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 26zl/cybersec-toolkit --skill deploying-ransomware-canary-filesgit clone --depth 1 https://github.com/26zl/cybersec-toolkitWrote 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/26zl/cybersec-toolkit/deploying-ransomware-canary-files)<a href="https://agentmods.dev/skills/26zl/cybersec-toolkit/deploying-ransomware-canary-files"><img src="https://agentmods.dev/badge/skills/26zl/cybersec-toolkit/deploying-ransomware-canary-files/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/26zl/cybersec-toolkit/deploying-ransomware-canary-files"><img src="https://agentmods.dev/badge/skills/26zl/cybersec-toolkit/deploying-ransomware-canary-files.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00103 | $0.01023 |
| Opus 5 | $0.00051 | $0.00511 |
| Sonnet 5 | $0.00021 | $0.00205 |
| Haiku 4.5 | $0.00010 | $0.00102 |
Grade A, and why
deploying-ransomware-canary-files 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 — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deploying Ransomware Canary Files
When to Use
- Deploying proactive ransomware detection on file servers, NAS devices, or endpoint systems
- Building an early-warning system that detects ransomware before it encrypts business-critical data
- Supplementing EDR solutions with lightweight canary file monitoring on systems where agents cannot be deployed
- Testing ransomware incident response procedures by simulating canary file triggers
- Monitoring shared drives, home directories, and backup volumes for unauthorized file operations
Do not use as a replacement for endpoint protection, backup strategy, or network segmentation. Canary files are a detection layer, not a prevention mechanism.
Prerequisites
- Python 3.8+ with pip
- watchdog library (pip install watchdog)
- Write access to directories where canary files will be placed
- SMTP server credentials or Slack webhook URL for alerting
- Administrative access for placing canaries in system directories
Workflow
Step 1: Generate Canary Files
Create decoy files with realistic names and content that attract ransomware scanners. Files should have names like Passwords.xlsx, Financial_Report_2026.docx, backup_credentials.csv and contain plausible-looking but fake data. Place them in directories ransomware typically targets first: user desktops, Documents folders, network share roots, and backup paths.
Step 2: Deploy Filesystem Monitor
Use Python's watchdog library with a custom FileSystemEventHandler that watches canary file paths. The handler triggers on on_modified, on_deleted, on_moved, and on_created events for canary files. Any legitimate user or process should never touch these files, so any interaction is a high-confidence indicator of ransomware or unauthorized access.
Step 3: Configure Alert Pipeline
Wire the filesystem monitor to multiple alert channels: email via SMTP, Slack webhook POST, syslog forwarding to SIEM, and local log file. Include the triggering event type, file path, timestamp, and process information (when available) in alert payloads.
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.
- 7d ago First seen · 105 lines · 103 tokens per session scan A 60e6da487925
deploying-ransomware-canary-files is a skill published in the GitHub repository 26zl/cybersec-toolkit (54 stars, last pushed today), licensed MIT. It adds 103 tokens to every session and 1,023 once invoked, about $0.0005 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 2026-09-03.
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