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 measuring-section-entropy-to-detect-packinggit 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/measuring-section-entropy-to-detect-packing)<a href="https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/measuring-section-entropy-to-detect-packing"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/measuring-section-entropy-to-detect-packing/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/measuring-section-entropy-to-detect-packing"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/measuring-section-entropy-to-detect-packing.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 | $0.00074 | $0.00728 |
| Opus 5 | $0.00037 | $0.00364 |
| Sonnet 5 | $0.00015 | $0.00146 |
| Haiku 4.5 | $0.00007 | $0.00073 |
Grade A, and why
measuring-section-entropy-to-detect-packing 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 8d 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Measuring Section Entropy to Detect Packing
When to Use
- You want a quick statistical signal of whether a sample is packed or encrypted.
- You are triaging a PE and need to flag suspicious high-entropy executable sections.
- You are deciding whether to route a sample to an unpacking workflow.
Do not use entropy as a verdict — legitimate compressed resources and installers also show high entropy; combine with imports, section names, and raw/virtual size anomalies.
Prerequisites
- Python (
mathstdlib); optionallypefilefor per-section analysis.
Safety & Handling
- Compute entropy on the inert file; never execute the sample.
- Keep the sample password-protected at rest and reference it by hash.
Workflow
Step 1: Compute whole-file and sliding-window entropy
Calculate Shannon entropy (0–8 bits/byte). Sliding-window entropy reveals localized high-entropy regions even when the overall value is moderate.
python scripts/analyst.py entropy sample.exe
Step 2: Compute per-section entropy (PE)
For each PE section, compute entropy and compare raw vs. virtual size. Executable sections with entropy > ~7.0 are a packing indicator.
Step 3: Correlate structural anomalies
Flag classic packer signs: high-entropy .text, unusual/renamed sections (UPX0, random
names), tiny raw size but large virtual size, and an entry point outside .text.
Step 4: Decide routing
If indicators stack (high entropy + thin IAT + odd sections), route to unpacking; otherwise proceed with normal static analysis.
Validation
- High-entropy executable sections are corroborated by other packing signs before concluding.
- Benign high-entropy cases (compressed resources) are not misclassified as packers.
- The routing decision (unpack vs. proceed) is justified by combined indicators.
Pitfalls
- Calling any high-entropy file "packed" without structural corroboration.
- Ignoring sliding-window entropy and missing a localized encrypted blob.
- Overlooking raw-vs-virtual size mismatch, a strong unpacking-at-runtime hint.
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.
- 8d ago First seen · 90 lines · 74 tokens per session scan A d91e16b540e6
measuring-section-entropy-to-detect-packing is a skill published in the GitHub repository meltedinhex/analyst-ai-pack (22 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 74 tokens to every session and 728 once invoked, about $0.0004 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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