Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/trilwu/secskillsnpx agentmods add skills/trilwu/secskills/writing-yara-rulesWrote 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/trilwu/secskills/writing-yara-rules)<a href="https://agentmods.dev/skills/trilwu/secskills/writing-yara-rules"><img src="https://agentmods.dev/badge/skills/trilwu/secskills/writing-yara-rules/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/trilwu/secskills/writing-yara-rules"><img src="https://agentmods.dev/badge/skills/trilwu/secskills/writing-yara-rules.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 | $0.00114 | $0.04537 |
| Opus 5 | $0.00057 | $0.02269 |
| Sonnet 5 | $0.00023 | $0.00907 |
| Haiku 4.5 | $0.00011 | $0.00454 |
Grade A, and why
writing-yara-rules scanned grade A with 1 finding 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 12d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -sL "https://defuddle.md/<url>" # scheme in the path is optional Copies of this mod
1 near-identical copy found in the catalogue:
- writing-yara-rules — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 403 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Writing YARA Rules
A good YARA rule matches the malware's nature — its code, its structure, its unavoidable constants — not its costume, which is a filename or a mutable string that changes on the next build. Match the nature and the rule survives recompilation and catches the whole family; match the costume and you catch one sample once. The entire craft is choosing strings and a condition that are both specific enough to avoid false positives and durable enough to generalize.
When to Use
- Writing a YARA rule to detect a malware family, tool, or capability
- Creating a signature for a file or in-memory artifact from a known sample
- Turning IOCs or a captured specimen into a portable, testable detection
- Hunting for a family across a corpus (VirusTotal Retrohunt, LOKI/THOR, on-host scan)
- Clustering related samples by shared code, constants, or structure
- Reviewing or tuning an existing rule for false positives and scan performance
When NOT to Use
- The detection belongs in log or SIEM telemetry, not on files or memory —
use
writing-sigma-rules - Choosing what to detect and where it should live, the strategy above rule
syntax — use
engineering-detections - You do not yet understand the sample well enough to pick durable anchors —
use
analyzing-malwarefirst - Running the hunt rather than authoring the signature — use
hunting-threats - Packaging IOCs, attribution, and a finished report — use
producing-threat-intelligence
Rule Anatomy
Three sections: meta (documentation, never matched on), strings (the
patterns), condition (the boolean that decides a hit).
import "pe"
rule Family_Loader_v1
{
meta:
author = "analyst"
date = "2026-07-26"
description = "ExampleLoader stage-1, config-decode stub + family constants"
hash = "a1b2c3...<sha256 of the analyzed sample>"
reference = "https://internal/case/1234"
version = "1"
tlp = "amber"
strings:
$decode = { 8A 04 0? 32 0? 88 0? 4? 3B ?? 72 }
$marker = "cfg::begin" ascii
$mutex = "Global\\ExL-%08x" ascii
condition:
uint16(0) == 0x5A4D and filesize < 2MB and 2 of them
}
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.
- 12d ago First seen · 403 lines · 114 tokens per session scan A f96db69f0795
writing-yara-rules is a skill published in the GitHub repository trilwu/secskills (138 stars, last pushed 7d ago), licensed MIT. It adds 114 tokens to every session and 4,537 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
context-engineering
Optimizes agent context setup. Use when starting a new session, when agent output quality degrades, when switching between tasks, or when you need to configure rules files and context for a project.
comet-memory
A review step for deciding whether information should become durable personal memory. It can keep, update, forget, or skip memory candidates based on bounded evidence.
recall-memory
Recall relevant long-term memories on demand. Given a topic or question, judges relevance from pre-loaded metadata, loads only relevant files, and returns a concise summary to the main agent.
agent-expert-creation
Create specialized agent experts with pre-loaded domain knowledge using the Act-Learn-Reuse pattern. Use when building domain-specific agents that maintain mental models via expertise files and self-improve prompts.
relevance-coarse-filter
Cheap, high-recall first-pass filter that removes obvious junk from a detector candidate pool before expensive story-origin research and PR judgment. Decides keep, monitoronly, or reject — never ranks, writes angles, verifies dates, or decides whether to pitch.
self-improve
Extract lessons from the current session, or sweep the project's past sessions when asked, and route them to the appropriate knowledge layer (project AGENTS.md, auto memory, existing skills, or new skills). Use when the user asks to "self-improve", "distill this session", "distill past sessions", "sweep past…