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avikchakraborty060-create/The-top-innovation

Lossless context guard for AI coding agents: outlines big files and digests noisy command output, nothing lost.

装之前:没有可直接复制的命令 · 许可证 MIT · 链接可访问

第一步:| npm install (649 packages, 23 deprecation warnings) | 41 | 1,048 | 204 | 81% |

无 SECURITY.md
在 GitHub 查看 ↗链接已于 2026-11-12 核查 · HTTP 200 · 打开的是厂商自己的官网
安装与配置
客户端配置(取自该仓库的安装命令)
{
  "mcpServers": {
    "avikchakraborty060-create-the-top-innova": {
      "command": "uv",
      "args": ["tool", "install", "skimread"]
    }
  }
}

解析自我们于 2026-10-26 抓取的该仓库 README;我们未执行过。

command/args 来自仓库写明的安装命令;不确定时请以该项目 README 为准。
VS Code(.vscode/mcp.json —— 键名是 servers)
{
  "servers": {
    "avikchakraborty060-create-the-top-innova": {
      "command": "uv",
      "args": ["tool", "install", "skimread"]
    }
  }
}
VS Code 顶层键是 <code>servers</code>,其余与上面那份相同。
Claude Code(命令行)
claude mcp add --transport stdio avikchakraborty060-create-the-top-innova -- uv tool install skimread
Claude Code 不收 JSON 文件,用这条命令直接加。
配置项的键名以你的客户端为准:客户端与 CLI
★ 数取值
0 ★ · avikchakraborty060-create/The-top-innovation · 取自 GitHub 搜索 API · (2026-11-13 20:00:50 核对)
来源明细
收录自 punkpeye/awesome-mcp-servers 榜单(我们从中收集的条目之一) · 条目类型:MCP 服务
收录与活跃
收录于 2026-10-23 · 最近见到 2026-11-14 · 共见到 26 次 · 被 23 个来源列出
评分构成
综合分 2,300 · 23 个来源 · mcp
安装
uv tool install skimread
常见问题(答案都来自我们的记录)
怎么安装?
该仓库给出的安装命令是 uv tool install skimread。我们的记录里它是仓库自己提供的;不保证在你的客户端上直接可用。
它适合谁 / 用来做什么?
我们把它归在这些标签下: MCP 服务。标签来自条目自身,不是我们强加的分类。
谁维护它、还在更新吗?
仓库 avikchakraborty060-create/The-top-innovation;我们收录于 2026-10-23;最近见到 2026-11-14。这里没有"官方认证"或"质量评级"——我们只记录来源与时间。

# skimread — skim before you read

以下摘录自该仓库的 README(片段) · 在 GitHub 读完整 README →

A lossless context guard for AI coding agents. Zero dependencies, runs entirely on your machine, MIT licensed. Works with Claude Code out of the box, and with any other agent (Codex, Cursor, Aider, Copilot, your own) through a CLI and an MCP server.
## What is it?
When you use an AI coding agent, you pay for every token it reads. Most of that money goes to two things the agent does not actually need:
1. Reading big files whole. The agent wants one function and reads all 2,000 lines of the file to find it. Next session it does it again.
2. Reading noisy command output. pytest -v prints 1,500 PASSED lines to tell you about 2 failures. npm install prints 40 deprecation warnings. cargo build lists every crate it compiled.
skimread sits between the agent and those two sources and hands the agent a skim first:
For files: an outline instead of the file. When the agent is about to read a large file, skimread gives it a map of the file (functions, classes, headings, keys, each with its line range) and says "read only the slice you need". The agent then reads 60 lines instead of 2,000.
For commands: a digest instead of the dump. Noisy commands run through skimread. The agent sees the errors, the failing tests, the stack frames in your code, and the summary line. The 1,500 PASSED lines are folded into one line that says so.
The key word is lossless. Nothing is thrown away. Every command's full output is kept on your disk, and every digest ends with a handle (skimread show 42, skimread show 42 120-180, skimread show 42 --grep TypeError). Every outline tells the agent exactly which line range to read next. The agent is never stuck and never has to re-run a build to see what it missed.
### Why not one of the existing output compressors?
Tools that compress shell output already exist (RTK, token-saver, condense). They share two limits. They cannot touch the agent's own file-reading tool, which is the bigger token sink. And they are lossy: when the compressor drops the one line the agent needed, the agent re-runs the command, and an independent benchmark found that this can cost more than not compressing at all. skimread guards file reads too, and keeps everything retrievable.
### What it is not
Not a cloud service. No network calls, no accounts, no telemetry. ~/.skimread is the whole state.
Not an LLM. Everything is deterministic parsing and folding, so it runs in milliseconds and costs nothing.
Not a summariser that guesses. It never rewrites a line; it only hides lines it can name and count, and tells you how to get them back.
## Numbers
Measured on real tool output (fixtures in tests/fixtures, reproduce with python bench/bench.py). Token counts are estimates (chars / 3.6).
| Command output | lines | tokens in | tokens out | saved |
| pytest -v (1,504 tests, 2 failing) | 1546 | 33,876 | 473 | 99% |
| vitest --reporter=verbose (579 tests, 3 failing) | 653 | 7,751 | 698 | 91% |
| pytest (65 tests, 3 failing, 60 warnings) | 294 | 3,451 | 496 | 86% |
| npm install (649 packages, 23 deprecation warnings) | 41 | 1,048 | 204 | 81% |
| cargo build (37 crates, 3 warnings) | 61 | 559 | 254 | 55% |
| go test -v (1 fail, 1 panic) | 35 | 301 | 137 | 54% |
收录徽章(可选)
想把收录标记放进 README 就复制这行,可选,不影响收录。
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来源
awesome · https://github.com/avikchakraborty060-create/The-top-innovation
按领域浏览
MCP 服务
收录来源
23 个独立来源
收录于
今天
安全策略
我们没在该仓库根目录找到 SECURITY.md(这不代表不安全)

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