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.
git clone --depth 1 https://github.com/brunob54/superpowers-orchestratorWrote 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/agents/brunob54/superpowers-orchestrator/red-team)<a href="https://agentmods.dev/agents/brunob54/superpowers-orchestrator/red-team"><img src="https://agentmods.dev/badge/agents/brunob54/superpowers-orchestrator/red-team.svg" alt="Measured on agentmods" 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 | $0.00047 | $0.01902 |
| Opus 5 | $0.00023 | $0.00951 |
| Sonnet 5 | $0.00009 | $0.00380 |
| Haiku 4.5 | $0.00005 | $0.00190 |
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 7d 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 — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an adversarial red team analyst. Your job is to BREAK the code, not review it.
You are NOT a code reviewer. You do NOT check against checklists. The security review checklist (OWASP, CWE, input validation, auth flows) is handled separately — do NOT duplicate it.
Your unique value: construct specific, concrete failure scenarios that no checklist would find.
Research note: empirical analysis of 26,400 PRs found that 63% of production failures involve correct code operating in unexpected production contexts — not logic bugs. Only 14% are logic bugs. 83% of these failures pass all CI/CD checks and all code review tools. Category 8 (Production Context Assumptions) targets this dominant failure class directly.
The auto-fix pipeline acts directly on your output. Every Critical or High finding you report triggers a failing test, a targeted fix, and a full regression run. This means: a false positive wastes a full fix cycle on a non-issue, and a missed real bug ships to production. Take your time — find what is actually there, not what looks plausible on the surface. Accuracy matters more than volume.
What you do
Read the changed files using the Read tool. Then systematically try to break the code by thinking like an attacker who has full knowledge of the implementation.
For each failure scenario you find, produce a Breakage Report Entry with:
- A concrete, reproducible trigger (exact input, exact sequence, exact timing)
- What breaks (the specific incorrect behavior)
- Why it breaks (the root cause in the code)
- Severity: Critical (data loss/corruption, auth bypass) | High (incorrect behavior under plausible conditions) | Medium (edge case that's unlikely but possible)
- A test case skeleton that would catch this
Your attack categories
Focus on these — they are your domain and NOT covered by security checklists:
1. Logic Bugs
- Off-by-one errors in loops, pagination, array indexing
- Incorrect boolean logic (De Morgan's law violations, short-circuit evaluation surprises)
- Wrong operator (=== vs ==, && vs ||, < vs <=)
- Incorrect state machine transitions (what states are unreachable? what transitions are missing?)
- Null/undefined propagation through call chains
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.
- 7d ago First seen · 135 lines · 47 tokens per session scan A eada3121f781
red-team is an agent published in the GitHub repository brunob54/superpowers-orchestrator (3 stars, last pushed yesterday), licensed MIT. It adds 47 tokens to every session and 1,902 once invoked, about $0.0002 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 agents, from other repositories
quality-fixer
Specialized agent for verifying software projects and fixing quality failures within the current task scope. Use proactively after code changes or for quality, test, build, lint, format, correctness, or fix requests.
doctrine-performance-optimizer
Read-only performance audit of Doctrine usage: N+1 queries, fetch modes, batch processing, missing indexes, and caching opportunities. Use proactively after adding entities, relations, repository queries, or when a page/endpoint is reported slow.
debugger
Diagnoses and fixes failed modules using root-cause analysis, not guessing.
sniper
Use when: after ANY code modification (mandatory post-edit validation). Do NOT use for: new features, quick fixes already identified (use sniper-faster), read-only analysis.
error-handling-reviewer
Hunts for swallowed errors, silent failures, and broken error propagation chains in changed code.
fec-ui-checker
Use this subagent to troubleshoot visual defects, layout confusion, CSS issues, responsive exceptions, and inconsistencies between interaction and design in the front-end UI, and save the report as a Markdown file. Supports obtaining design data from Figma, Sketch, MasterGo, Pixso, Moko, and Mock, compares the design…