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 meltedinhex/analyst-ai-pack --skill scanning-samples-with-yaragit clone --depth 1 https://github.com/meltedinhex/analyst-ai-packWrote 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/meltedinhex/analyst-ai-pack/scanning-samples-with-yara)<a href="https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/scanning-samples-with-yara"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/scanning-samples-with-yara/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/meltedinhex/analyst-ai-pack/scanning-samples-with-yara"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/scanning-samples-with-yara.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 | $0.00065 | $0.00765 |
| Opus 5 | $0.00032 | $0.00382 |
| Sonnet 5 | $0.00013 | $0.00153 |
| Haiku 4.5 | $0.00006 | $0.00076 |
Grade A, and why
scanning-samples-with-yara 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 9d 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.
Scanning Samples with YARA
When to Use
- You want to classify one or many samples against known malware-family or capability rules.
- You need to confirm a family hypothesis with a targeted rule and read which strings matched.
- You are triaging a directory of files and want to flag the suspicious ones.
Do not use a YARA match as proof of family attribution on its own — public rules vary in quality and can over-match. Corroborate with behavior or code analysis.
Prerequisites
yaraCLI oryara-python(pip install yara-python).- A curated rule set (your own plus vetted public rules). Avoid blindly merging large noisy collections.
- Samples in neutralized form inside the lab.
Workflow
Step 1: Organize rules
Keep rules in categories (family, capability, packer, anomaly) and compile them once for speed. Tag rules so matches are self-describing.
Step 2: Scan and read matches
python scripts/analyst.py scan rules/ sample.bin
# prints matched rule names, tags, and matched strings with offsets
The matched string offsets matter: they tell you where in the file the signal is (header, overlay, resource), which guides deeper analysis.
Step 3: Triage a directory
python scripts/analyst.py scan rules/ ./samples --recursive --summary
Rank files by number/severity of matches to prioritize analyst time.
Step 4: Tune for false positives
If a rule fires on benign files, tighten it: require multiple strings (2 of ($a*)),
anchor to file structure (uint16(0) == 0x5A4D), or raise the condition specificity.
Step 5: Promote good signals to detection
A rule that reliably identifies a family or capability becomes a hunting/detection artifact — hand it to the detection-engineering workflow.
Validation
- Matches reproduce across runs and tools (CLI and
yara-pythonagree). - Each kept rule has an acceptable false-positive rate against a known-clean corpus.
- Match offsets correspond to meaningful file regions, not incidental byte coincidences.
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.
- 9d ago First seen · 100 lines · 65 tokens per session scan A 132919157361
scanning-samples-with-yara is a skill published in the GitHub repository meltedinhex/analyst-ai-pack (22 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 65 tokens to every session and 765 once invoked, about $0.0003 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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