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 andycungkrinx91/konoha --skill deploying-decoy-files-for-ransomware-detectiongit clone --depth 1 https://github.com/andycungkrinx91/konohaWrote 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/andycungkrinx91/konoha/deploying-decoy-files-for-ransomware-detection)<a href="https://agentmods.dev/skills/andycungkrinx91/konoha/deploying-decoy-files-for-ransomware-detection"><img src="https://agentmods.dev/badge/skills/andycungkrinx91/konoha/deploying-decoy-files-for-ransomware-detection/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/andycungkrinx91/konoha/deploying-decoy-files-for-ransomware-detection"><img src="https://agentmods.dev/badge/skills/andycungkrinx91/konoha/deploying-decoy-files-for-ransomware-detection.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 | $0.00087 | $0.01865 |
| Opus 5.5 | $0.00035 | $0.00746 |
| Sonnet 5.5 | $0.00017 | $0.00373 |
| Haiku 4.5 | $0.00009 | $0.00186 |
Grade A, and why
deploying-decoy-files-for-ransomware-detection 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 today.
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.
This is a copy
89% identical to deploying-decoy-files-for-ransomware-detection — 23 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 223 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deploying Decoy Files for Ransomware Detection
When to Use
- Setting up early-warning detection for ransomware on file servers or endpoints
- Supplementing EDR/AV with a deception-based detection layer that catches unknown ransomware variants
- Creating high-fidelity ransomware alerts that have very low false-positive rates (legitimate users have no reason to touch decoy files)
- Testing ransomware response procedures by validating that canary file modifications trigger the expected alerting pipeline
- Protecting high-value file shares (finance, HR, legal) with tripwire files that indicate unauthorized encryption activity
Do not use decoy files as the sole ransomware defense. They are a detection mechanism, not a prevention mechanism, and should complement backups, EDR, and access controls.
Prerequisites
- Python 3.8+ with
watchdoglibrary for cross-platform file system monitoring - Administrative access to target file shares or endpoints for canary placement
- File integrity monitoring (FIM) tool or SIEM integration for alert routing
- Understanding of target directory structure to place canaries in high-value locations
- Windows: NTFS change journal or ReadDirectoryChangesW API access
- Linux: inotify support in kernel (standard in modern kernels)
Workflow
Step 1: Design Canary File Strategy
Plan file placement for maximum detection coverage:
Canary File Placement Strategy:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Naming Convention:
- Use names that sort FIRST and LAST alphabetically in each directory
- Ransomware typically enumerates directories A-Z or Z-A
- Examples: _AAAA_budget_2024.docx, ~zzzz_report_final.xlsx
Placement Locations:
- Root of every file share (\\server\share\_AAAA_canary.docx)
- Desktop, Documents, Downloads on each endpoint
- Department-specific shares (Finance, HR, Legal)
- Backup staging directories
- Home directories of high-privilege accounts
File Types:
- .docx, .xlsx, .pdf (most targeted by ransomware)
- .sql, .bak (database files, high value)
- Mix of file types to detect ransomware that targets specific extensions
What ships with it
2 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.
- today First seen · 223 lines · 87 tokens per session scan A ab4c9d15a796
deploying-decoy-files-for-ransomware-detection is a skill published in the GitHub repository andycungkrinx91/konoha (12 stars, last pushed today), licensed MIT. It adds 87 tokens to every session and 1,865 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 89% identical to deploying-decoy-files-for-ransomware-detection, differing in 23 lines, and is treated as a copy.
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