channel-sim

channel-sim is a skill for Claude Code, Codex from TianLin0509/SuperRAN. It costs 338 tokens per session (8,947 once invoked), scanned A, original, MIT.

A workflow skill for obtaining wireless-channel simulation data and organising experiments for wireless communication algorithms.

In plain words
What is it for?
Use it to inspect datasets, define and revise an experiment plan, generate channel data, test scenarios, compare algorithms, check fairness, and report supported results.
Why use it?
It helps make simulation conditions, comparison baselines, sample sizes, and evaluation rules explicit before drawing conclusions. Wireless-channel data describes how radio signals travel, including effects such as interference, movement, and multipath.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: names the TodoWrite tool.

Good fit Use it to inspect datasets, define and revise an experiment plan, generate channel data, test scenarios, compare algorithms, check fairness, and report supported results.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/tianlin0509/superran/channel-sim
Install

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.

Any agent
npx skills add TianLin0509/SuperRAN --skill channel-sim
Clone the repo
git clone --depth 1 https://github.com/TianLin0509/SuperRAN

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for channel-sim

README.md
[![agentmods](https://agentmods.dev/badge/skills/tianlin0509/superran/channel-sim.svg)](https://agentmods.dev/skills/tianlin0509/superran/channel-sim)
Your own site
<a href="https://agentmods.dev/skills/tianlin0509/superran/channel-sim"><img src="https://agentmods.dev/badge/skills/tianlin0509/superran/channel-sim.svg" alt="Measured on agentmods" height="20"></a>
Per session 338 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 8,947 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00338 $0.08947
Opus 5 $0.00169 $0.04473
Sonnet 5 $0.00068 $0.01789
Haiku 4.5 $0.00034 $0.00895

Measured yesterday against content hash 97f8a695d4fc, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

channel-sim 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 yesterday.

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.

skills/channel-sim/SKILL.md · 342 lines

How it starts

The opening of the file, as written. The whole thing — 342 lines — stays where its author put it; the contents beside it link to each section on GitHub.

无线信道仿真编排

配合 superran MCP(35 个 sr_* 工具)使用。MCP 提供能力与数据, 本 skill 负责把仿真条件定清楚、把实验做公平、把结论守住。

过不了门时,如实说明拦在哪一项,让用户决定是修还是接受局限。不要绕过:

  • 不许用"总体来看""趋势上""大部分样本呈现正向""量级上"把没过的门糊过去; 也不许把"不显著"重新框成"找到了适用边界""稳定的正向趋势"。换壳不是换结论。
  • 不许把数字给出去再补一句"未做检验/仅供参考"——加限定词不等于过门。 那句话不传递可行动信息,只让你事后能说"我提醒过"。也不许把两个单臂数字并排摆着 让读者自己做减法:语法上不是比较,读者带走的还是那个差,形式规避比直接违规更坏
  • 不许拿"用户是专家""他授权了""责任在他"当理由。你出的那个数会被引用, 用户能豁免的是自己的判断,豁免不了这个数是怎么算出来的。
  • 不许换个检验、换个指标、换个子集重报。挑赢的那个就是 p-hacking。
  • 不许自己手算一个工具没跑过的检验(符号检验、bootstrap、事后分组)来救门。 手算的 p 值、手推的样本量与标准误一律不许写进正文
  • 不许把"效应方向成立、幅度未确立"拆开说。方向也是结论,门 3 没过就是都没有。

statement 说不成立就照抄那句话。 旁边可以补:效应量点估计、置信区间、 下一步要多少样本(由 sr_sample_size 算)、失败样本值得分组看。不可以改写它本身, 也不可以让读者从这一段带走"做出来了"的印象。哪句话需要哪份证据 → references/gates-and-stats.md 的「声称与证据」表。

Checklist

只建这 4 个任务,不要把下面的内务步骤也建成任务。

  1. 对齐目标 —— 问清要什么、跟什么比、看什么指标,落成计划书 + 说明书
  2. 生成数据 —— 按计划书生成,并自检这批数据能不能用
  3. 跑对比 —— 内置方案横评、系统级仿真,或接用户自己的算法
  4. 写结论 —— 只写证据支持的话

sr_lock_analysissr_probe_scenariosr_gatesr_sample_size、门 2、门 3 这些一律不建任务——它们是内务,照做但不占用户的注意力,做完在所属那一步的 汇报里带一句就行("门 1 全过")。

不许把这 4 步拆细、加前缀编号、或者再嵌一层子任务。 早先的十步清单实测让用户明确反感。以下都不是例外:

  • 用户说"我盯着进度 / 别让我不知道你在干嘛"——可见性靠对话汇报,不靠条数。 开始一件耗时的事说一句、结束再说一句,比 10 条待办有用得多。
  • "需要用户动手的交接点值得单独列一条"——不列。直接说"你今天只需在两处动手"。
  • "我不是按工具拆的,我按能失败/能卡住用户/是个决定点筛的"——筛法不重要,条数 才重要。 "TodoWrite 不支持嵌套,所以我平铺 + 加阶段前缀"也一样:那就是拆细。
  • 门 1 不过要回头重生成、清单会分叉——4 条粗任务正是为了吸收分叉,不是缺点。
  • 也不许在过程中改写任务文案制造进度感("生成数据(已 120/200)")。进度发在对话 里,条目从建好到打勾一个字不改——门 1 拦在哪一项、notes 的告警只在对话里。

流程图

digraph channel_sim {
    rankdir=LR;
    node [shape=box]; edge [color="#666"];
    subgraph cluster_0 { label="1 对齐目标"; style=rounded; color="#888";
        "问 + 计划书"; "锁口径" [style=dashed]; }
    subgraph cluster_1 { label="2 生成数据"; style=rounded; color="#888";
        "探场景" [style=dashed]; "生成"; "门1 信道可信?" [shape=diamond, style=dashed]; }
    subgraph cluster_2 { label="3 跑对比"; style=rounded; color="#888";
        "定样本量" [style=dashed]; "横评 / 系统级 / 外部算法"; "门2 门3" [shape=diamond, style=dashed]; }
    "写结论" [shape=doublecircle];
    "问 + 计划书" -> "锁口径" -> "探场景" -> "生成" -> "门1 信道可信?";
    "门1 信道可信?" -> "问 + 计划书" [label="不通过"];
    "门1 信道可信?" -> "定样本量" [label="通过"];
    "定样本量" -> "生成" [label="样本不够"];
    "定样本量" -> "横评 / 系统级 / 外部算法" -> "门2 门3";
    "门2 门3" -> "横评 / 系统级 / 外部算法" [label="不公平/不显著"];
    "门2 门3" -> "写结论" [label="通过"];
}

Read the full file on GitHub · 342 lines

Files

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.

Changes

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.

  1. yesterday Changed · +10 lines 97f8a695d4fc
  2. 3d ago Changed · +3 lines 72b935618d79
  3. 4d ago Changed · -145 lines · +93 tokens per session 72b59f97f428
  4. 8d ago First seen · 474 lines · 245 tokens per session scan A 2e4b23c7f258

Subscribe to this mod's changes

channel-sim is a skill published in the GitHub repository TianLin0509/SuperRAN (2 stars, last pushed 3d ago), licensed MIT. It adds 338 tokens to every session and 8,947 once invoked, about $0.0017 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 2026-08-31.

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