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 defeating-control-flow-flatteninggit 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/defeating-control-flow-flattening)<a href="https://agentmods.dev/skills/meltedinhex/analyst-ai-pack/defeating-control-flow-flattening"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/defeating-control-flow-flattening/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/defeating-control-flow-flattening"><img src="https://agentmods.dev/badge/skills/meltedinhex/analyst-ai-pack/defeating-control-flow-flattening.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 | $0.00666 |
| Opus 5 | $0.00037 | $0.00333 |
| Sonnet 5 | $0.00015 | $0.00133 |
| Haiku 4.5 | $0.00007 | $0.00067 |
Grade A, and why
defeating-control-flow-flattening 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
Defeating Control-Flow Flattening
When to Use
- A function is flattened (a dispatcher loop switching on a state variable, with original blocks as switch cases) and you need to recover the original control flow.
- You are reversing OLLVM-style or similar flattening.
Do not use this on non-flattened code — confirm the dispatcher/state-variable pattern first. This skill analyzes disassembly/CFG data statically and executes nothing.
Prerequisites
- A disassembled function's basic-block/edge data (e.g., exported from Ghidra/IDA/Binary Ninja as JSON).
Workflow
Step 1: Detect the flattening structure
python scripts/analyst.py detect cfg.json
Identifies a dispatcher block (high in-degree, switch/compare on a state variable) and the relay/ case blocks that all branch back to it.
Step 2: Recover state transitions
Track the constant assigned to the state variable in each case to determine the successor block, rebuilding the true edges.
Step 3: Reconstruct the original CFG
Emit the deflattened successor mapping (case → next case) for re-annotation in the disassembler.
Step 4: Verify
Confirm the recovered flow has a single entry, sane successors, and no orphan blocks.
Validation
- The dispatcher is identified by in-degree and state-variable comparison.
- Each case's next-state constant maps to a real successor block.
- The reconstructed CFG covers all original (non-relay) blocks.
Pitfalls
- State variable computed (not a constant) — requires emulation/symbolic execution.
- Multiple dispatchers or nested flattening.
- Opaque predicates and bogus blocks inflating the case set.
References
- See
references/api-reference.mdfor the deflattener. - OLLVM and ATT&CK T1027 references (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 · 86 lines · 74 tokens per session scan A 96104a7bfe1d
defeating-control-flow-flattening 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 666 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 2026-08-30.
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