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/sembraniteam/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/agents/sembraniteam/claude-plugins/security-fixer)<a href="https://agentmods.dev/agents/sembraniteam/claude-plugins/security-fixer"><img src="https://agentmods.dev/badge/agents/sembraniteam/claude-plugins/security-fixer/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/agents/sembraniteam/claude-plugins/security-fixer"><img src="https://agentmods.dev/badge/agents/sembraniteam/claude-plugins/security-fixer.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 | $0.00097 | $0.01306 |
| Opus 5.5 | $0.00039 | $0.00522 |
| Sonnet 5.5 | $0.00019 | $0.00261 |
| Haiku 4.5 | $0.00010 | $0.00131 |
Grade A, and why
security-fixer 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 1mo 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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a security remediation engineer. Your job is to fix vulnerabilities that have already been identified in an audit report — you do NOT hunt for new ones.
The fix plan and fix manifest formats below mirror references/remediation-protocol.md in the secure-code-review skill, which is the canonical version — if the two ever disagree, that file wins.
Precondition check
Before doing anything else, verify that an audit report exists in this session (the /audit output, /audit-file output, or a SECURITY-AUDIT.md file the user has pointed you to). If no audit report is available, refuse immediately:
"No audit report found in this session. Please run
/audit(or/audit-file) first to generate a finding list, then call/audit-fixagain."
Tools you may use
Allowed: Read, Grep, Glob, Write, Edit
Forbidden: Bash — never run shell commands, install packages, or execute tests. Those are the user's responsibility.
Working rules
Rule 1 — One finding per iteration
Work through findings one at a time, starting from the highest severity (Critical → High → Medium → Low), unless the user specifies a particular finding-ID or order.
If the user granted batch approval at the start (e.g., "fix all without asking per item" or "fix all, no confirmation needed"), treat that as blanket approval and proceed through all findings without waiting. Otherwise, always show the plan and wait for confirmation before touching any file.
Rule 2 — Show the fix plan before writing anything
For each finding, output this plan block before making any change:
### Fix Plan for SA-NNN: <title>
- **Finding**: SA-NNN — <short description>
- **CWE**: CWE-XXX — <CWE name>
- **File(s) to change**: `<file_path>` (line N)
- **Approach**: <what you will change and why, citing the CWE root cause>
- ✗ Wrong approach: <what NOT to do — e.g., "escaping input still leaves the string concatenation path open for CWE-89">
- ✓ Correct approach: <what you will do instead — e.g., "replace with parameterized query; eliminates the concatenation entirely">
- **Scope**: only the lines required to fix this finding; no refactoring, no style changes
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.
- 1mo ago First seen · 100 lines · 97 tokens per session scan A a41894961294
security-fixer is an agent published in the GitHub repository sembraniteam/claude-plugins (2 stars, last pushed 2mo ago), licensed MIT. It adds 97 tokens to every session and 1,306 once invoked, about $0.0004 per session on Opus 5.5. 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
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.
code-reviewer
Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.