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 2233admin/reverse-skill-evolver --skill competition-websocket-runtimegit clone --depth 1 https://github.com/2233admin/reverse-skill-evolverWrote 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/2233admin/reverse-skill-evolver/competition-websocket-runtime)<a href="https://agentmods.dev/skills/2233admin/reverse-skill-evolver/competition-websocket-runtime"><img src="https://agentmods.dev/badge/skills/2233admin/reverse-skill-evolver/competition-websocket-runtime/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/2233admin/reverse-skill-evolver/competition-websocket-runtime"><img src="https://agentmods.dev/badge/skills/2233admin/reverse-skill-evolver/competition-websocket-runtime.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 | $0.00114 | $0.00606 |
| Opus 5.5 | $0.00046 | $0.00242 |
| Sonnet 5.5 | $0.00023 | $0.00121 |
| Haiku 4.5 | $0.00011 | $0.00061 |
Grade A, and why
competition-websocket-runtime 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 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.
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.
This is a copy
100% identical to competition-websocket-runtime — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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 WebSocket Runtime
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 decisive behavior is carried by realtime handshake and frame flow rather than one-shot HTTP alone.
Reply in Simplified Chinese unless the user explicitly requests English.
Quick Start
- Map the handshake first: origin, path, headers, cookies, query, auth token, and upgrade response.
- Separate connection setup, subscription messages, keepalives, server pushes, and reconnect logic.
- Record message schema, topic or channel identity, and state side effects in one chain.
- Tie frames to rendered, stored, or backend-visible effects.
- Reproduce the smallest handshake-plus-frame sequence that reaches the decisive state change.
Workflow
1. Map The Realtime Handshake
- Record the initial HTTP or SSE request, upgrade headers, cookies, tokens, query params, origin checks, and negotiated protocol.
- Note whether auth material is carried by headers, cookies, query strings, or initial application frames.
- Keep route, subscription endpoint, and session identity tied together.
2. Decode Message Flow
- Separate subscribe, unsubscribe, ack, heartbeat, server push, reconnect, and terminal frames.
- Recover message types, channel IDs, schema fields, and sequencing that matter to behavior.
- Distinguish transport keepalive from application-level business messages.
3. Reduce To The Decisive Realtime Path
- Compress the result to the smallest sequence: handshake -> auth or subscribe frame -> pushed or accepted frame -> resulting state change.
- Keep canonical frame order and any replayed minimal order side by side.
- If the hard part is generic protocol reassembly without runtime UI or app-state linkage, switch back to the tighter protocol skill.
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.
- 1mo ago First seen · 51 lines · 114 tokens per session scan A 8237cae908ea
competition-websocket-runtime is a skill published in the GitHub repository 2233admin/reverse-skill-evolver (24 stars, last pushed 5d ago), licensed MIT. It adds 114 tokens to every session and 606 once invoked, about $0.0005 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to competition-websocket-runtime, differing in 0 lines, and is treated as a copy.
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