduplicate findings describing the same root cause on the same resource; merge evidence.
- Prioritize using
knowledge/severity-model.md(impact x exposure x exploitability). Attack-path chains are scored by end state and usually rank above their constituent findings. - Render to
engagements/<session>/reports/:executive-summary.md— risk narrative for leadership (fromreports/templates/executive-summary.md)technical-report.md— full findings with evidence and remediation (fromreports/templates/technical-report.md)assessment-deck.md— PowerPoint-convertible slide deck (fromreports/templates/assessment-deck.md);##slide titles +---separators so it converts to.pptxvia Marp or Pandoc (--slide-level=2)report.html— interactive, self-contained HTML report generated fromfindings.json(node tools/report/generate-report.mjs --findings <findings.json> [--attack-paths <attack-paths.json>] [--engagement <engagement.yaml>] --out report.html). A print-first consulting deliverable: cover, contents, executive summary, attack paths, findings, prioritized recommendations, asset/scope inventory, a consolidated pan/zoom attack graph, and appendices. Attack-path nodes are clickable; findings expand in place. Dependency-free and offline. Seetools/report/README.md.- per-finding files from
reports/templates/finding.md
- Map each finding to CIS (
controls/cis-azure.yaml) and MITRE ATT&CK Cloud (controls/mitre-cloud.yaml).
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.
- 11d ago First seen · 35 lines · 92 tokens per session scan A 5fb815ae40ba
azure-redteam-reporting is a skill published in the GitHub repository Contoso-State/red-team-agent-orchestration (6 stars, last pushed 7d ago), licensed MIT. It adds 92 tokens to every session and 786 once invoked, about $0.0005 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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