python-manager-discovery

Pola penemuan spesifik manajer lingkungan dan masalah yang diketahui. Gunakan saat mengerjakan atau meninjau kode penemuan lingkungan untuk conda, poetry, pipenv, pyenv,…

npx skills add https://github.com/microsoft/vscode-python-environments --skill python-manager-discovery

Environment Manager Discovery Patterns

This skill documents manager-specific discovery patterns, environment variable precedence, and known issues.

Manager Quick Reference

ManagerConfig FilesCache LocationKey Env Vars
Poetrypoetry.toml, pyproject.toml, config.tomlPlatform-specificPOETRY_VIRTUALENVS_IN_PROJECT, POETRY_CACHE_DIR
PipenvPipfile, Pipfile.lockXDG or WORKON_HOMEWORKON_HOME, XDG_DATA_HOME
Pyenv.python-version, versions/~/.pyenv/ or pyenv-winPYENV_ROOT, PYENV_VERSION
Condaenvironment.yml, conda-meta/Registries + pathsCONDA_PREFIX, CONDA_DEFAULT_ENV
venvpyvenv.cfgIn-projectNone

Poetry

Discovery Locations

Virtualenvs cache (default):

  • Windows: %LOCALAPPDATA%\pypoetry\Cache\virtualenvs
  • macOS: ~/Library/Caches/pypoetry/virtualenvs
  • Linux: ~/.cache/pypoetry/virtualenvs

In-project (when enabled):

  • .venv/ in project root

Config Precedence (highest to lowest)

  1. Local config: poetry.toml in project root
  2. Environment variables: POETRY_VIRTUALENVS_*
  3. Global config: ~/.config/pypoetry/config.toml

Known Issues

IssueDescriptionFix
{cache-dir} placeholderNot resolved in paths from configResolve placeholder before use
Wrong default pathWindows/macOS differ from LinuxUse platform-specific defaults
In-project detectionPOETRY_VIRTUALENVS_IN_PROJECT must be checkedCheck env var first, then config

Code Pattern

async function getPoetryVirtualenvsPath(): Promise<string> {
    // 1. Check environment variable first
    const envVar = process.env.POETRY_VIRTUALENVS_PATH;
    if (envVar) return envVar;

    // 2. Check local poetry.toml
    const localConfig = await readPoetryToml(projectRoot);
    if (localConfig?.virtualenvs?.path) {
        return resolvePoetryPath(localConfig.virtualenvs.path);
    }

    // 3. Use platform-specific default
    return getDefaultPoetryCache();
}

function resolvePoetryPath(configPath: string): string {
    // Handle {cache-dir} placeholder
    if (configPath.includes('{cache-dir}')) {
        const cacheDir = getDefaultPoetryCache();
        return configPath.replace('{cache-dir}', cacheDir);
    }
    return configPath;
}

Pipenv

Discovery Locations

Default:

  • Linux: ~/.local/share/virtualenvs/ (XDG_DATA_HOME)
  • macOS: ~/.local/share/virtualenvs/
  • Windows: ~\.virtualenvs\

When WORKON_HOME is set:

  • Use $WORKON_HOME/ directly

Environment Variables

VarPurpose
WORKON_HOMEOverride virtualenv location
XDG_DATA_HOMEBase for Linux default
PIPENV_VENV_IN_PROJECTCreate .venv/ in project

Known Issues

IssueDescriptionFix
Missing WORKON_HOME supportEnv var not checkedRead env var before defaults
Missing XDG_DATA_HOME supportNot used on LinuxCheck XDG spec

Code Pattern

function getPipenvVirtualenvsPath(): string {
    // Check WORKON_HOME first
    if (process.env.WORKON_HOME) {
        return process.env.WORKON_HOME;
    }

    // Check XDG_DATA_HOME on Linux
    if (process.platform === 'linux') {
        const xdgData = process.env.XDG_DATA_HOME || path.join(os.homedir(), '.local', 'share');
        return path.join(xdgData, 'virtualenvs');
    }

    // Windows/macOS defaults
    return path.join(os.homedir(), '.virtualenvs');
}

PyEnv

Discovery Locations

Unix:

  • ~/.pyenv/versions/ (default)
  • $PYENV_ROOT/versions/ (if PYENV_ROOT set)

Windows (pyenv-win):

  • %USERPROFILE%\.pyenv\pyenv-win\versions\
  • Different directory structure than Unix!

Key Differences: Unix vs Windows

AspectUnixWindows (pyenv-win)
Commandpyenvpyenv.bat
Root~/.pyenv/%USERPROFILE%\.pyenv\pyenv-win\
Shims~/.pyenv/shims/%USERPROFILE%\.pyenv\pyenv-win\shims\

Known Issues

IssueDescriptionFix
path.normalize() vs path.resolve()Windows drive letter missingUse path.resolve() on both sides
Wrong command on WindowsLooking for pyenv instead of pyenv.batCheck for .bat extension

Code Pattern

function getPyenvRoot(): string {
    if (process.env.PYENV_ROOT) {
        return process.env.PYENV_ROOT;
    }

    if (process.platform === 'win32') {
        // pyenv-win uses different structure
        return path.join(os.homedir(), '.pyenv', 'pyenv-win');
    }

    return path.join(os.homedir(), '.pyenv');
}

function getPyenvVersionsPath(): string {
    const root = getPyenvRoot();
    return path.join(root, 'versions');
}

// Use path.resolve() for comparisons!
function comparePyenvPaths(pathA: string, pathB: string): boolean {
    return path.resolve(pathA) === path.resolve(pathB);
}

Conda

Discovery Locations

Environment locations:

  • Base install envs/ directory
  • ~/.conda/envs/
  • Paths in ~/.condarc envs_dirs

Windows Registry:

  • HKCU\Software\Python\ContinuumAnalytics\
  • HKLM\SOFTWARE\Python\ContinuumAnalytics\

Shell Activation

ShellActivation Command
bash, zshsource activate envname
fishconda activate envname (NOT source!)
PowerShellconda activate envname
cmdactivate.bat envname

Known Issues

IssueDescriptionFix
Fish shell activationUses bash-style commandUse fish-compatible syntax
Registry pathsMay be stale/invalidVerify paths exist
Base vs named envsDifferent activationCheck if activating base

Code Pattern

function getCondaActivationCommand(shell: ShellType, envName: string): string {
    switch (shell) {
        case 'fish':
            // Fish uses different syntax!
            return `conda activate ${envName}`;
        case 'cmd':
            return `activate.bat ${envName}`;
        case 'powershell':
            return `conda activate ${envName}`;
        default:
            // bash, zsh
            return `source activate ${envName}`;
    }
}

venv

Discovery

Identification:

  • Look for pyvenv.cfg file in directory
  • Contains home and optionally version keys

Version Extraction Priority

  1. version field in pyvenv.cfg
  2. Parse from home path (e.g., Python311)
  3. Spawn Python executable (last resort)

Code Pattern

async function getVenvVersion(venvPath: string): Promise<string | undefined> {
    const cfgPath = path.join(venvPath, 'pyvenv.cfg');

    try {
        const content = await fs.readFile(cfgPath, 'utf-8');
        const lines = content.split('\n');

        for (const line of lines) {
            const [key, value] = line.split('=').map((s) => s.trim());
            if (key === 'version') {
                return value;
            }
        }

        // Fall back to parsing home path
        const homeLine = lines.find((l) => l.startsWith('home'));
        if (homeLine) {
            const home = homeLine.split('=')[1].trim();
            const match = home.match(/(\d+)\.(\d+)/);
            if (match) {
                return `${match[1]}.${match[2]}`;
            }
        }
    } catch {
        // Config file not found or unreadable
    }

    return undefined;
}

PET Server (Native Finder)

JSON-RPC Communication

The PET server is a Rust-based locator that communicates via JSON-RPC over stdio.

Known Issues

IssueDescriptionFix
No timeoutJSON-RPC can hang foreverAdd Promise.race with timeout
Silent spawn errorsExtension continues without envsSurface spawn errors to user
Resource leaksWorker pool not cleaned upDispose on deactivation
Type guard missingResponse types not validatedAdd runtime type checks
Cache key collisionPaths normalize to same keyUse consistent normalization

Code Pattern

async function fetchFromPET<T>(method: string, params: unknown): Promise<T> {
    const timeout = 30000; // 30 seconds

    const result = await Promise.race([
        this.client.request(method, params),
        new Promise<never>((_, reject) => setTimeout(() => reject(new Error('PET server timeout')), timeout)),
    ]);

    // Validate response type
    if (!isValidResponse<T>(result)) {
        throw new Error(`Invalid response from PET: ${JSON.stringify(result)}`);
    }

    return result;
}

Lebih banyak skill dari microsoft

oss-growth
microsoft
Persona peretas pertumbuhan OSS
agent-framework-azure-ai-py
microsoft
Bangun agen Azure AI Foundry menggunakan Microsoft Agent Framework Python SDK (agent-framework-azure-ai). Gunakan saat membuat agen persisten dengan AzureAIAgentsProvider, menggunakan alat yang dihosting (code interpreter, file search, web search), mengintegrasikan server MCP, mengelola utas percakapan, atau mengimplementasikan respons streaming. Mencakup alat fungsi, keluaran terstruktur, dan agen multi-alat.
development
airunway-aks-setup
microsoft
Siapkan AI Runway di AKS — dari klaster kosong hingga model berjalan. Mencakup verifikasi klaster, instalasi controller, penilaian GPU, penyiapan penyedia, dan deployment pertama. KAPAN: "setup AI Runway", "onboard AKS cluster", "install AI Runway", "airunway setup", "deploy model to AKS", "GPU inference on AKS", "KAITO setup on AKS", "run LLM on AKS", "vLLM on AKS", "set up model serving on AKS", "AI Runway controller".
devops
appinsights-instrumentation
microsoft
Panduan untuk instrumentasi aplikasi web dengan Azure Application Insights. Menyediakan pola telemetri, pengaturan SDK, dan referensi konfigurasi. KAPAN: cara menginstrumentasi aplikasi, SDK App Insights, pola telemetri, apa itu App Insights, panduan Application Insights, contoh instrumentasi, praktik terbaik APM.
devops
applicationinsights-web-ts
microsoft
Instrumentasi aplikasi browser/web dengan Application Insights JavaScript SDK (@microsoft/applicationinsights-web). Digunakan untuk Real User Monitoring (RUM) — tampilan halaman, klik, dependensi AJAX/fetch, pengecualian, peristiwa kustom, dan jejak agen GenAI sisi browser yang dikorelasikan dengan jejak OpenTelemetry backend. Mencakup pengaturan SDK Loader Script dan npm, ekstensi kerangka kerja (React, React Native, Angular), Click Analytics, inisialisasi telemetri, dan konvensi semantik OTel GenAI untuk span agen/alat/model yang dipancarkan dari browser.
devops
azure-ai-anomalydetector-java
microsoft
Bangun aplikasi deteksi anomali dengan Azure AI Anomaly Detector SDK untuk Java. Gunakan saat mengimplementasikan deteksi anomali univariat/multivariat, analisis deret waktu, atau pemantauan bertenaga AI.
development
azure-ai-language-conversations-py
microsoft
Implementasikan Pemahaman Bahasa Percakapan (CLU) menggunakan SDK Python azure-ai-language-conversations. Gunakan saat bekerja dengan ConversationAnalysisClient untuk menganalisis maksud dan entitas percakapan, membangun fitur NLP, atau mengintegrasikan pemahaman bahasa ke dalam aplikasi.
development
azure-ai-ml-py
microsoft
Azure Machine Learning SDK v2 untuk Python. Gunakan untuk ruang kerja ML, pekerjaan, model, kumpulan data, komputasi, dan pipeline. Pemicu: "azure-ai-ml", "MLClient", "ruang kerja", "registri model", "pekerjaan pelatihan", "kumpulan data".
development