Reverse Skill is a routing package for AI coding agents that selects appropriate reverse-engineering, penetration-testing, and security-research methods and tools for a given target. It is used for tasks involving APKs, binaries, frontend JavaScript, packet captures, CTF challenges, and authorized penetration testing. Its catalogue add-ons provide the skills and instructions that guide these workflows.
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 zhaoxuya520/reverse-skill --skill competition-custom-protocol-replaygit clone --depth 1 https://github.com/zhaoxuya520/reverse-skillWrote 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/zhaoxuya520/reverse-skill/competition-custom-protocol-replay)<a href="https://agentmods.dev/skills/zhaoxuya520/reverse-skill/competition-custom-protocol-replay"><img src="https://agentmods.dev/badge/skills/zhaoxuya520/reverse-skill/competition-custom-protocol-replay/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/zhaoxuya520/reverse-skill/competition-custom-protocol-replay"><img src="https://agentmods.dev/badge/skills/zhaoxuya520/reverse-skill/competition-custom-protocol-replay.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00116 | $0.00595 |
| Opus 5 | $0.00058 | $0.00298 |
| Sonnet 5 | $0.00023 | $0.00119 |
| Haiku 4.5 | $0.00012 | $0.00060 |
Grade A, and why
competition-custom-protocol-replay 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.
Copies of this mod
4 near-identical copies found in the catalogue:
- competition-custom-protocol-replay — 100% identical, 0 lines differ
- competition-custom-protocol-replay — 100% identical, 0 lines differ
- competition-custom-protocol-replay — 100% identical, 0 lines differ
- competition-custom-protocol-replay — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 51 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Competition Custom Protocol Replay
Use this skill only as a downstream specialization after $ctf-sandbox-orchestrator is already active and has established sandbox assumptions, node ownership, and evidence priorities. If that has not happened yet, return to $ctf-sandbox-orchestrator first.
Use this skill when the hard part is not merely naming the protocol, but reproducing the exact message order and state needed for acceptance.
Reply in Simplified Chinese unless the user explicitly requests English.
Quick Start
- Identify client and server roles, session boundaries, and reset conditions before decoding field semantics.
- Recover framing, lengths, delimiters, sequence numbers, checksums, nonces, and state transitions before broad replay attempts.
- Keep one canonical transcript of a successful exchange.
- Change one field or one message at a time while replaying.
- Reproduce the smallest accepted conversation that proves the decisive branch.
Workflow
1. Map The Session State Machine
- Identify handshake, negotiation, authentication, keepalive, command, and teardown phases.
- Record which fields are static, which are derived, and which depend on prior messages.
- Keep message order, direction, and timing tied to the same session identity.
2. Recover Framing And Integrity
- Reconstruct lengths, delimiters, type bytes, checksums, MACs, counters, compression, or encryption boundaries.
- Distinguish transport framing from application-level framing.
- Note exactly where server acceptance changes when one field or step is mutated.
3. Build The Minimal Replay Harness
- Reduce the path to the smallest transcript that reaches the accepted state, parser branch, command effect, or artifact.
- Preserve both the original captured sequence and the replayed minimal sequence.
- If the problem is mainly generic PCAP or stream decoding with no stateful replay requirement, switch back to the broader PCAP skill.
Read This Reference
- Load
references/custom-protocol-replay.mdfor the state-machine checklist, transcript checklist, and evidence packaging.
What ships with it
2 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 · 51 lines · 116 tokens per session scan A b4ffc7e78122
competition-custom-protocol-replay is a skill published in the GitHub repository zhaoxuya520/reverse-skill (35,402 stars, last pushed 7d ago), licensed MIT. It adds 116 tokens to every session and 595 once invoked, about $0.0006 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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