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 solving-malware-with-symbolic-executiongit 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/solving-malware-with-symbolic-execution)<a href="https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/solving-malware-with-symbolic-execution"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/solving-malware-with-symbolic-execution/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/solving-malware-with-symbolic-execution"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/solving-malware-with-symbolic-execution.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 | $0.00084 | $0.00771 |
| Opus 5 | $0.00042 | $0.00385 |
| Sonnet 5 | $0.00017 | $0.00154 |
| Haiku 4.5 | $0.00008 | $0.00077 |
Grade A, and why
solving-malware-with-symbolic-execution 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Solving Malware With Symbolic Execution
When to Use
- You need to find an input that reaches (or avoids) a specific address — a license/unlock check, an environment guardrail, or an opaque predicate — without manual constraint solving.
- You want to recover a key/password a check routine validates, or prune bogus branches.
Do not use angr's unicorn/concrete execution against untrusted code outside isolation — and
do not treat symbolic execution as a substitute for understanding the routine. This skill generates
a harness; run it in an isolated environment.
Prerequisites
- The target binary and the relevant addresses (entry, target/avoid). The
angrpackage (the script degrades gracefully and reports availability if absent).
Safety & Handling
- Run generated harnesses in an isolated VM; angr loads the binary — keep inputs contained.
Workflow
Step 1: Check availability and generate a harness
python scripts/analyst.py check
python scripts/analyst.py harness --binary sample.bin --find 0x401234 --avoid 0x401260 \
--out solve.py
Emits an angr script that loads the binary, sets a symbolic stdin/argument, explores to --find
while avoiding --avoid, and prints the solving input.
Step 2: Constrain the input
Edit the harness to add the input's length/charset constraints (printable, specific size) so the solver converges quickly.
Step 3: Run and interpret
Run the harness; the recovered concrete input is the key/password/flag that reaches the target.
Step 4: Validate manually
Confirm the recovered input actually drives the intended path in a debugger/disassembler.
Validation
- The harness sets explicit find/avoid addresses and a constrained symbolic input.
- A found solution is verified to reach the target in a debugger.
- State explosion is mitigated with constraints or
veritesting.
Pitfalls
- State explosion on loops/large inputs — constrain length and use
LAZY_SOLVES/veritesting. - Symbolic-execution-hostile code (hashing, heavy crypto) that angr cannot tractably solve.
- Wrong addresses (ASLR/base) — use the binary's load base, not a runtime address.
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 · 94 lines · 84 tokens per session scan A 713b25c719b5
solving-malware-with-symbolic-execution is a skill published in the GitHub repository meltedinhex/analyst-ai-pack (22 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 84 tokens to every session and 771 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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