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/.claude/commands/redteam-graph.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/commands/contoso-state/red-team-agent-orchestration/redteam-graph)<a href="https://agentmods.dev/commands/contoso-state/red-team-agent-orchestration/redteam-graph"><img src="https://agentmods.dev/badge/commands/contoso-state/red-team-agent-orchestration/redteam-graph/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/commands/contoso-state/red-team-agent-orchestration/redteam-graph"><img src="https://agentmods.dev/badge/commands/contoso-state/red-team-agent-orchestration/redteam-graph.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 | $0.00012 | $0.00534 |
| Opus 5 | $0.00006 | $0.00267 |
| Sonnet 5 | $0.00002 | $0.00107 |
| Haiku 4.5 | $0.00001 | $0.00053 |
Grade A, and why
redteam-graph 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 10d 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 — 29 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Azure Red Team — Graph Orchestration Standard
Every engagement in this framework runs as ONE explicit, declarative graph. The single source of truth is graph/redteam.graph.json (redteam-azure v2.0.0): 14 nodes over a 12-specialist read-only roster, validated by tools/graph/validate-graph.mjs.
Two engines, one graph
- Dependency-free Node runner —
tools/graph/run-graph.mjs— executes the graph inside every CLI runtime (GitHub Copilot, Claude Code, OpenAI Codex, Cursor) with zero runtime dependencies. - First-class LangGraph target —
integrations/langgraph/— compiles the same JSON into a PythonStateGraphfor deployment.
Control flow
validate_scope (one subscription + read-only attestation) -> memory_load -> preflight_inventory -> Send fan-out across the roster (plan_specialists -> run_specialist) -> deterministic fan-in merge (collect_raw) -> bounded evaluator-optimizer reflection loop (evaluate, max_revisions=2, quality_threshold=0.85) -> Agent-as-a-Judge false-positive gate (judge) -> human-authorized active lanes (authorize_active interrupt) -> correlate -> report -> reflexion_debrief.
Self-improving loops (primary standard)
The evaluator-optimizer cycle, the Agent-as-a-Judge gate, and the reflexion debrief are auto-applied at runtime with NO pull request and NO human gate — tools/graph/self-improve.mjs. Cross-run learning is written ONLY to the memory/methodology/ namespace.
Immutable boundary
Read-only enforcement in guardrails/guard.mjs sits OUTSIDE the learning surface. Self-improvement can never modify guardrails/** or any guardrail namespace — that firewall is the one thing the graph cannot rewrite.
When you plan or run an engagement, follow this graph. Do not invent an ad-hoc order of operations; extend the team by editing graph/redteam.graph.json (and its roster) and re-validating, so every runtime and the LangGraph target stay in lock-step.
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.
- 10d ago First seen · 29 lines · 12 tokens per session scan A 49291160599b
redteam-graph is a command published in the GitHub repository Contoso-State/red-team-agent-orchestration (6 stars, last pushed 5d ago), licensed MIT. It adds 12 tokens to every session and 534 once invoked, about $0.0001 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.