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 analyzing-loaders-and-droppersgit 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/analyzing-loaders-and-droppers)<a href="https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/analyzing-loaders-and-droppers"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/analyzing-loaders-and-droppers/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/analyzing-loaders-and-droppers"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/analyzing-loaders-and-droppers.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 | $0.00069 | $0.00695 |
| Opus 5 | $0.00034 | $0.00347 |
| Sonnet 5 | $0.00014 | $0.00139 |
| Haiku 4.5 | $0.00007 | $0.00069 |
Grade A, and why
analyzing-loaders-and-droppers 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 11d 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.
What it actually says
Analyzing Loaders and Droppers
When to Use
- You have a first-stage loader/dropper and need to find the next stage: an embedded resource, an appended overlay, or a download URL.
- You need to identify the decode/decrypt routine and the execution mechanism (process creation, injection, scheduled task, DLL side-loading).
Do not use this to run the loader and fetch live stages — characterize staging statically and retrieve next stages only in an isolated sandbox.
Prerequisites
- The loader/dropper sample (read inertly).
Safety & Handling
- Read bytes statically; defang download URLs; never execute to pull live payloads.
Workflow
Step 1: Locate staged payloads
python scripts/analyst.py stage sample.bin
Detects embedded PE/archive signatures, an appended overlay beyond the PE's mapped size, large high-entropy blobs, and download URLs.
Step 2: Identify decode/execution mechanism
Scan imports/strings for decode APIs (CryptDecrypt, base64), download APIs (URLDownloadToFile,
WinHttp, InternetReadFile), and execution (CreateProcess, ShellExecute, WinExec,
side-loading hints).
Step 3: Map the delivery chain
Document stage-1 → decode → stage-2 → execution and map each step to ATT&CK.
Step 4: Defang and report
Defang URLs and produce IOCs for the staging infrastructure.
Validation
- Embedded/overlay stages are confirmed by signature or entropy, with offsets recorded.
- Download URLs and execution mechanism are identified from imports/strings.
- The multi-stage chain maps cleanly to ATT&CK techniques.
Pitfalls
- Treating a benign overlay (installer data, signature) as a payload without corroboration.
- Missing stages fetched at runtime when no URL is in plaintext (obfuscated/encoded).
- Confusing the decode routine for the payload itself.
References
- See
references/api-reference.mdfor the stager. - ATT&CK T1105 and T1140 (linked in frontmatter).
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.
- 11d ago First seen · 91 lines · 69 tokens per session scan A 2a7eea3911f2
analyzing-loaders-and-droppers is a skill published in the GitHub repository meltedinhex/analyst-ai-pack (22 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 69 tokens to every session and 695 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 2026-08-30.
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