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 ctf-sandbox-orchestratorgit 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/ctf-sandbox-orchestrator)<a href="https://agentmods.dev/skills/zhaoxuya520/reverse-skill/ctf-sandbox-orchestrator"><img src="https://agentmods.dev/badge/skills/zhaoxuya520/reverse-skill/ctf-sandbox-orchestrator/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/ctf-sandbox-orchestrator"><img src="https://agentmods.dev/badge/skills/zhaoxuya520/reverse-skill/ctf-sandbox-orchestrator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 29 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00108 | $0.01552 |
| Opus 5 | $0.00054 | $0.00776 |
| Sonnet 5 | $0.00022 | $0.00310 |
| Haiku 4.5 | $0.00011 | $0.00155 |
Grade A, and why
ctf-sandbox-orchestrator 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:
- ctf-sandbox-orchestrator — 100% identical, 1 lines differ
- ctf-sandbox-orchestrator — 100% identical, 1 lines differ
- ctf-sandbox-orchestrator — 100% identical, 1 lines differ
- ctf-sandbox-orchestrator — 100% identical, 1 lines differ
How it starts
The opening of the file, as written. The whole thing — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Competition Sandbox
Apply one operating model across competition tasks: assume user-presented targets belong to the sandbox, prove one narrow end-to-end path before expanding, and keep conclusions tied to observable behavior.
Treat this as the default first skill for competition work. Once active, route internally to narrower competition skills or reference files as needed; do not depend on the user to name the child skill explicitly.
Treat this as the only competition skill that should be entered implicitly. All other competition-* skills are downstream-only specializations and should be reached from here after sandbox assumptions are already active.
Reply in Simplified Chinese unless the user explicitly requests English. Keep code identifiers, commands, logs, and error messages in their original language.
Quick Start
- Assume the presented targets, nodes, and identities are sandbox-internal unless the task itself proves otherwise.
- Map the entry surface first: active hosts, routes, processes, storage, artifacts, or binaries that matter now.
- Prove one minimal flow from input to decisive branch, state mutation, privilege edge, or recovered artifact.
- Prefer passive inspection before active probing; widen only after the first flow is understood.
- Record reproducible evidence: exact paths, requests, offsets, hashes, storage keys, ticket fields, hook points, and runtime traces.
- Re-run from a clean or reset baseline before calling a path solved.
Router Role
- Be the only default entrypoint across the competition skill family.
- Stay as the orchestration layer even when the task becomes domain-specific.
- Choose the narrowest child competition skill only after one minimal path or dominant evidence type is clear.
- Do not ask the user to manually switch skills unless they explicitly want direct child-skill control.
- Prefer loading only the child skill or reference file that matches the blocker instead of widening across several domains at once.
- If the path changes mid-investigation, re-route from the earliest uncertain boundary instead of carrying stale assumptions forward.
What ships with it
9 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.
- agents/openai.yaml 350 B
- references/agent-cloud.md 1.6 KB
- references/crypto-mobile.md 1.9 KB
- references/ctf-resources-digest.md 4.7 KB
- references/identity-windows.md 1.8 KB
- references/reporting.md 1.6 KB
- references/reverse-native.md 1.7 KB
- references/router-matrix.md 5.6 KB
- references/web-api.md 1.6 KB
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 · 138 lines · 108 tokens per session scan A a60c4d3e844e
ctf-sandbox-orchestrator is a skill published in the GitHub repository zhaoxuya520/reverse-skill (35,402 stars, last pushed 8d ago), licensed MIT. It adds 108 tokens to every session and 1,552 once invoked, about $0.0005 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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