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 AgentComputerAI/torch --skill reverse-engineergit clone --depth 1 https://github.com/AgentComputerAI/torchWrote 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/agentcomputerai/torch/reverse-engineer)<a href="https://agentmods.dev/skills/agentcomputerai/torch/reverse-engineer"><img src="https://agentmods.dev/badge/skills/agentcomputerai/torch/reverse-engineer/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/agentcomputerai/torch/reverse-engineer"><img src="https://agentmods.dev/badge/skills/agentcomputerai/torch/reverse-engineer.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 | $0.00096 | $0.07361 |
| Opus 5.5 | $0.00038 | $0.02944 |
| Sonnet 5.5 | $0.00019 | $0.01472 |
| Haiku 4.5 | $0.00010 | $0.00736 |
Grade A, and why
reverse-engineer scanned grade A with 1 finding 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 1mo 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
**When**: Phase 0 curl returns HTML with no target data. The data loads via XHR/fetch after JS runs. How it starts
The opening of the file, as written. The whole thing — 675 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Reverse Engineering
When the data isn't in the HTML, it's in an API. When the API isn't public, it's hidden in the JavaScript. When the JavaScript is obfuscated, the keys are still in the page source — because the browser needs them to decrypt, which means you can too.
This skill is an escalation ladder. Start at Level 1 and stop as soon as you have what you need.
Escalation ladder
Level 1 📡 Network capture watch DevTools, find the API call
Level 2 🔬 Response classification Shannon entropy — plaintext vs compressed vs encrypted
Level 3 🔁 API replay copy as cURL, replay with fetch, 403 bypass tricks
Level 4 🔑 Token extraction find auth tokens, CSRF, API keys in page source
Level 5 📦 JS deobfuscation unpack webpack, deobfuscate, read the source
Level 6 🔐 Payload decryption NaCl / AES-GCM / CryptoJS — extract keys, decrypt
Level 7 🔌 WebSocket interception establish WS, subscribe to channels, decode frames
Level 8 🔮 GraphQL reconstruction extract queries from JS, force PersistedQueryNotFound
Level 9 🧬 Protobuf decoding reverse-engineer .proto schema from binary blobs
Level 1 — Network capture
When: Phase 0 curl returns HTML with no target data. The data loads via XHR/fetch after JS runs.
Open the page in the real Chrome debug port, capture every API call during page load:
const page = await browser.newPage();
const apiCalls = [];
page.on("response", async (res) => {
const url = res.url();
const ct = res.headers()["content-type"] || "";
if (
ct.includes("json") || ct.includes("grpc") || ct.includes("protobuf") ||
url.includes("/api/") || url.includes("/graphql") || url.includes("/v1/") || url.includes("/v2/")
) {
try {
const body = await res.text();
apiCalls.push({ url, status: res.status(), contentType: ct, size: body.length, sample: body.slice(0, 500) });
} catch {}
}
});
await page.goto(url, { waitUntil: "networkidle2" });
for (const call of apiCalls) {
console.log(`${call.status} ${call.url} (${call.contentType}, ${call.size}B)`);
console.log(` ${call.sample.slice(0, 200)}`);
}
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.
- 1mo ago First seen · 675 lines · 96 tokens per session scan A dcfaa1daf05e
reverse-engineer is a skill published in the GitHub repository AgentComputerAI/torch (5 stars, last pushed 6mo ago), licensed MIT. It adds 96 tokens to every session and 7,361 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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