Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
作者提供的说明(英文原文)
Critical Path Method scheduling and lock-aware parallel cohorts for MCP agents.
这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。
我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。
选择你使用的 AI 应用,然后复制设置。
你需要
你用的是哪个应用?
在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。
打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json
{
"mcpServers": {
"cpm-planner": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/praxec/cpm-planner:0.5.0"
]
}
}
}在终端中运行这条命令
claude mcp add cpm-planner -- docker run -i --rm ghcr.io/praxec/cpm-planner:0.5.0粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"cpm-planner": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/praxec/cpm-planner:0.5.0"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"cpm-planner": {
"type": "stdio",
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"ghcr.io/praxec/cpm-planner:0.5.0"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。
你的选择通过 Cookie 保存在这台设备上,无需账户。
这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。
它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。
我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月7日
注册名称已通过 GitHub 账户 praxec 验证,该账户拥有此仓库
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
它在你的电脑上运行;我们只能读取互联网端点的工具列表
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。
它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。
注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。
它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。
GitHub 仓库最后更新于–,最新版本(0.5.0)于11天前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Docker。
同一分类中有:Spec Driven Development, Remote Desktop Commander, sync. labs
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
Lipsync videos, dub them into other languages, make talking videos and open sync. Studio.
按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
cpm-planner is a Critical Path Method (CPM) planner for agent work, exposed as an MCP server. You submit a task graph and it computes the schedule: earliest and latest start and finish, slack, the critical path and the bottleneck tasks that gate completion. It then leases lock-aware cohorts of ready deliverables with disjoint file sets, so several workers can run in parallel without colliding. Plans can live in the repository as plan-as-code files with forkable, comparable variants; read-only tools lint, level against resource capacities and run Monte Carlo risk; earned-value tools track PV, EV, AC, SPI and CPI against a frozen baseline; and an optional AI review proposes verified schedule improvements. Any MCP client (Claude Code, Cursor, VS Code, Codex, a custom orchestrator, or a praxec workflow) drives it over stdio. It is a standalone tool with no dependency on praxec.
Prebuilt packages are published for Linux, macOS and Windows on x86_64 and ARM64.
The installers resolve your OS and CPU and download the matching archive. They verify its SHA-256 against the release's `checksums.sha256` before installing, then atomically replace the binary in a user-local directory. A checksum mismatch aborts with a non-zero exit and installs nothing. Re-running the installer upgrades in place.
# Linux / macOS -> ~/.local/bin/cpm-planner
curl -fsSL https://github.com/praxec/cpm-planner/releases/latest/download/install.sh | sh
irm https://github.com/praxec/cpm-planner/releases/latest/download/install.ps1 | iex
The installer prints the absolute path it installed to. Use that path when you register the server with a GUI client.
Installer details: inspecting the script, PATH, upgrades and direct downloads
If you would rather read the script before running it, download, inspect, then run (see Pin a version for ):
# Linux / macOS
curl -fsSLO https://github.com/praxec/cpm-planner/releases/latest/download/install.sh less install.sh sh install.sh --version
# Windows
irm https://github.com/praxec/cpm-planner/releases/latest/download/install.ps1 -OutFile install.ps1 Get-Content install.ps1 .\install.ps1 -Version
PATH. If the install directory is not on your PATH, the installer prints the exact line to add. Pass --add-to-path (-AddToPath on Windows) to have it do that for you, user-level only and never with sudo.
On Linux and macOS it appends the line only if it is not already there, to:
~/.zshrc for zsh;~/.bash_profile if it exists, else ~/.bashrc, for bash;~/.profile for other shells.For fish it prints fish_add_path for you to run instead of writing a file.
Downloads. The installers only accept https:// download URLs.
Upgrades. After upgrading, restart your MCP client so it launches the new binary. On Windows the old exe is renamed to cpm-planner.exe.old and removed on the next run.
Direct downloads. You can also download an archive yourself and check it against checksums.sha256; a machine-readable release-manifest.json is published too. All assets are at https://github.com/praxec/cpm-planner/releases/latest.
| Platform | Asset |
|---|---|
| Linux x86_64 | cpm-planner-x86_64-unknown-linux-gnu.tar.gz |
| Linux ARM64 | cpm-planner-aarch64-unknown-linux-gnu.tar.gz |
| macOS x86_64 | cpm-planner-x86_64-apple-darwin.tar.gz |
| macOS Apple Silicon | cpm-planner-aarch64-apple-darwin.tar.gz |
| Windows x86_64 | cpm-planner-x86_64-pc-windows-msvc.zip |
| Windows ARM64 | cpm-planner-aarch64-pc-windows-msvc.zip |
In the commands in this README, is a release tag such as v0.2.0. Pick one from the releases page.
# Linux / macOS
curl -fsSL https://github.com/praxec/cpm-planner/releases/latest/download/install.sh | sh -s -- --version
$env:PRAXEC_VERSION = ' '; irm https://github.com/praxec/cpm-planner/releases/latest/download/install.ps1 | iex
& ([scriptblock]::Create((irm https://github.com/praxec/cpm-planner/releases/latest/download/install.ps1))) -Version
With Node 18 or later, install the cpm-planner command from npm:
npm install -g @matthew-cochran/cpm
cpm-planner --version
The package is a small launcher. It downloads the release binary for your platform, verifies its checksum and caches it, during install or, where npm skips install scripts, on first run. The package version is the release version, so npm install -g @matthew-cochran/cpm@0.2.0 pins one. To run it without installing, use npx -y @matthew-cochran/cpm. For details and environment variables, see npm/README.md.
Build and install a tagged release with Cargo (Rust 1.99 or newer):
cargo install --git https://github.com/praxec/cpm-planner --tag <version> --locked
The crates.io listing lags behind the GitHub releases until the next cargo publish, so cargo install cpm-planner does not give you the current version yet. Install from the tag as above.
docker pull ghcr.io/praxec/cpm-planner
docker run -i --rm -v cpm-planner-data:/data ghcr.io/praxec/cpm-planner
-i keeps stdin open for the stdio transport.
The image sets CPM_PLANNER_DB=/data/cpm-planner.db and declares /data as a volume owned by the unprivileged app user, so a named volume persists plan state across runs. For a bind mount (-v "$PWD:/data"), run as yourself so the directory is writable: --user "$(id -u):$(id -g)". Pass -e CPM_PLANNER_DB=... only to override the path.
To register the image with a client, put docker in command and the rest in args. For example, in Claude Code:
claude mcp add cpm-planner --scope user -- docker run -i --rm -v cpm-planner-data:/data ghcr.io/praxec/cpm-planner
cpm-planner --version
It prints cpm-planner . To check the MCP side without a client, run the smoke script from the repository. It performs the MCP initialize and tools/list handshake and fails if plan.submit, plan.status or plan.get is missing:
node scripts/mcp-smoke.mjs /absolute/path/to/cpm-planner
Write a plan as .cpm-planner/plans/ / .json (a PlanGraph):
plan.lint {path: ".cpm-planner/plans/checkout/main.json"} — static checks, no state written.plan.sync {path: ".cpm-planner/plans/checkout/main.json"} — register the variant and return its plan_id.plan.status {plan_id} — schedule, critical path, ready set and locks.plan.sync tracks the file by content hash; plan.status reports definition_drift when the tracked file and the stored head graph disagree.
Freeze the baseline, record progress, then read the report (earned_pct is earned in proportion under earning_rule: "weighted"; fifty_fifty credits 50% once a deliverable is in progress or has any reported earned_pct, and the default zero_hundred earns at completion):
plan.baseline {plan_id} — freeze the CPM schedule and budgets as baseline 1.plan.mark_status {plan_id, deliverable_id, caller_id, status: {"status": "in_progress"}, earned_pct: 50, actual_effort_hours: 4} — report progress and actual cost.plan.ev {plan_id} — PV, EV, AC, SV, CV, SPI, CPI, EAC and alerts.plan.snapshot {plan_id, format: "markdown"} — append the reading and export a Markdown table.A ratio with a zero denominator is null and explained in undefined; alerts (SPI_BELOW_0_9, CPI_BELOW_0_9) compare the two latest stored readings.
Any MCP client can run cpm-planner over stdio: register the cpm-planner binary as the command, or, with Node 18 or later, npx -y @matthew-cochran/cpm. For a GUI client, use the absolute path the installer printed.
Install the agent skills (/cpm-plan and friends) with cpm-planner skills install --target --user (or --project ), where is claude, codex, cursor, copilot, gemini, all, or agents-md (project only).
docs/AGENT-INSTALL.md has the per-client steps (Claude Code, Claude Desktop, Codex, Cursor, VS Code, Gemini CLI and others), env blocks, verification and troubleshooting. llms.txt indexes the docs for LLMs.
| Tool | Does | |
|---|---|---|
plan.submit | Submit a task graph; returns a plan id (idempotent on the graph + caller). A prerequisite is a deliverable id string or an object `{id, consumes?, kind?: artifact\ | interface, lag_hours?}; both forms round-trip through plan.get. A deliverable may set milestone: true (an acceptance point, zero-length unless given an estimate or duration, reported in plan.status milestones). An optional earning_rule (zero_hundred, the default: 100% only when complete; fifty_fifty: 50% once in progress or any earned_pct is reported; weighted: the reported earned_pct) sets how earned value credits partial progress and is part of the plan's identity. A deliverable's optional duration_hours (calendar time) replaces effort as its scheduled length and a prerequisite's lag_hours delays the dependent's start; effort stays the cost basis. An optional estimate {optimistic, likely, pessimistic} (0 <= optimistic <= likely <= pessimistic) is a three-point effort estimate. Scheduled-length precedence: duration_hours > estimated_effort_hours > estimate.likely > 0 for a milestone > the estimator default; Monte Carlo samples the estimate only when neither duration_hours nor estimated_effort_hours is set. Limits: at most 5000 deliverables, and every hour value (effort, duration, lag, estimate) finite and between 0 and 1000000 (INVALID_GRAPH). With name (optional project/variant, variant defaults to main`) it registers a named variant instead of an unnamed plan. |
| plan.acquire_cohort | Atomically acquire up to N ready deliverables with mutually disjoint file sets (an owned_files entry may be {path, mode: "append"}; append claims on one path may be co-leased and appear in shared_paths; a file may be owned by several deliverables only if they are ordered by prerequisites, or all claims are append); optional ttl_seconds sets the lease TTL (clamped to the server maximum); optional ids and filter.metadata target deliverables; metadata.kind = "manual" deliverables are never leased; th
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