Environment Setup Code Generation Prompt

This prompt is designed for a single, high-value job: reducing the time it takes a new developer to go from reading your documentation to making their first successful API call. It instructs the model to generate a complete, self-contained bootstrap script that installs dependencies, configures the runtime, and runs a verification step. The primary users are DevRel engineers, technical writers, and documentation platform teams who own the developer onboarding experience and are measured by metrics like 'time-to-first-hello-world' (TTFHW). The core promise is a script that can be copied and pasted into a clean terminal, producing a working environment in under five minutes without the developer needing to read external setup guides or troubleshoot missing dependencies.

The script generated by this prompt must be idempotent, meaning a developer can run it multiple times safely. If dependencies are already installed, the script should skip or update them gracefully rather than failing or duplicating work. It should also be verifiable, ending with a command that confirms the environment is functional—such as running a version check, importing the library, or making a no-op API call. The prompt is most effective when you provide a specific target runtime (e.g., python 3.11+, node 20 LTS), a package manager (pip, npm, maven), and the exact SDK or framework name and version. Without these constraints, the model may generate a generic script that fails on version conflicts or assumes a different operating system.

Do not use this prompt for generating production deployment scripts, CI/CD pipeline configurations, or security-sensitive bootstrap sequences that handle secrets, certificates, or production credentials. The generated script is an onboarding aid, not a hardened infrastructure tool. It should never embed API keys, tokens, or passwords. If your onboarding flow requires authentication, the script should instruct the developer to set environment variables or use a local config file, not hardcode secrets. For production environment setup, use infrastructure-as-code tools and require human review. For this prompt, the next step after generation is to test the script in a clean virtual machine or container that matches your documented prerequisites, then iterate on any failure points before publishing.

This prompt template generates a self-contained environment setup script for a specific SDK or framework. It is designed to reduce onboarding friction by producing a script that installs dependencies, configures the runtime, and verifies that the environment is functional. The output should be idempotent—running it multiple times on a clean base image should produce the same working state without errors. Replace the square-bracket placeholders with your target SDK, operating system, package manager, and any required environment variables before pasting the prompt into your AI system.

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You are an environment setup engineer. Generate a bootstrap script for the [SDK_NAME] SDK version [SDK_VERSION] targeting [OPERATING_SYSTEM] using [PACKAGE_MANAGER].

## Requirements
- The script must be idempotent: running it multiple times on a clean [BASE_IMAGE] should succeed without errors.
- Install all dependencies declared in [DEPENDENCY_FILE_PATH].
- Configure the following environment variables with secure placeholder values: [ENV_VARIABLES_LIST].
- Include a verification step that runs [VERIFICATION_COMMAND] and exits with code 0 on success.
- Handle the case where [PACKAGE_MANAGER] is not pre-installed by detecting and installing it first.
- Output clear progress messages to stdout and errors to stderr.
- Use [SCRIPT_LANGUAGE] for the script.

## Constraints
- Do not hardcode real credentials. Use placeholder strings like [PLACEHOLDER_API_KEY].
- Do not install global packages unless explicitly listed in [GLOBAL_DEPENDENCIES].
- The script must exit with a non-zero code if any step fails.
- Avoid interactive prompts; use non-interactive flags for all package manager commands.

## Output Format
Return only the script content inside a fenced code block with the appropriate language tag. Precede the code block with a brief plain-text explanation of what the script does and any manual pre-requisites.

Adaptation notes: Replace each square-bracket placeholder with concrete values for your environment. [SDK_NAME] and [SDK_VERSION] define the target library. [BASE_IMAGE] should be a clean OS image you can test against (e.g., ubuntu:22.04). [VERIFICATION_COMMAND] must be a command that fails meaningfully when the environment is broken—for example, running a minimal SDK import and a health-check API call. [ENV_VARIABLES_LIST] should enumerate every environment variable the SDK expects, with secure placeholder defaults. If your SDK requires system-level dependencies beyond what the package manager provides, add them to [GLOBAL_DEPENDENCIES]. Before shipping this prompt into a documentation pipeline, test the generated script inside a container or VM that matches [BASE_IMAGE] and confirm the verification step passes. For high-risk production environments, route the generated script through a human review step before publishing it to external developers.

The environment setup prompt is designed to be the first step in an automated onboarding or CI pipeline. It should be called with a structured specification of the target runtime, SDK version, and required capabilities. The prompt's output is a script, not a conversational answer, so the harness must treat it as executable code. This means the integration layer should parse the model's response, extract the script block, write it to a temporary file, and execute it inside a clean, ephemeral container or virtual environment. Do not run generated scripts directly on a developer's host machine without sandboxing; always assume the script may contain errors or unexpected side effects until validated.

The implementation harness should follow a strict validate → execute → verify → report loop. First, validate the output structure: confirm the model returned a fenced code block with a recognizable shebang or package manager command. If the output is malformed, retry with a stricter [OUTPUT_SCHEMA] that demands a single code block. Second, execute the script inside a Docker container or conda environment that matches the target OS and runtime specified in the prompt's [CONTEXT]. Capture stdout, stderr, and the exit code. Third, run a verification step: execute the [VERIFICATION_COMMAND] provided in the prompt input (e.g., `python -c

import sdk; print(sdk.version)

and check that it succeeds. If any step fails

log the full trace

classify the failure (syntax error

missing dependency

version conflict

network timeout)

and decide whether to retry with an error-repair prompt or escalate for human review. For production use

implement a circuit breaker that stops after three consecutive failures to avoid burning tokens on unfixable generation loops.

Model choice matters here. Prefer models with strong code generation benchmarks and a known ability to follow strict output formatting instructions. For cost-sensitive pipelines, use a smaller, fine-tuned model for the initial generation and fall back to a larger model only when validation fails. Always log the prompt version, model ID, input parameters, generated script hash, execution exit code, and verification result. This audit trail is essential for debugging flaky setups and for proving to security teams that generated scripts are traceable. Never pass live API keys, internal tokens, or production credentials into the prompt context; use placeholder variables like [YOUR_API_KEY] and inject real secrets only inside the sandboxed execution environment via secure environment variable injection.