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/abossenbroek/abossenbroek-claude-pluginsWrote 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/abossenbroek/abossenbroek-claude-plugins/red-team-w-fix)<a href="https://agentmods.dev/commands/abossenbroek/abossenbroek-claude-plugins/red-team-w-fix"><img src="https://agentmods.dev/badge/commands/abossenbroek/abossenbroek-claude-plugins/red-team-w-fix/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/abossenbroek/abossenbroek-claude-plugins/red-team-w-fix"><img src="https://agentmods.dev/badge/commands/abossenbroek/abossenbroek-claude-plugins/red-team-w-fix.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 | $0.00009 | $0.01656 |
| Opus 5 | $0.00005 | $0.00828 |
| Sonnet 5 | $0.00002 | $0.00331 |
| Haiku 4.5 | $0.00001 | $0.00166 |
Grade A, and why
red-team-w-fix 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 — 263 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/redteam-w-fix Command
Red team analysis with interactive fix selection. Identifies issues, generates fix options, and lets you choose which fixes to apply.
Usage
/redteam-w-fix [mode] [target]
Arguments
mode (optional):
quick- Fast 2-3 vector analysis, skip groundingstandard- Balanced 5-6 vectors with basic grounding (default)deep- All categories + meta-analysis with full groundingfocus:[category]- Deep dive on specific category (e.g.,focus:reasoning-flaws)
target (optional):
conversation- Current conversation context (default)file:path- Analyze specific filecode- Analyze recent git changes
Instructions
You are the entry point for red team analysis with fix planning. Your job is to:
- Check PAL MCP availability (optional enhancement)
- Parse the mode and target from arguments
- Extract a structured context snapshot
- Launch the fix-coordinator to get findings with fix options
- Present an interactive menu for fix selection
- Generate an implementation summary based on selections
Step 1: Check PAL Availability (Non-Blocking)
Launch the pal-availability-checker agent to detect if PAL MCP is available:
Task: Launch pal-availability-checker agent
Agent: agents/pal-availability-checker.md
Prompt: Check if PAL MCP is available and list models
Parse the YAML result and include pal_available: true/false in the snapshot.
This step is NON-BLOCKING - continue regardless of result. PAL is optional.
Step 2: Parse Arguments
Determine mode and target from the command arguments:
- Default mode:
standard - Default target:
conversation
Step 3: Extract Context Snapshot
Create a YAML-formatted snapshot of the current session. DO NOT include raw conversation - structure it as data:
snapshot:
mode: [parsed mode]
target: [parsed target]
pal_available: [true/false from Step 1]
pal_models: [list of models if available, empty if not]
conversational_arc:
message_count: [count of messages in conversation]
phases:
- phase: "[phase name]"
messages: [range]
summary: "[what happened in this phase]"
key_transitions:
- from_msg: [number]
to_msg: [number]
note: "[what changed and why]"
early_assumptions_carried_forward:
- assumption: "[assumption text]"
introduced_at: [message number]
still_active: [true/false]
claims:
- id: C[N]
text: "[factual claim made by assistant]"
speaker: assistant
confidence: [stated_as_fact|hedged|uncertain]
message_num: [source message number]
files_read:
- path: [file path]
summary: "[brief description of content/purpose]"
tools_invoked:
- tool: [tool name]
command: "[command or action]"
outcome: "[result summary]"
decisions:
- decision: "[decision made]"
rationale: "[stated reason]"
assumptions_explicit:
- "[explicitly stated assumption]"
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 · 263 lines · 9 tokens per session scan A 38a48c701bf2
red-team-w-fix is a command published in the GitHub repository abossenbroek/abossenbroek-claude-plugins (2 stars, last pushed 4mo ago), licensed BSD-3-Clause. It adds 9 tokens to every session and 1,656 once invoked, about $0.0000 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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