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.
npx skills add gaasher/Agent-Loop-Skills --skill purple-teamgit clone --depth 1 https://github.com/gaasher/Agent-Loop-SkillsWrote 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/gaasher/agent-loop-skills/purple-team)<a href="https://agentmods.dev/skills/gaasher/agent-loop-skills/purple-team"><img src="https://agentmods.dev/badge/skills/gaasher/agent-loop-skills/purple-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/gaasher/agent-loop-skills/purple-team"><img src="https://agentmods.dev/badge/skills/gaasher/agent-loop-skills/purple-team.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 145 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
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 | $0.00183 | $0.02619 |
| Opus 5 | $0.00092 | $0.01309 |
| Sonnet 5 | $0.00037 | $0.00524 |
| Haiku 4.5 | $0.00018 | $0.00262 |
Grade A, and why
purple-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 9d 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 — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purple Team
The combined red+blue loop — the outer orchestration the red-team skill says "lives outside it."
The artifact is a target system (frozen within a phase, patched between phases); the feedback signal
is how many new failure classes a fresh attack pass finds against the patched target. Each cycle
runs three strictly separated phases — find (red-team), fix (blue-team), re-verify (a fresh
red-team pass) — and you repeat until a fresh find stays dry (zero new classes), meaning the target
is hardened. Red and blue run as independent agents so the attacker that wrote a catalogue never
grades its own patch. On stop it opens a pull request with the cycle history and the patch set.
When to use
Use to harden a guardrail/classifier/filter/prompt/API the user owns or is authorized to test, when the
goal is an actually-hardened target plus a reviewable patch — not just a catalogue (that is red-team
alone) and not just closing a pre-existing catalogue (that is blue-team alone). It needs a runnable
oracle for the objective signal, and the two sibling skills installed.
Default: spawn red and blue as separate subagents per phase. Escape hatch: on hosts without subagent dispatch, run each phase inline (serial) per the role files — still correct, but the same context plays both sides, so be deliberate about not letting the fix bias the re-verify. Not for unauthorized targets.
Setup
Resolve bindings interactively. If loop.run.yaml exists, load it, confirm the values in one line, and
skip to the loop. Otherwise: on Claude Code (the AskUserQuestion tool is available) infer a likely
value per binding and recommend it; on other hosts ask each as a quoted prompt. Then write
loop.run.yaml (format: examples/run.example.yaml) and confirm before creating any other files.
The phases reuse the sibling skills, so the bindings are their union — one shared loop.run.yaml drives
both. The same file is <target_files> to blue (writable) and the program behind <target_cmd> to red
(read-only); the same path is red's <failures_log> and blue's <catalogue>.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 9d ago First seen · 147 lines · 183 tokens per session scan A d1673718824e
purple-team is a skill published in the GitHub repository gaasher/Agent-Loop-Skills (166 stars, last pushed 2mo ago), licensed MIT. It adds 183 tokens to every session and 2,619 once invoked, about $0.0009 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 2026-08-30.
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